The present inventions relate to methods and systems for non-invasive measurements in the human body, and in particular, methods and systems related to detecting a mental state of a human and providing biofeedback of that mental state.
It is generally known that awareness of one's subconscious mental state, such as anxiety, focus, attention, creativity, positive or negative reflections/attitude on experiences or the use of objects, the employment of certain critical cognitive brain areas, etc., may lead to better emotional mood regulation and more objective decision-making. However, the conscious mind typically has peripheral or no awareness of subconscious mental states. Thus, if a person has a negative or unhealthy mental state (e.g., anxiety) within the context of a life or work experience, such person may not be aware of such mental state, and therefore, will be unable to take corrective actions (e.g., modifying or creating a new life or work experience) in order to alleviate or change this mental state.
There remains a need to make a person consciously aware of his or her subconscious mental state in a normal life and work environment, so that such human may better regulate his or her emotions or make more objective decisions.
In accordance with a first aspect of the present inventions, a mental state awareness system comprises a non-invasive brain interface assembly (e.g., an optical measurement assembly, magnetic measurement assembly, etc.) configured for detecting brain activity from a brain of a user. The non-invasive brain interface assembly may comprise, e.g., at least one detector configured for detecting energy (e.g., optical energy or magnetic energy) from the brain of the user, and processing circuitry configured for identifying the brain activity in response to detecting the energy from the brain of the user. In one embodiment, the non-invasive brain interface assembly comprises a head-worn unit carrying the energy source(s), and an auxiliary non-head-worn unit carrying the processing circuitry.
The mental state awareness system further comprises a processor configured for determining a mental state of a user (e.g., anxiety, focus, attention, creativity, positive or negative reflections/attitude on experiences or the use of objects, and the employment of certain critical cognitive brain areas) based on the detected brain activity.
The mental state awareness system further comprises a biofeedback device configured for automatically providing biofeedback to the user indicative of the determined mental state of the user. In one embodiment, the biofeedback device is configured for providing/directing vibrational signals (e.g., encoded with one or more messages) to the user indicative of the determined mental state of the user through peripheral somatosensation. In another embodiment, the biofeedback device is configured for providing/directing audio or visual signals to the user indicative of the determined mental state of the user.
The mental state awareness system optionally comprises a peripheral device configured for providing additional life/work context to the user. In one embodiment, the peripheral device is configured for being programmed with one of a plurality of user experiences corresponding to the additional life/work context. In another embodiment, the peripheral device is configured for automatically providing the additional life/work context to the user in response to the determined mental state of the user, such that the mental state of the user is modified. The mental state awareness system optionally comprises a database, sever, or cloud structure configured for tracking a brain activity history of the user. In this case, the processor may be configured for determining the mental state of the user further based on the tracked brain activity history of the user. The processor may be configured for determining the mental state of the user further based on tracked brain activity of a pool of users and/or tracking life/work context of the user, and acquiring meta data in depth assessment of awareness and behavior modulation patterns of the user.
In accordance with a second aspect of the present inventions, a method of making user aware of a mental state comprises detecting (e.g., optically detected, magnetically detected, etc.) brain activity from a brain of a user using a non-invasive brain interface. One method further comprises detecting energy from the brain of the user, and identifying the brain activity in response to detecting the energy from the brain of the user.
The method further comprises determining a mental state of a user (one of anxiety, focus, attention, creativity, positive or negative reflections/attitude on experiences or the use of objects, and the employment of certain critical cognitive brain areas) based on the detected brain activity.
The method further comprises automatically providing biofeedback to the user indicative of the determined mental state of the user. In one method, providing biofeedback to the user comprises providing/directing vibrational signals (e.g., encoded with one or more messages) to the user indicative of the determined mental state of the user through peripheral somatosensation. In another method, providing biofeedback to the user comprises providing/directing audio or visual signals to the user indicative of the determined mental state of the user.
The method optionally comprises providing additional life/work context to the user via a peripheral device. The additional life/work context may be provided to the user in response to the determined mental state of the user, such that the mental state of the user is modified. The method may further comprise programming the peripheral device with one of a plurality of user experiences corresponding to the additional life/work context.
The method optionally comprises tracking a brain activity history of the user. In this case, the mental state of the user is may be determined further based on the tracked brain activity history of the user.
Other and further aspects and features of the invention will be evident from reading the following detailed description of the preferred embodiments, which are intended to illustrate, not limit, the invention.
The drawings illustrate the design and utility of embodiments of the present invention, in which similar elements are referred to by common reference numerals. In order to better appreciate how the above-recited and other advantages and objects of the present inventions are obtained, a more particular description of the present inventions briefly described above will be rendered by reference to specific embodiments thereof, which are illustrated in the accompanying drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:
Referring now to
The mental state awareness system 10 comprises a non-invasive brain interface assembly 14 configured for detecting brain activity of a user 12. As will be discussed in further detail below, the brain interface assembly 14 can be optically-based, magnetically-based, or based on any other modality that enables it to non-invasively detect brain activity of the user 12 (i.e., through the intact skin and skull of the user 12), through the use of sensitive electronics, as will be described below, and is designed to be worn by the user 12. As will also be discussed in further detail below, the non-invasive brain interface assembly 14 is portable in that it can be worn by the user 12. The brain interface assembly 14 is also configured for determining a mental state (such as, e.g., anxiety, focus, attention, creativity, positive or negative reflections/attitude on experiences or the use of objects, the employment of certain critical cognitive brain areas, etc.) of the user 12 based on the detected brain activity, although this function can be performed by other processing components in the mental state awareness system 10, as described in further detail below.
The mental state of the user 12 may be determined based on the detected brain activity in any one of a variety of manners. In one embodiment, a univariate approach in determining the mental state of the user 12 may be performed, i.e., the brain activity can be detected in a plurality (e.g., thousands) of separable cortical modules of the user 12, and the brain activity obtained from each cortical module can be analyzed separately and independently. In another embodiment, a multivariate approach in determining the mental state of the user 12 may be performed, i.e., the brain activity can be detected in a plurality (e.g., thousands) of separable cortical modules of the user 12, and the full spatial pattern of the brain activity obtained from the cortical modules can be assessed together.
Any one of a variety of models can be used to classify the mental state of the user 12, and will highly depend on the characteristics of brain activity that are input onto the models. Such characteristics of brain activity may typically be extracted from the spatiotemporal brain activity that is captured, and can include, e.g., location of signal, fine grained pattern within or across locations, amplitude of signal, timing of response to behavior, magnitude of frequency bands of the signal (taking the Fourier transform of the time series), ratio of magnitude of frequency bands, cross-correlation between time series of signal between two or more locations captured simultaneously, spectral coherence between two or more locations captured simultaneously, components that maximize variance, components that maximize non-gaussian similarity, etc. The characteristics of brain activity selected to be input into the models must be considered in reference to univariate and multivariate approaches, since the univariate approach, e.g., focuses on a single location, and therefore will not take advantage of features that correlate multiple locations. The characteristics of the brain activity can be extracted from preprocessed raw data recorded during situations of patterns of thought and perception in everyday life, which are characterized by a continually changing stream of consciousness. The preprocessing of the raw data typically involves filtering the data (either in the time domain or the frequency domain) to smooth, remove noise, and separate different components of signal.
Selecting a model will be heavily dependent on whether the data is labeled or unlabeled (meaning is it known what the user is doing at the time that the brain activity is detected), as well as many other factors (e.g., is the data assumed to be normally distributed, is the data assumed relationship linear, is the data assumed relationship non-linear, etc.) Models can include, e.g., support vector machines, expectation maximization techniques, naïve-Bayesian techniques, neural networks, simple statistics (e.g., correlations), deep learning models, pattern classifiers, etc.
These models are typically initialized with some training data (meaning that a calibration routine can be performed on the user to determine what the user is doing). If no training information can be acquired, such models can be heuristically initialized based on prior knowledge, and the models can be iteratively optimized with the expectation that optimization will settle to some optimal maximum or minimum solution. Once it is known what the user is doing, the proper characteristics of the neural activity and proper models can be queried. The models may be layered or staged, so that, e.g., a first model focuses on pre-processing data (e.g., filtering), the next model focuses on clustering the pre-processed data to separate certain features that may be recognized to correlate with a known activity performed by the user, and then the next model can query a separate model to determine the mental state based on that user activity.
