Patent Application
20220248949
The present disclosure relates to devices and methods for assessing color vision.
Accurate assessment of color vision is essential in many situations. Clinicians need it to detect and characterize the types and severities of the color vision deficits (CVDs) that affect >20% of the world population [1]; industry needs it to develop and validate color schemes for physical products and digital displays; and scientists need it to study the neural mechanisms of color vision and its variations across people.
The anomaloscope—which is based on color matching—is the current gold-standard method for assessing color vision due to its ability to identify the type and severity of CVDs [2]. With this method, the individual under study manually adjusts two light sources, one composed of a single wavelength and the other composed of two different wavelengths, until they appear to be the same color (i.e. are metamers;
There are deficiencies in available methods and devices for accurate assessment of color vision. At present, color vision is evaluated mainly by behavioral methods, all of which require the attention and active participation of the subject. Moreover, current methods for assessing color vision require extensive training of the examiner and considerable time to administer.
The present disclosure is directed to overcoming these and other deficiencies in the art.
A first aspect relates to a method for assessing color vision. The method includes identifying one or more steady-state visual evoked potentials (SSVEPs) to identify metamers.
A second aspect relates to a device for assessing color vision. The device uses metamers identified via steady-state visual evoked potentials (SSVEPs).
A third aspect relates to a method of treating color vision deficiency. The method includes measuring one or more metamers identified via steady-state visual evoked potentials (SSVEPs), and administering a treatment for color vision deficiency.
A fourth aspect relates to a method of treating color vision deficiency. The method includes measuring a response to metameric stimuli identified by the device described herein, and administering a treatment for color vision deficiency.
A fifth aspect relates to a system for identifying a response to one or more metameric stimuli. The system includes providing one or more steady-state visual evoked potentials (SSVEP), and identifying one or more metamers.
A sixth aspect relates to a method of individually modifying color vision. The method includes utilizing feedback of brain activity elicited in response to metamers and colors that are close to being metamers identified by the methods described herein.
A seventh aspect relates to a method for assessing color vision. The method includes measuring neural activity using a human-computer interface or brain-computer interface.
An eighth aspect relates to a method for assessing color vision using neural activity as a means to personalize visual displays.
A ninth aspect relates to a method for assessing light sensitive cells in the nervous system using flashing lights.
Present methods for assessing color vision require the person's active participation. Here a brain-computer interface-based method is described for assessing color vision that does not require the person's participation. This method uses steady-state visual evoked potentials to identify metamers—two light sources that have different spectral distributions but appear to the person to be the same color. It is demonstrated that minimization of the visual evoked potential elicited by two flickering light sources identifies the metamer; this approach can distinguish people with color-vision deficits from those with normal color vision; and this metamer-identification process can be automated. This new method has numerous potential clinical, scientific, and industrial applications.
Here, a new way to identify metamers is presented. It is based on color matching, but—unlike existing methods—it does not require the active participation of the person being tested. Instead, it uses a noninvasive measure of brain activity (i.e. electroencephalography [EEG]) to quantify the brain's response to flickering lights (i.e. the steady-state visual evoked potential [SSVEP]).
When a visual stimulus alternates between two light sources at a set frequency, chromaticity and/or brightness differences between the two sources elicit brain activity (i.e. an SSVEP) at the same frequency as the alternation (see [4] for review, which is hereby incorporated by reference in its entirety; see also
It was hypothesized that a stimulus alternating between metameric light sources will elicit little or no SSVEP. If this is correct, metamers should be identifiable as the pair of alternating light sources that minimize the SSVEP.
To test this hypothesis, a stimulator was designed that can produce metameric stimuli. It comprises three independently-controlled LEDs with wavelengths of 525 nm (green), 590 nm (amber), and 625 nm (red), respectively. These three LEDs can generate a monochromatic (amber) light source and a dichromatic (red and green) light source (
It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein and may be used to achieve the benefits and advantages described herein.
A first aspect relates to a method for assessing color vision. The method includes identifying one or more steady-state visual evoked potentials (SSVEPs) to identify metamers.