As will be described in further detail below, the training data or prior knowledge of the user may be obtained by providing known life/work context to the user. Altogether, the models can be used to track mental state and perception under natural or quasi-natural (i.e., in response to providing known life/work context to the user) and dynamic conditions taking in the time-course of averaged activity and determining the mental state of the user based on constant or spontaneous fluctuations in the characteristics of the brain activity extracted from the data.
A set of data models that have already been proven, for example in a laboratory setting, can be initially uploaded to the mental state awareness system 10, which system will then use the uploaded models to determine the mental state of the user. Optionally, the mental state awareness system 10 may collect data during actual use with the user, which can then be downloaded and analyzed in a separate server, for example in a laboratory setting, to create new or updated models. Software upgrades, which may include the new or updated models, can be uploaded to the mental state awareness system 10 to provide new or updated data modelling and data collection.
Further details regarding determining the mental state of a person based on detected brain activity can be found in a variety of peer-reviewed publications. See, e.g., Lee, B. T., Seok, J. H., Lee., B. C, Cho, S. W., Chai, J. H., Choi, I. G., Ham, B. J., “Neural correlates of affective processing in response to sad and angry facial stimuli in patients with major depressive disorder,” Prog Neuropsychopharmacol Biol Psychiatry, 32(3), 778-85 (2008); A. C. Felix-Ortiz, A. C., Burgos-Robles, A Bhagat, N. D., Leppla, C. A., Tye, K. M. “Bidirectional modulation of anxiety-related and social behaviors by amygdala projections to the medial prefrontal cortex,” Neuroscience 321, 197-209 (2016); Beauregard, M., Levesque, J. & Bourgouin, P., “Neural correlates of conscious self-regulation of emotion,” J. Neurosci. (2001): 21, RC165; Phan, K. L., Wager, T., Taylor, S. F. & Liberzon, I., “Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI,” Neuroimage, 16, 331-348 (2002); Canli, T. & Amin, Z., “Neuroimaging of emotion and personality: scientific evidence and ethical considerations,” Brain Cogn., 50, 414-431 (2002), McCloskey, M. S., Phan, K. L. & Coccaro, E. F., “Neuroimaging and personality disorders,” Curr. Psychiatry Rep., 7, 65-72 (2005); Pridmore, S., Chambers, A. & McArthur, M., “Neuroimaging in psychopathy,” Aust. N. Z. J. Psychiatry, 39, 856-865 (2005); Heekeren, H. R., Marrett, S., Bandettini, P. A. & Ungerleider, L. G., “A general mechanism for perceptual decision-making in the human brain,” Nature, 431, 859-862 (2004); Shin L M, Rauch S L, Pitman R K. Amygdala, Medial Prefrontal Cortex, and Hippocampal Function in PTSD, Ann N Y Acad Sci., 1071(1) (2006); Lis E, Greenfield B, Henry M, Guile J M, Dougherty G., “Neuroimaging and genetics of borderline personality disorder: a review,” J Psychiatry Neurosci., 32(3), 162-173 (2007); Etkin A, Wager T D, “Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia,” Am J Psychiatry, 164(10), 1476-1488 (2007); Etkin A. Functional Neuroimaging of Major Depressive Disorder: A Meta-Analysis and New Integration of Baseline Activation and Neural Response Data, Am J Psychiatry, 169(7), 693-703 (2012); Sheline Y I, Price J L, Yan Z, Mintun M A, “Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus, Proc Natl Acad Sci., 107(24), 11020-11025 (2010); Bari A, Robbins T W, “Inhibition and impulsivity: Behavioral and neural basis of response control,” Prog Neurobiol., 108:44-79 (2013); Kagias, Konstantinos et al. “Neuronal responses to physiological stress,” Frontiers in genetics, 3:222 (2012).
The mental state awareness system 10 further comprises an optional peripheral life/work context device 16 (e.g., a Smartphone or tablet computer) configured for incorporating known life/work context (e.g., GPS tracking, calendar scheduling, means for listening to music, means for listening to a lecture, means for learning a language, means for engaging in video conversations with others located in remote locations, etc.) to promote, adjust and/or calibrate the experience of the user 12.
For example, based on this known life/work context provided to the user 12 via the peripheral life/work context device 16, the quasi-natural conditions that are contributed to or promoting the actual mental state of the user 12 can be known or better assessed to more accurately determine this mental state.
As another example, the peripheral life/work context device 16 may provide the known life/work context to the user 12 to automatically promote, adjust, regulate, and/or calibrate the mental state of the user, e.g., anxiety, fear, alertness. For example, if the determined mental state of the user 12 is anxiety, then the peripheral life/work context device 16 may change a music selection to a more soothing melody.
The experience of the user 12 can also be individually programmed using a manual selection or manual input on the peripheral life/work context device 16 by the user 12. For example, a variety of individual experiences, such as reading, meditation, taking a nap, watching a television program, watching a live theater or musical performance, or the option for programming any other type of individual experience, can be available from the peripheral life/work context device 16 through a menu of selectable options in order to promote, adjust, regulate and/or calibrate the mental state of the user 12. Such experiences can be selected or individually programed by the user 12, and can be made available through the graphical user interface of the peripheral device 16 though a button, tab, or icon, e.g., through the use of a radio button or similar selectable options, representing one of a set of options of individual experiences.
The mental state awareness system 10 further comprises a biofeedback device 18 configured for automatically providing biofeedback to the user 12 indicative of the mental state determined by the brain interface assembly 14. In the preferred embodiment, the biofeedback device 18 is configured for providing/directing vibrational (or haptic) signals indicative of the determined mental state of the user 12 through peripheral somatosensation, e.g., to areas of the user's 12 skin, e.g., arm, wrist, hand, finger, etc., to provide the user 12 convenient awareness recognition of the determined mental state. The biofeedback device 18 may encode different messages by how the vibrations are constructed or modulated in amplitude or frequency. In one embodiment, the vibrations encode speech, e.g., conversations or speech envelopes, or encode speech at a word level, e.g., single vowel, single word, or a combination of single words and vowels. In another embodiment, the vibration modalities may be encoded to mental state type, level, urgency, or other user-relevant information.
As such, the biofeedback device 18 can serve as brain input through the peripheral nervous (PNS) or sympathetic nervous system (SNS), thereby closing the loop that connects the user's 12 subconscious mental state via brain interfaces by the brain interface assembly 14 to the user's 12 conscious awareness of such mental state. In alternative embodiments, the biofeedback device 18 may be configured for providing/directing audio or visual feedback to the user 12 that may be encoded to signal urgency, levels of mental states, or other user-relevant information, which likewise serves as brain input through the audio or visual nervous system, thereby closing the loop that connects the user's 12 subconscious mental state to the user's 12 conscious awareness of such mental state.
The mental state awareness system 10 also optionally comprises a database, server, or cloud structure 20 configured for tracking the brain activity of the user 12. For example, the database, server, or cloud structure 20 may be configured to collect raw data (e.g., brain activity data) generated by the brain interface assembly 14. Furthermore, the database, server, or cloud structure 20 (independently of or in conjunction with the mental state determination functions of the brain interface assembly 14) may be configured for performing a data analysis of the raw data in order to determine the mental state of the user 12.
For example, if the raw data obtained by the user 12 is being anonymized and stored in the database, server, or cloud structure 20, the data models can be pooled across various users, which deep learning algorithms would benefit from. The database, server, or cloud structure 20 may be configured for performing cross-correlation analysis of the signal data analysis in order to reduce the pool size of the database and focus subject averaged data to a pool that is similar to the user. Most likely, each user will have a portion of their model optimized to them, but then another portion takes advantage of patterns extracted from a larger pool of users. It should also be appreciated that each user may perform any variety of an infinite number of activities. Thus, even if a user is properly calibrated, such calibration will only be for a small set of infinite possibilities. Generalizing models may comprise various variabilities and optimizing may be difficult. However, by building a large user database on the database, server, or cloud structure 20, a data analysis pipeline connected to such database, server, or cloud structure 20 can preprocess data (clean it up), extract all different kinds of features, and then apply an appropriate data model, to overcome this issue. The brain activity of the user 12 may be tracked with additional life/work context to acquire meta data in depth assessment of awareness and behavior modulation patterns of the user 12. Although, all of the tracked data analysis has been described as being performed by the database, server, or cloud structure 20, it should be appreciated that at least a portion of the tracked data analysis functionality may be incorporated in the peripheral life/work context device 16, with the caveat that it is preferred that the tracking of the brain activity between a pool of users be performed by the database, server, or cloud structure 20.
Having described the structure, function, and application of data models of the mental state awareness system 10, one method 100 of operating the mental state awareness system 10 will now be described.
Initially, the user 12 may have a subconscious mental state (block 102). Such mental state may be, e.g., anxiety, although the user 12 may have other mental states, e.g., focus, attention, creativity, positive or negative reflections/attitude on experiences or the use of objects, the employment of certain critical cognitive brain areas, etc., as discussed above. The anxiety of the user 12 may be broken down into a specific experience (block 104), e.g., anxiety about a thing (block 104a), e.g., rent, mortgage, or credit card payment is due, anxiety about a topic (block 104b), e.g., concerned over the well-being of a parent, being interviewed, presenting or acting in front of an audience, or anxiety about fear (block 104c), e.g., fear of darkness in unfamiliar spaces, fear of aircraft travel, fear of ocean liner travel, fear of heights. The peripheral life/work context device 16 may incorporate additional life/work context into the experience of the user 12 (e.g., GPS tracking, calendar scheduling, means for listening to music, means for listening to a lecture, means for learning a language, means for engaging in video conversations with others located in remote locations, etc.) (block 106). It should be appreciated that, although the additional life/work context is illustrated as being provided to the user 12 after or during the initial experience that results in the mental state, the additional life/work context can be provided to the user 12 at any time during the method 100.
The brain interface assembly 14 detects the brain activity of the user 12 (block 108). For example, the brain interface assembly 14 may detect energy (e.g., optical energy or magnetic energy) from the brain and through the skull of the user 12, and determine the brain activity in response to detecting the energy from the brain of the user 12. The brain interface assembly 14 (or alternatively, the database, server, or cloud structure 20) then determines the mental state of a user 12 (in this case, anxiety) based on the detected brain activity (block 110).
The biofeedback device 16 then provides biofeedback to the user 12 indicative of the determined mental state of the user 12 caused by any one of the experiences (block 112). For example, the biofeedback device 16 may provide/direct vibrational signals to the user 12 indicative of the determined mental state of the user 12 through peripheral somatosensation, e.g., vibrational signals encoded with one or more messages, or alternatively, may provide/direct audio or visual signals to the user 12 indicative of the determined mental state of the user 12. Thus, input is provided to the brain of the user 12 to make the user 12 aware of his or her mental state, thereby closing the loop on the experience 108 (block 114). As such, the user 12 may regulate, adjust, and/or calibrate his or her emotions or make more objective decisions (block 116). Furthermore, the peripheral life/work context device 16 may automatically regulate, adjust and/or calibrate the experience of the user 12 based on the determined mental state of the user 12 by, e.g., playing soothing music (block 118).
Referring to
The brain interface assembly 14a includes a wearable unit 22a configured for being applied to the user 12, and in this case, worn on the head of the user 12; and an auxiliary head-worn or non-head-worn unit 24a (e.g., worn on the neck, shoulders, chest, or arm). Alternatively, the functionality of the unit 24a may be incorporated into the head-worn unit 22a. The auxiliary non-head-worn unit 24a may be coupled to the head-worn unit 22a via a wired connection 26 (e.g., electrical wires). Alternatively, the brain interface assembly 14a may use a non-wired connection (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the respective head-worn unit 22a and the auxiliary unit 24a.
The head-worn unit 22a comprises electronic or optical components, such as, e.g., one or more optical sources, an interferometer, one or more optical detector(s) (not shown), etc., an output port 28a for emitting sample light 30 generated by the brain interface assembly 14a into the head of the user 12, an input port 28b configured for receiving neural-encoded signal light 32 from the head of the user 12, which signal light is then detected, modulated and/or processed to determine neural activity within the brain of the user 12, and a support housing structure 34 containing the electronic or optical components, and ports 28a, 28b.
The support housing structure 34 may be shaped, e.g., have a banana, headband, cap, helmet, beanie, other hat shape, or other shape adjustable and conformable to the user's head, such that the ports 28a, 28b are in close contact with the outer skin of the head, and in this case, the scalp of the user 12. The support housing structure 34 may be made out of any suitable cloth, soft polymer, plastic, hard shell, and/or any other suitable material as may serve a particular implementation. In an alternative embodiment, optical fibers (not shown) may be respectively extended from the ports 28a, 28b, thereby freeing up the requirement that the ports 28a, 28b be disposed in close proximity to the surface of the head. In any event, an index matching fluid may be used to reduce reflection of the light generated by the head-worn unit 22a from the outer skin of the scalp. An adhesive, strap, or belt (not shown) can be used to secure the support housing structure 34 to the head of the user 12.
The auxiliary unit 24a comprises a housing 36 containing a controller 38 and a processor 40. The controller 38 is configured for controlling the operational functions of the head-worn unit 22a, whereas the processor 40 is configured for processing the neural-encoded signal light 32 acquired by the head-worn unit 22a to detect and localize the neural activity within the brain of the user 12. The auxiliary unit 24a may additionally include a power supply (which if head-worn, may take the form of a rechargeable or non-chargeable battery), a control panel with input/output functions, a display, and memory. Alternatively, power may be provided to the auxiliary unit 24a wirelessly (e.g., by induction).
The functionalities of the peripheral life/work context device 16, biofeedback device 18, and database, server, or cloud structure 20 may be the same as described above with respect to
The peripheral life/work context device 16 is coupled to the auxiliary unit 24a of the brain interface assembly 14a (and/or the biofeedback device 18) via a wireless connection 42 (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for communicating between the peripheral life/work context device 16 and the brain interface assembly 14a (and/or the biofeedback device 18). Alternatively, a wired connection between the peripheral life/work context device 16 and the brain interface assembly 14a (and/or the biofeedback device 18) may be used.
The biofeedback device 18 is coupled to the brain interface assembly 14a (and in this case, to the auxiliary unit 24a) via a wired connection 44 (e.g., electrical wires). Alternatively, a non-wired connection (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the respective the auxiliary unit 24a of the brain interface assembly 14a and the biofeedback device 18 may be used.
The database, server, or cloud structure 20 may be coupled to the auxiliary unit 24a of the brain interface assembly 14a (and/or the peripheral life/work context device 16 and biofeedback device 18) via a wireless connection 46 (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the biofeedback device 18 and the database, server or cloud structure 20. Alternatively, a wired connection between the database, server, or cloud structure 20 and the auxiliary unit 24a of the brain interface assembly 14a (and/or the peripheral life/work context device 16 and biofeedback device 18) may be used.
Referring to
The brain interface assembly 14b includes a head-worn unit 22b that is configured for being applied to the user 12, and in this case, worn on the head of the user 12; and an auxiliary non-head-worn unit 24b (e.g., worn on the neck, shoulders, chest, or arm). Alternatively, the functionality of the unit 24b may be incorporated into the head-worn unit 22b, as described below. The auxiliary non-head-worn unit 24b may be coupled to the head-worn unit 22b via a wired connection 26 (e.g., electrical wires). Alternatively, the brain interface assembly 14b may use a non-wired connection (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the respective head-worn unit 22b and the auxiliary unit 24b.
The head-worn unit 22b includes one or more light sources 48 configured for generating light pulses. The light source(s) 48 may be configured for generating one or more light pulses at one or more wavelengths that may be applied to a desired target (e.g., a target within the brain). The light source(s) 48 may be implemented by any suitable combination of components. For example, light source(s) 48 described herein may be implemented by any suitable device. For example, a light source as used herein may be, for example, a distributed feedback (DFB) laser, a super luminescent diode (SLD), a light emitting diode (LED), a diode-pumped solid-state (DPSS) laser, a laser diode (LD), a super luminescent light emitting diode (sLED), a vertical-cavity surface-emitting laser (VCSEL), a titanium sapphire laser, a micro light emitting diode (m LED), and/or any other suitable laser or light source.
The head-worn unit 22b includes a plurality of photodetector units 50, e.g., comprising single-photon avalanche diodes (SPADs) configured for detecting a single photon (i.e., a single particle of optical energy) in each of the light pulses. For example, an array of these sensitive photodetector units can record photons that reflect off of tissue within the brain in response to application of one or more of the light pulses generated by the light sources 48. Based on the time it takes for the photons to be detected by the photodetector units, neural activity and other attributes of the brain can be determined or inferred.
Photodetector units that employ the properties of a SPAD are capable of capturing individual photons with very high time-of-arrival resolution (a few tens of picoseconds). When photons are absorbed by a SPAD, their energy frees bound charge carriers (electrons and holes) that then become free-carrier pairs. In the presence of an electric field created by a reverse bias voltage applied to the diode, these free-carriers are accelerated through a region of the SPAD, referred to as the multiplication region.
As the free carriers travel through the multiplication region, they collide with other carriers bound in the atomic lattice of the semiconductor, thereby generating more free carriers through a process called impact ionization. These new free-carriers also become accelerated by the applied electric field and generate yet more free-carriers. This avalanche event can be detected and used to determine an arrival time of the photon. In order to enable detection of a single photon, a SPAD is biased with a reverse bias voltage having a magnitude greater than the magnitude of its breakdown voltage, which is the bias level above which free-carrier generation can become self-sustaining and result in a runaway avalanche. This biasing of the SPAD is referred to as arming the device. When the SPAD is armed, a single free carrier pair created by the absorption of a single photon can create a runaway avalanche resulting in an easily detectable macroscopic current.
It will be recognized that in some alternative embodiments, the head-worn unit 22b may include a single light source 48 and/or single photodetector unit 50. For example, brain interface system 14b may be used for controlling a single optical path and for transforming photodetector pixel measurements into an intensity value that represents an optical property of a brain tissue region. In some alternative embodiments, the head-worn unit 22b does not include individual light sources. Instead, a light source configured to generate the light that is detected by the photodetector may be included elsewhere in the brain interface system 14b. For example, a light source may be included in the auxiliary unit 24b.
The head-worn unit 22b further comprises a support housing structure 52 containing the light source(s) 48, photodetector units 50, and other electronic or optical components. As will be described in further detail below, the support housing structure 52 may be shaped, e.g., have a banana, headband, cap, helmet, beanie, other hat shape, or other shape adjustable and conformable to the user's head, such that the photodetector units 50 are in close contact with the outer skin of the head, and in this case, the scalp of the user 12. The support housing structure 52 may be made out of any suitable cloth, soft polymer, plastic, hard shell, and/or any other suitable material as may serve a particular implementation.
The auxiliary unit 24b comprises the housing 36 containing the controller 38 and the processor 40. The controller 38 is configured for controlling the operational functions of the head-worn unit 22b, whereas the processor 40 is configured for processing the photons acquired by the head-worn unit 22b to detect and localize the neural activity within the brain of the user 12. The auxiliary unit 24b may additionally include a power supply (which if head-worn, may take the form of a rechargeable or non-chargeable battery), a control panel with input/output functions, a display, and memory. Alternatively, power may be provided to the auxiliary unit 24b wirelessly (e.g., by induction).
The functionalities of the peripheral life/work context device 16, biofeedback device 18, and database, server, or cloud structure 20 may be the same as described above with respect to
The peripheral life/work context device 16 is coupled to the auxiliary unit 24b of the brain interface assembly 14b (and/or the biofeedback device 18) via a wireless connection 42 (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for communicating between the peripheral life/work context device 16 and the brain interface assembly 14b (and/or the biofeedback device 18). Alternatively, a wired connection between the peripheral life/work context device 16 and the brain interface assembly 14c (and/or the biofeedback device 18) may be used.
The biofeedback device 18 is coupled to the brain interface assembly 14b (and in this case, to the auxiliary unit 24b) via a wired connection 44 (e.g., electrical wires). Alternatively, a non-wired connection (e.g., wireless radio frequency (RF) signals (e.g.,
Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the respective the auxiliary unit 24b of the brain interface assembly 14c and the biofeedback device 18 may be used.
The database, server, or cloud structure 20 may be coupled to the auxiliary unit 24b of the brain interface assembly 14b (and/or the peripheral life/work context device 16 and biofeedback device 18) via a wireless connection 46 (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the biofeedback device 18 and the database, server or cloud structure 20. Alternatively, a wired connection between the database, server, or cloud structure 20 and the auxiliary unit 24b of the brain interface assembly 14b (and/or the peripheral life/work context device 16 and biofeedback device 18) may be used.
Referring now to
As shown in
As shown in
As shown in
As shown in
In any of the embodiments illustrated in
Each photodetector unit 50 may be self-contained. In other words, each photodetector unit 50 may be housed within its own casing. Each photodetector unit 50 may include an individual light source configured to generate light and a plurality of photodetectors configured to detect photons of the light after the photons reflect from a target within a brain of the user 12. In some examples, each photodetector unit 50 may include a printed circuit board on which the light source and the photodetectors are disposed. In some alternative embodiments, each photodetector unit 50 does not include individual light sources. Instead, a light source configured to generate the light that is detected by the photodetector units 50 may be included elsewhere. For example, a light source may be included in the master control unit 54 and coupled to the photodetector units 50 through electrical connections.
The master control unit 54 is communicatively coupled to each of photodetector units 50 by way of a plurality of wires. In some examples, the wires are at least partially tunneled from the photodetector units 50 to the master control unit 54 within a material of the support housing structure 52. In some examples, each photodetector unit 50 includes a plug interface configured to connect to one or more of the wires. The master control unit 54 may be configured to control the photodetector units 50. For example, the master control unit 54 may direct the light source of each photodetector unit 50 to generate the light in the photodetectors of each photodetector unit 50 to detect the photons of the light. As shown, the master control unit 54 is located within the support housing structure 52. In alternative embodiments, the master control unit 54 may be configured to be worn off the head of user 12. In some examples, the master control unit 54 may be selectively removed from the support housing structure 52.
Referring to
The brain interface assembly 14c includes a magnetoencephalography (MEG) head-worn unit 22c that is configured for being applied to the user 12, and in this case, worn on the head of the user 12; and an auxiliary non-head-worn unit 24c (e.g., worn on the neck, shoulders, chest, or arm). Alternatively, the functionality of the unit 24c may be incorporated into the head-worn unit 22c, as described below. The auxiliary non-head-worn unit 24c may be coupled to the head-worn unit 22c via a wired connection 26 (e.g., electrical wires). Alternatively, the brain interface assembly 14c may use a non-wired connection (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the respective head-worn unit 22c and the auxiliary unit 24c.
The head-worn unit 22c includes a plurality of optically pumped magnetometers (OPMs) 64 or other suitable magnetometers to measure biologically generated magnetic fields from the brain of the user 12 and a passive shield 66 (and/or flux concentrators). By placing the passive shield 66 over the head of the user 12, the ambient background magnetic field arising from areas outside the passive shield 66 is greatly decreased and the magnetometers 64 can measure or detect magnetic fields from activity occurring in the brain of the user 12 due to the reduction in the ambient background magnetic field.
An OPM is an optical magnetometry system used to detect a magnetic field that propagates through the human head. Optical magnetometry can include the use of optical methods to measure a magnetic field with very high accuracy—on the order of 1×10−15 Tesla. Of particular interest for their high-sensitivity, an OPM can be used in optical magnetometry to measure weak magnetic fields. (The Earth's magnetic field is typically around 50 micro Tesla). In at least some systems, the OPM has an alkali vapor gas cell that contains alkali metal atoms in a combination of gas, liquid, or solid states (depending on temperature). The gas cell may contain a quenching gas, buffer gas, or specialized anti-relaxation coatings or any combination thereof. The size of the gas cells can vary from a fraction of a millimeter up to several centimeters, allowing the practicality of OPMs to be used with wearable non-invasive brain interface devices.
The head-worn unit 22c further comprises a support housing structure 68 containing the OPMs 64, passive shield 66, and other electronic or magnetic components. As will be described in further detail below, the support housing structure 84 may be shaped, e.g., have a banana, headband, cap, helmet, beanie, other hat shape, or other shape adjustable and conformable to the user's head, such that the OPMs 64 are in close contact with the outer skin of the head, and in this case, the scalp of the user 12. The support housing structure 68 may be made out of any suitable cloth, soft polymer, plastic, hard shell, and/or any other suitable material as may serve a particular implementation.
The auxiliary unit 24c comprises the housing 36 containing the controller 38 and the processor 40. The controller 38 is configured for controlling the operational functions of the head-worn unit 22c, whereas the processor 40 is configured for processing the magnetic fields detected by the head-worn unit 22c to detect and localize the neural activity within the brain of the user 12. The auxiliary unit 24c may additionally include a power supply (which if head-worn, may take the form of a rechargeable or non-chargeable battery), a control panel with input/output functions, a display, and memory. Alternatively, power may be provided to the auxiliary unit 24c wirelessly (e.g., by induction).
The functionalities of the peripheral life/work context device 16, biofeedback device 18, and database, server, or cloud structure 20 may be the same as described above with respect to
The peripheral life/work context device 16 is coupled to the auxiliary unit 24c of the brain interface assembly 14c (and/or the biofeedback device 18) via a wireless connection 42 (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for communicating between the peripheral life/work context device 16 and the brain interface assembly 14c (and/or the biofeedback device 18). Alternatively, a wired connection between the peripheral life/work context device 16 and the brain interface assembly 14c (and/or the biofeedback device 18) may be used.
The biofeedback device 18 is coupled to the brain interface assembly 14c (and in this case, to the auxiliary unit 24c) via a wired connection 44 (e.g., electrical wires). Alternatively, a non-wired connection (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the respective the auxiliary unit 24c of the brain interface assembly 14c and the biofeedback device 18 may be used.
The database, server, or cloud structure 20 may be coupled to the auxiliary unit 24b of the brain interface assembly 14c (and/or the peripheral life/work context device 16 and biofeedback device 18) via a wireless connection 46 (e.g., wireless radio frequency (RF) signals (e.g., Bluetooth, Wifi, cellular, etc.) or optical links (e.g., fiber optic or infrared (IR)) for providing power to or communicating between the biofeedback device 18 and the database, server or cloud structure 20. Alternatively, a wired connection between the database, server, or cloud structure 20 and the auxiliary unit 24c of the brain interface assembly 14c (and/or the peripheral life/work context device 16 and biofeedback device 18) may be used.
Referring now to
As shown in
As shown in
As shown in
Although particular embodiments of the present inventions have been shown and described, it will be understood that it is not intended to limit the present inventions to the preferred embodiments, and it will be obvious to those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the present inventions. Thus, the present inventions are intended to cover alternatives, modifications, and equivalents, which may be included within the spirit and scope of the present inventions as defined by the claims.
Pursuant to 35 U.S.C. § 119(e), this application claims the benefit of U.S. Provisional Patent Application 62/784,364, filed Dec. 21, 2018, and U.S. Provisional Patent Application 62/818,786, filed Mar. 15, 2019, which are expressly incorporated herein by reference.
Number | Name | Date | Kind |
---|---|---|---|
4018534 | Thorn et al. | Apr 1977 | A |
4207892 | Binder | Jun 1980 | A |
4281645 | Jobsis | Aug 1981 | A |
4515165 | Carroll | May 1985 | A |
4655225 | Dahne et al. | Apr 1987 | A |
4963727 | Cova | Oct 1990 | A |
5090415 | Yamashita | Feb 1992 | A |
5377100 | Pope et al. | Dec 1994 | A |
5720619 | Fisslinger | Feb 1998 | A |
5853370 | Chance et al. | Dec 1998 | A |
5929982 | Anderson | Jul 1999 | A |
6163715 | Larsen et al. | Dec 2000 | A |
6240309 | Yamashita et al. | May 2001 | B1 |
6384663 | Cova et al. | May 2002 | B2 |
6488617 | Katz | Dec 2002 | B1 |
6541752 | Zappa et al. | Apr 2003 | B2 |
6683294 | Herbert et al. | Jan 2004 | B1 |
6992772 | Block | Jan 2006 | B2 |
7095491 | Forstner et al. | Aug 2006 | B2 |
7356365 | Schurman | Apr 2008 | B2 |
7507596 | Yaung et al. | Mar 2009 | B2 |
7547872 | Niclass et al. | Jun 2009 | B2 |
7613504 | Rowe | Nov 2009 | B2 |
7667400 | Goushcha | Feb 2010 | B1 |
7705284 | Inoue et al. | Apr 2010 | B2 |
7714292 | Agarwal et al. | May 2010 | B2 |
7774047 | Yamashita et al. | Aug 2010 | B2 |
7899506 | Xu et al. | Mar 2011 | B2 |
8026471 | Itzler | Sep 2011 | B2 |
8078250 | Chen et al. | Dec 2011 | B2 |
8082015 | Yodh et al. | Dec 2011 | B2 |
8115170 | Stellari et al. | Feb 2012 | B2 |
8168934 | Niclass et al. | May 2012 | B2 |
8209224 | Pradeep et al. | Jun 2012 | B2 |
8356004 | Jung et al. | Jan 2013 | B2 |
8473024 | Causevic et al. | Jun 2013 | B2 |
8609162 | Giuliano et al. | Dec 2013 | B2 |
8633431 | Kim | Jan 2014 | B2 |
8637875 | Finkelstein et al. | Jan 2014 | B2 |
8754378 | Prescher et al. | Jun 2014 | B2 |
8762202 | Pradeep et al. | Jun 2014 | B2 |
8817257 | Herve | Aug 2014 | B2 |
9012860 | Nyman et al. | Apr 2015 | B2 |
9041136 | Chia | May 2015 | B2 |
9058081 | Baxter | Jun 2015 | B2 |
9076707 | Harmon | Jul 2015 | B2 |
9101279 | Ritchey et al. | Aug 2015 | B2 |
9114140 | Giuliano et al. | Aug 2015 | B2 |
9131861 | Ince et al. | Sep 2015 | B2 |
9160949 | Zhang et al. | Oct 2015 | B2 |
9176241 | Frach | Nov 2015 | B2 |
9178100 | Webster et al. | Nov 2015 | B2 |
9190552 | Brunel et al. | Nov 2015 | B2 |
9201138 | Eisele et al. | Dec 2015 | B2 |
9209320 | Webster | Dec 2015 | B1 |
9211077 | Jung et al. | Dec 2015 | B2 |
9257523 | Schneider et al. | Feb 2016 | B2 |
9257589 | Niclass et al. | Feb 2016 | B2 |
9265974 | You et al. | Feb 2016 | B2 |
9299732 | Webster et al. | Mar 2016 | B2 |
9299873 | Mazzillo et al. | Mar 2016 | B2 |
9312401 | Webster | Apr 2016 | B2 |
9316735 | Baxter | Apr 2016 | B2 |
9331116 | Webster | May 2016 | B2 |
9339227 | Darcy et al. | May 2016 | B2 |
9368487 | Su et al. | Jun 2016 | B1 |
9401448 | Bienfang et al. | Jul 2016 | B2 |
9407796 | Dinten et al. | Aug 2016 | B2 |
9417106 | Tobita | Aug 2016 | B2 |
9419635 | Kumar et al. | Aug 2016 | B2 |
9431439 | Soga et al. | Aug 2016 | B2 |
9440064 | Wingeier et al. | Sep 2016 | B2 |
9442201 | Schmand et al. | Sep 2016 | B2 |
9449377 | Sarkar et al. | Sep 2016 | B2 |
9450007 | Motta et al. | Sep 2016 | B1 |
9466631 | Fallica et al. | Oct 2016 | B2 |
9476979 | Drader et al. | Oct 2016 | B2 |
9478579 | Dai et al. | Oct 2016 | B2 |
9495684 | Jung et al. | Nov 2016 | B2 |
9529079 | Droz | Dec 2016 | B1 |
9535157 | Caley et al. | Jan 2017 | B2 |
9574936 | Heinonen | Feb 2017 | B2 |
9625580 | Kotelnikov et al. | Apr 2017 | B2 |
9627569 | Harmon | Apr 2017 | B2 |
9639063 | Dutton et al. | May 2017 | B2 |
9640704 | Frey et al. | May 2017 | B2 |
9658158 | Renna et al. | May 2017 | B2 |
9659980 | McGarvey et al. | May 2017 | B2 |
9671284 | Dandin | Jun 2017 | B1 |
9685576 | Webster | Jun 2017 | B2 |
9702758 | Nouri | Jul 2017 | B2 |
9704205 | Akutagawa et al. | Jul 2017 | B2 |
9712736 | Kearns et al. | Jul 2017 | B2 |
9728659 | Hirigoyen et al. | Aug 2017 | B2 |
9729252 | Tyler et al. | Aug 2017 | B2 |
9736603 | Osborne et al. | Aug 2017 | B2 |
9741879 | Frey et al. | Aug 2017 | B2 |
9753351 | Eldada | Sep 2017 | B2 |
9767246 | Dolinsky et al. | Sep 2017 | B2 |
9768211 | Harmon | Sep 2017 | B2 |
9773930 | Motta et al. | Sep 2017 | B2 |
9804092 | Zeng et al. | Oct 2017 | B2 |
9812438 | Schneider et al. | Nov 2017 | B2 |
9831283 | Shepard et al. | Nov 2017 | B2 |
9851302 | Mattioli Della Rocca et al. | Dec 2017 | B2 |
9867250 | Powers et al. | Jan 2018 | B1 |
9869753 | Eldada | Jan 2018 | B2 |
9881963 | Chen et al. | Jan 2018 | B1 |
9882003 | Aharoni | Jan 2018 | B1 |
9886095 | Pothier | Feb 2018 | B2 |
9899544 | Mazzillo et al. | Feb 2018 | B1 |
9899557 | Muscara' et al. | Feb 2018 | B2 |
9939316 | Scott et al. | Apr 2018 | B2 |
9939536 | O'Neill et al. | Apr 2018 | B2 |
9943698 | Chase et al. | Apr 2018 | B2 |
9946344 | Ayaz et al. | Apr 2018 | B2 |
D817553 | Aaskov et al. | May 2018 | S |
10016137 | Yang et al. | Jul 2018 | B1 |
D825112 | Saez | Aug 2018 | S |
10056415 | Na et al. | Aug 2018 | B2 |
10091554 | Newell et al. | Oct 2018 | B1 |
10141458 | Zhang et al. | Nov 2018 | B2 |
10143414 | el Kaliouby et al. | Dec 2018 | B2 |
10157954 | Na et al. | Dec 2018 | B2 |
10158038 | Do Valle et al. | Dec 2018 | B1 |
10188860 | Wingeier et al. | Jan 2019 | B2 |
10219700 | Yang et al. | Mar 2019 | B1 |
10234942 | Connor | Mar 2019 | B2 |
10256264 | Na et al. | Apr 2019 | B2 |
10258760 | Sherpa et al. | Apr 2019 | B1 |
10340408 | Katnani | Jul 2019 | B1 |
10515993 | Field et al. | Dec 2019 | B2 |
10517521 | Kaliouby et al. | Dec 2019 | B2 |
10546233 | Bhattacharyya et al. | Jan 2020 | B1 |
10579925 | Kasabov et al. | Jan 2020 | B2 |
10558171 | Kondo | Feb 2020 | B2 |
10586454 | Toyoda et al. | Mar 2020 | B2 |
10593349 | Park | Mar 2020 | B2 |
10600179 | Mcvey | Mar 2020 | B2 |
10628741 | el Kaliouby et al. | Apr 2020 | B2 |
10636318 | Letterese et al. | Apr 2020 | B2 |
20030176806 | Pineda et al. | Sep 2003 | A1 |
20040049134 | Tosaya et al. | Mar 2004 | A1 |
20050061986 | Kardynal et al. | Mar 2005 | A1 |
20060150989 | Migaly | Jul 2006 | A1 |
20060161218 | Danilov | Jul 2006 | A1 |
20080177197 | Lee et al. | Jul 2008 | A1 |
20090012402 | Mintz | Jan 2009 | A1 |
20090083129 | Pradeep et al. | Mar 2009 | A1 |
20110208675 | Shoureshi et al. | Aug 2011 | A1 |
20120029304 | Medina et al. | Feb 2012 | A1 |
20120172743 | Aguilar et al. | Jul 2012 | A1 |
20130032713 | Barbi et al. | Feb 2013 | A1 |
20130221221 | Bouzid et al. | Aug 2013 | A1 |
20130289385 | Lozano et al. | Oct 2013 | A1 |
20130297599 | Henshall | Nov 2013 | A1 |
20130311132 | Tobita | Nov 2013 | A1 |
20130342835 | Blacksberg | Dec 2013 | A1 |
20140023999 | Greder | Jan 2014 | A1 |
20140027607 | Mordarski et al. | Jan 2014 | A1 |
20140021119 | Pacala et al. | Jul 2014 | A1 |
20140191115 | Webster et al. | Jul 2014 | A1 |
20140200432 | Banerji | Jul 2014 | A1 |
20140228701 | Chizeck et al. | Aug 2014 | A1 |
20140275891 | Muehlemann et al. | Sep 2014 | A1 |
20140291481 | Zhang et al. | Oct 2014 | A1 |
20140303450 | Caponi | Oct 2014 | A1 |
20150041625 | Dutton | Feb 2015 | A1 |
20150041627 | Webster | Feb 2015 | A1 |
20150054111 | Niclass et al. | Feb 2015 | A1 |
20150077279 | Song | Mar 2015 | A1 |
20150150505 | Kaskoun et al. | Jun 2015 | A1 |
20150192677 | Yu et al. | Jul 2015 | A1 |
20150200222 | Webster | Jul 2015 | A1 |
20150248651 | Akutagawa et al. | Sep 2015 | A1 |
20150290454 | Tyler et al. | Oct 2015 | A1 |
20150293224 | Eldada et al. | Oct 2015 | A1 |
20150297109 | Garten | Oct 2015 | A1 |
20150327777 | Kostic et al. | Nov 2015 | A1 |
20150333095 | Fallica et al. | Nov 2015 | A1 |
20150338917 | Steiner et al. | Nov 2015 | A1 |
20150355462 | Saito et al. | Dec 2015 | A1 |
20150364635 | Bodlovic et al. | Dec 2015 | A1 |
20160049765 | Eldada | Feb 2016 | A1 |
20160099371 | Webster | Apr 2016 | A1 |
20160119983 | Moore | Apr 2016 | A1 |
20160150963 | Roukes et al. | Jun 2016 | A1 |
20160161600 | Eldada et al. | Jun 2016 | A1 |
20160181302 | McGarvey et al. | Jun 2016 | A1 |
20160218236 | Dhulla et al. | Jul 2016 | A1 |
20160220163 | Yamada | Aug 2016 | A1 |
20160242690 | Principe et al. | Aug 2016 | A1 |
20160270656 | Samec et al. | Sep 2016 | A1 |
20160278715 | Yu et al. | Sep 2016 | A1 |
20160287107 | Szabados | Oct 2016 | A1 |
20160341656 | Liu et al. | Nov 2016 | A1 |
20160356718 | Yoon et al. | Dec 2016 | A1 |
20160357260 | Raynor et al. | Dec 2016 | A1 |
20170030769 | Clemens et al. | Feb 2017 | A1 |
20170042439 | Yeow | Feb 2017 | A1 |
20170047372 | McGarvey et al. | Feb 2017 | A1 |
20170052065 | Sharma et al. | Feb 2017 | A1 |
20170118423 | Zhou et al. | Apr 2017 | A1 |
20170131143 | Andreou et al. | May 2017 | A1 |
20170139041 | Drader et al. | May 2017 | A1 |
20170141100 | Tseng et al. | May 2017 | A1 |
20170176579 | Niclass et al. | Jun 2017 | A1 |
20170176596 | Shpunt et al. | Jun 2017 | A1 |
20170179173 | Mandai et al. | Jun 2017 | A1 |
20170186798 | Yang et al. | Jun 2017 | A1 |
20170188876 | Marci et al. | Jul 2017 | A1 |
20170202518 | Furman et al. | Jul 2017 | A1 |
20170229037 | Gazzaley | Aug 2017 | A1 |
20170262943 | Akutagawa et al. | Sep 2017 | A1 |
20170265822 | Du | Sep 2017 | A1 |
20170276545 | Henriksson | Sep 2017 | A1 |
20170299700 | Pacala et al. | Oct 2017 | A1 |
20170303789 | Tichauer et al. | Oct 2017 | A1 |
20170314989 | Mazzillo et al. | Nov 2017 | A1 |
20170352283 | Lau | Dec 2017 | A1 |
20170363467 | Clemens et al. | Dec 2017 | A1 |
20180003821 | Imai | Jan 2018 | A1 |
20180014741 | Chou | Jan 2018 | A1 |
20180019268 | Zhang et al. | Jan 2018 | A1 |
20180026147 | Zhang et al. | Jan 2018 | A1 |
20180027196 | Yang et al. | Jan 2018 | A1 |
20180033895 | Mazzillo et al. | Feb 2018 | A1 |
20180039053 | Kremer et al. | Feb 2018 | A1 |
20180045816 | Jarosinski et al. | Feb 2018 | A1 |
20180062345 | Bills et al. | Mar 2018 | A1 |
20180069043 | Pan et al. | Mar 2018 | A1 |
20180081061 | Mandai et al. | Mar 2018 | A1 |
20180089848 | Yang et al. | Mar 2018 | A1 |
20180090526 | Mandai et al. | Mar 2018 | A1 |
20180090536 | Mandai et al. | Mar 2018 | A1 |
20180092557 | Bickford | Apr 2018 | A1 |
20180102442 | Wang et al. | Apr 2018 | A1 |
20180103528 | Moore | Apr 2018 | A1 |
20180167606 | Cazaux et al. | Jun 2018 | A1 |
20180175230 | Droz et al. | Jun 2018 | A1 |
20180189678 | Gupta et al. | Jul 2018 | A1 |
20180217261 | Wang | Aug 2018 | A1 |
20180278984 | Aimone | Sep 2018 | A1 |
20180366342 | Inoue et al. | Dec 2018 | A1 |
20190006399 | Otake et al. | Jan 2019 | A1 |
20190021657 | Mohammadrezazadeh et al. | Jan 2019 | A1 |
20190082990 | Poltorak | Mar 2019 | A1 |
20190088697 | Furukawa et al. | Mar 2019 | A1 |
20190090526 | Alshatwi et al. | Mar 2019 | A1 |
20190113385 | Fukuchi | Apr 2019 | A1 |
20190175068 | Everdell | Jun 2019 | A1 |
20190200888 | Poltorak | Jul 2019 | A1 |
20190201691 | Poltorak | Jul 2019 | A1 |
20190224441 | Poltorak | Jul 2019 | A1 |
20190246929 | Poltorak | Aug 2019 | A1 |
20190247662 | Poltroak | Aug 2019 | A1 |
20190321583 | Poltorak | Oct 2019 | A1 |
20190355773 | Field et al. | Nov 2019 | A1 |
20190378869 | Field et al. | Dec 2019 | A1 |
Number | Date | Country |
---|---|---|
2294973 | Mar 2011 | EP |
2939706 | Nov 2015 | EP |
8804034 | Jun 1988 | WO |
WO02043564 | Jun 2002 | WO |
2008144831 | Dec 2008 | WO |
2012135068 | Oct 2012 | WO |
WO2012135068 | Oct 2012 | WO |
2013034770 | Mar 2013 | WO |
2013066959 | May 2013 | WO |
WO2014055932 | Apr 2014 | WO |
2015052523 | Apr 2015 | WO |
2016166002 | Oct 2016 | WO |
2017004663 | Jan 2017 | WO |
2017130682 | Aug 2017 | WO |
2017150146 | Sep 2017 | WO |
2017203936 | Nov 2017 | WO |
2018007829 | Jan 2018 | WO |
2018033751 | Feb 2018 | WO |
2018122560 | Jul 2018 | WO |
Entry |
---|
Lee, B.T., Seok, J.H., Lee., B.C, Cho, S.W., Chai, J.H., Choi, I.G., Ham, B.J., “Neural correlates of affective processing in response to sad and angry facial stimuli in patients with major depressive disorder,” Prog Neuropsychopharmacol Biol Psychiatry, 32(3), 778-85 (2008. |
A.C. Felix-Ortiz, A.C., Burgos-Robles, A., Bhagat, N.D., Leppla, C.A., Tye, K.M., “Bidirectional modulation of anxiety-related and social behaviors by amygdala projections to the medial prefrontal cortex,” Neuroscience 321, 197-209 (2016). |
Beauregard, M., Levesque, J. & Bourgouin, P., “Neural correlates of conscious self-regulation of emotion,” J. Neurosci. (2001): 21, RC165. |
Phan, K. L., Wager, T., Taylor, S. F. & Liberzon, I., “Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI,” Neuroimage, 16,331-348 (2002). |
Canli, T. & Amin, Z., “Neuroimaging of emotion and personality: scientific evidence and ethical considerations,” Brain Cogn., 50, 414-431 (2002). |
McCloskey, M. S., Phan, K. L. & Coccaro, E. F., “Neuroimaging and personality disorders,” Curr. Psychiatry Rep., 7, 65-72 (2005). |
Heekeren, H. R., Marrett, S., Bandettini, P. A. & Ungerleider, L. G., “A general mechanism for perceptual decision-making in the human brain,” Nature, 431, 859-862 (2004). |
Shin LM, Rauch SL, Pitman RK., “Amygdala, Medial Prefrontal Cortex, and Hippocampal Function in PTSD,” Ann N Y Acad Sci., 1071(1) (2006). |
Lis E, Greenfield B, Henry M, Guile JM, Dougherty G., “Neuroimaging and genetics of borderline personality disorder: a review,” J Psychiatry Neurosci., 32(3), 162-173 (2007). |
Etkin A, Wager TD, “Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia,” Am J Psychiatry, 164(10),1476-1488 (2007). |
Hamilton, P., Etkin A., “Functional Neuroimaging of Major Depressive Disorder: A Meta-Analysis and New Integration of Baseline Activation and Neural Response Data”, Am J Psychiatry, 169(7), 693-703 (2012). |
Sheline YI, Price JL, Yan Z, Mintun MA, “Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus,” Proc Natl Acad Sci., 107(24), 11020-11025 (2010). |
Bari A, Robbins TW, “Inhibition and impulsivity: Behavioral and neural basis of response control,” Prog Neurobiol., 108:44-79 (2013). |
Kagias, Konstantinos et al. “Neuronal responses to physiological stress,” Frontiers in genetics, 3:222 (2012). |
Clark, Ian A., et al., “First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage”, 0005-7967/ 2014 the Authors. Published by Elsevier Ltd. Behaviour Research and Therapy. This is an open access article under the CC by license (http://creativecommons.org/licenses/by/3.0/); 10 pgs. |
George, Mark S., M.D., “Changes in Mood and Hormone Levels After Rapid-Rate Transcranial Magnetic Stimulation (rTMS) of the Prefrontal Cortex”, Journal of Neuropsychiatry, vol. 8, No. 2, Spring 1996, 9 pages. |
Milad, M. R., et al., “Neuroscience of fear extinction: Implications for assessment and treatment of fear-based and anxiety related disorders”, Behaviour Research and Therapy (2014), http://dx.doi.org/10.1016/j.brat.2014.08.006, 7 pages. |
S.Z.K, Tan et al.,“Eternal sunshine of the neuromodulated mind: Altering fear memories through neuromodulation”, Experimental Neurology 314 (2019) 9-19, 11 pages. |
Zhang, Fei-Fei, et al., “Brain structure alterations in depression: Psychoradiological evidence”, CNS Neurosci T 2018, John Wiley & Sons Ltd her. 2018;24:994-1003, 10 pages. |
PCT International Search Report and Written Opinion for International Appln. No. PCT/US2019/024027, Applicant HI LLC, forms PCT/ISA/210, 220 and 237 dated Aug. 19, 2019 (13 pages). |
Stefan K. Ehrlich, et al., “A closed-loop, music-based brain-computer interface for emotion mediation,” PLoS One 14(3): e0213516. https://doi.org/10.1371/journal.pone.0213516; Mar. 18, 2019. |
Patrick Gomez, et al., “Relationships Between Musical Structure and Psychophysiological Measures of Emotion”, American Psychological Association, vol. 7, No. 2, 2007, pp. 377-387, 10 pages. |
Fernando Lopes da Silva, “EEG and MEG: Relevance to Neuroscience”, Center of Neuroscience; http://dx.doi.org/10.1016/j.neuron.2013.10.017; 17 pages. |
Elena Boto, et al., “A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers”, NeuroImage 149 (2017) 404-414; 11 pages. |
Stanislas Dehaene, et al., “Imaging unconscious semantic priming”, Nature; vol. 395; Oct. 8, 1998; 4 pages. |
John D. E. Gabrieli, et al., “The role of left prefrontal cortex in language and memory”, Proc. Natl. Acad. Sci. USA, vol. 95, pp. 906-913, Feb. 1998; 8 pages. |
Yang Jiang, et al., “Turning Up the Old Brain with New Tricks: Attention Training via Neurofeedback”, Frontiers in Aging Neuroscience; Mar. 2017; vol. 9; Article 52; 9 pages. |
Peter Lintelle, Sensory Marketing Aspects: Priming, Expectations, Crossmodal Correspondences & More; CreateSpace Independent Publishing Platform, Jul. 23, 2014, ISBN-10: 1500616400, ISBN-13: 978-1500616403; 3 pages. |
Samat Moldakarimova, et al., “Perceptual priming leads to reduction of gamma frequency oscillations”, PNAS, Mar. 23, 2010, vol. 107, No. 12; 6 pages. |
M. Teplan, “Fundamentals of EEG Measurement”, Measurement Science Review, vol. 2, Section 2, 2002; 11 pages. |
S. G. Mason, “A Brain-Controlled Switch for Asynchronous Control Applications,” IEE Transactions on Biomedical Engineering, vol. 47, No. 10, 11 pages, Oct. 2000. |
Pineda et al., “Learning to Control Brain Rhythms: Making a Brain-Computer Interface Possible,” IEEE Trans Neural Sys Rehab, 4 pages, Jul. 15, 2002. |
Pineda et al., “The functional significance of mu rhythms: Translating “seeing” and “hearing” into “doing”,” Brain Research Reviews 50, 12 pages, 2005. |
Pour, et al., “Brain-Computer Interface: Next Generation Thought Controlled Distributed Video Game Development Platform,” IEEE, 7 pages, May 8, 2008. |
Tierney, et al., “Cognitive neuroscience using wearable magnetometer arrays: Non-invasive assessment of language function,” NeuroImage, 181, 8 pages, 2018. |
Judith Amores, et al., “Promoting Relaxation Using Virtual Reality, Olfactory Interfaces and Wearable EEG,” 2018 EEE 15th International Conference on Waerable and Implantable Body Sensor Networks; Mar. 4, 2018, (4 pages). |
PCT International Search Report and Written Opinion for International Appln. No. PCT/US2020/029031, Applicant HI LLC, forms PCT/ISA/210, 220 and 237 dated Sep. 2, 2020 (18 Pages). |
PCT International Search Report and Written Opinion for International Appln. No. PCT/US2020/025971, Applicant HI LLC, forms PCT/ISA/210, 220 and 237 dated Sep. 15, 2020 (15 pages). |
International Search Report and Written Opinion received in International Application No. PCT/US20/028820, dated Aug. 26, 2020. |
International Search Report and Written Opinion received in International Application No. PCT/US20/027537, dated Sep. 7, 2020. |
International Search Report and Written Opinion received in International Application No. PCT/US20/034062, dated Aug. 26, 2020. |
Blutman, et al., “A 0.1 pJ Freeze Vernier Time-to-Digital Converter in 65nm CMOS,” 2014 International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia. |
De Heyn, et al., “A Fast Start-up 3GHz-10GHz Digitally Controlled Oscillator for UWB Impulse Radio in 90nm CMOS,” 2007 European Solid-State Circuits Conference—(ESSCIRC), Munich, Germany, pp. 484-487. |
Henderson, et al., “A 256x256 40nm/90nm CMOS 3D-Stacked 120dB-Dynamic-Range Reconfigurable Time-Resolved SPAD Imager,” 2019 IEEE International Solid-State Circuits Conference—(ISSCC), San Francisco, CA, USA, 2019, pp. 106-108. doi: 10.1109/ISSCC.2019.8662355. |
Henderson, et al., A 192 x 128 Time Correlated Spad Image Sensor in 40-nm CMOS Technology IEEE Journal of Solid-State Circuits, 2019. |
Mita, et al., “High-Speed and Compact Quenching Circuit for Single-Photon Avalanche Diodes,” IEEE Transactions on Instrumentation and Measurement, vol. 57, No. 3, Mar. 2008. Pages 543-547. |
Richardson, et al., “A 32x32 50ps Resolution 10 bit Time to Digital Converter Array in 130nm CMOS for Time Correlated Imaging,” CICC 2009 Proceedings of the IEEE 2009 Custom Integrated Circuits Conference. IEEE Society, San Jose, U.S.A., pp. 77-80, Sep. 9, 2013. https://doi.org/doi:10.1109/CICC.2009.5280890. |
International Search Report and Written Opinion received in International Application No. PCT/US2018/058580 dated Feb. 12, 2019. |
International Search Report and Written Opinion received in International Application No. PCT/US2018/062777 dated Feb. 13, 2019. |
Bellis, Stephen et al., Photon counting imaging: the DigitalAPD, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Feb. 2006, vol. 6068, pp. 111-120. |
Cambie, Dario et al., Every photon counts: understanding and optimizing photon paths in luminescent solar concentrator-based photomicroreactors (LSC-PMs), React. Chem. Eng., 2017, 2, 561-566. |
Dalla Mora, et al., Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy, IEEE Journal of Selected Topics in Quantum Electronics, vol. 16, No. 4, Jul./Aug. 2010,1023-1030. |
Dalla Mora, et al., Memory effect in silicon time-gated single-photon avalanche diodes, Journal of Applied Physics 117, 114501 (2015). |
Dalla Mora, et al., Memory effect in silicon time-gated single-photon avalanche diodes, http://dx.doi. org/10.1063/1.4915332, Journal of Applied Physics 117, 114501, 2015 ,2015 ,1-7. |
Dutton, et al., A Time-Correlated Single-Photon-Counting Sensor with 14GS/s Histogramming Time-to-Digital Converter, 2015 IEEE International Solid-State Circuits Conference ISSCC 2015 / Session 11 / Sensors and Imagers for Life Sciences / 11.5. |
Fisher, et al., A Reconfigurable Single-Photon-Counting Integrating Receiver for Optical Communications, IEEE Journal of Solid-State Circuits, Vol. 48, No. 7, July 2013, https://www.researchgate.net/publication/260626902. |
Gallivanoni, et al., Progress in Quenching Circuits for Single Photon Avalanche Diodes, IEEE Transactions on Nuclear Science, vol. 57, No. 6, Dec. 2010. |
Gnecchi, et al., a 1x16 SiPM Array for Automotive 3D Imaging LiDAR Systems. |
Harmon, Eric S. et al., Compound Semiconductor SPAD Arrays, LightSpin Technologies, http://www.lightspintech.com/publications.html. |
Lee, et al., High-Performance Back-Illuminated Three-Dimensional Stacked Single-Photon Avalanche Diode Implemented in 45-nm CMOS Technology, IEEE Journal of Selected Topics in Quantum Electronics 6, 1-9 (2018). |
Mandai, et al., a 4 X 4 X 416 digital SIPM array with 192 TDCs for multiple high-resolution timestamp acquisition, 2013 JINST 8 PO5024. |
Maruyama, et al., a 1024 x 8, 700-ps. Time-Gated SPAD Line Sensor for Planetary Surface Exploration With Laser Raman Spectroscopy and Libs, IEEE Journal of Solid-State Circuits, vol. 49, No. 1, Jan. 2014 ,2014 ,179-189. |
Mora, Alberto D. et al., Fast-Gated Single-Photon Avalanche Diode for Wide Dynamic Range Near Infrared Spectroscopy, IEEE Journal of Selected Topics in Quantum Electronics, vol. 16, No. 4, pp. 1023-1030, Jul./Aug. 2010. |
Parmesan, et al., A 256 x 256 Spad array with in-pixel Time to Amplitude Conversion for Fluorescence Lifetime Imaging Microscopy, 2015. |
Puszka, et al. Time-resolved diffuse optical tomography using fast-gated single-photon avalanche diodes, Biomedical optics express, 2013, vol. 4, No. 8, pp. 1351-1365 (Year: 2013). |
Takai, et al., Single-Photon Avalanche Diode with Enhanced Nir-Sensitivity for Automotive Lidar Systems, Sensors, 2016, 16(4): 459, pp. 1-9 (Year: 2016). |
Zhang, et al., A CMOS SPAD Imager with Collision Detection and 128 Dynamically Reallocating TDCs for SinglePhoton Counting and 3D Time-of-Flight Imaging, Sensors (Basel, Switzerland), 18(11), 4016. doi:10.3390/s18114016. |
Non-Final Office Action received in U.S. Appl. No. 16/856,524 dated Dec. 1, 2020. |
Response filed in U.S. Appl. No. 16/856,524 filed Feb. 11, 2021. |
Notice of Allowance received in U.S. Appl. No. 16/856,524 dated Feb. 26, 2021. |
Number | Date | Country | |
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20200196932 A1 | Jun 2020 | US |
Number | Date | Country | |
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62784364 | Dec 2018 | US | |
62818786 | Mar 2019 | US |