It is to be appreciated that certain aspects, modes, embodiments, variations, and features of the present disclosure are described below in various levels of detail in order to provide a substantial understanding of the present technology. The definitions of certain terms as used in this specification are provided below. Unless defined otherwise, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
As used herein, the term “about” means that the numerical value is approximate and small variations would not significantly affect the practice of the disclosed embodiments. Where a numerical limitation is used, unless indicated otherwise by the context, “about” means the numerical value can vary by ±1 or ±10%, or any point therein, and remain within the scope of the disclosed embodiments.
Where a range of values is described, it should be understood that intervening values, unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in other stated ranges, may be used in the embodiments described herein.
As used herein, the terms “subject”, “individual”, or “patient,” are used interchangeably, and mean any animal, including mammals, such as mice, rats, other rodents, rabbits, dogs, cats, swine, cattle, sheep, horses, or primates, such as humans.
It is further appreciated that certain features described herein, which are, for clarity, described in the context of separate embodiments, can also be provided in combination in a single embodiment. Conversely, various features which are, for brevity, described in the context of a single embodiment, can also be provided separately or in any suitable sub-combination.
Steady-state visual evoked potentials (SSVEPs) as described herein include electroencephalogram (EEG) measures of the signals from cortical brain regions that respond in synchrony with a flickering visual stimulus, the signals represent brain responses that have reached a steady-state relationship with visual stimulus. The present disclosure includes methods, systems, and devices that acquire, process, and/or utilize Steady-state visual evoked potentials (SSVEPs) for monitoring, tracking, and/or diagnosing various paradigms in color vision efficiency and/or deficiency, particularly for SSVEPs generated by optical stimuli.
Metamers as described herein include two light sources that have different spectral distributions but appear to a subject (e.g., a person) to be the same color. When four or more non-coincident color primaries are used in a display, commonly called a “multiprimary” display in the art, there are often multiple combinations of values for the primaries that may give the same color value. That is to say, for a given hue, saturation, and brightness, there may be more than one set of intensity values of the four or more primaries that may give the same color impression to a human viewer. Each such possible intensity value set may be referred to as a “metamer” for that color. Thus, a metamer is a combination (or a set) of at least two groups of colored sub-pixels such that there exists signals that, when applied to each such group, yields a desired color that is perceived by the subject. Such a signal may vary from group of sub-pixels to group, in order to produce the same or substantially similar perceived color. Because of this, a degree of freedom exists to adjust relative values of the primaries for some effect.
In one embodiment, a brain computer interface (BCI) is used to identify metamers. The brain computer interface may include any device or measure suitable to capture information on brain structure and function. For example, the brain computer interface may include Magnetic Resonance Imaging (MRI), functional Magnetic Resonance Imaging (fMRI), magnetoencephalography (MEG), electroencephalogram (EEG), or any combination thereof. In one embodiment, the BCI is an EEG measurement.
In some implementations, for example, the methods, devices, and systems described herein include using SSVEP and brain-computer interfaces (BCIs) to bridge the human brain with computers or external devices. By detecting the SSVEP frequencies from a non-invasively recorded EEG, the users of SSVEP-based brain-computer interface can interact with or control external devices and/or environments through gazing at distinct frequency-coded targets.
In one embodiment, the metamers are in response to a metameric stimuli comprising a light source. A light source may, in one embodiment, be an LED light source, or alternatively, any suitable light source to produce a metameric stimuli. The light source may produce any suitable wavelength to produce a metameric stimuli. For example, the light source may produce a wavelength of about 525 nm (green), about 590 nm (amber), about 625 nm (red), or any combination thereof. In one embodiment, the light source comprises a wavelength between 400 nm and 700 nm. These three LEDs may, in one embodiment, generate a monochromatic (amber) light source and a dichromatic (red and green) light source that are metamers. In one embodiment, the metameric stimuli comprise a monochromatic light source or a dichromatic light source. In one embodiment, the metameric stimuli is alternating. In one embodiment, the metameric stimuli comprise at least two different stimuli, wherein said stimuli differ in color, hue, luminance, saturation, or any combination thereof.
In one embodiment, the method further comprises providing a subject having or suspected of having a color vision deficiency.
Color vision deficiency as described herein includes obstacles in recognizing colors. For example, subjects with red-green color blindness cannot distinguish between red and green. Color vision deficiency is very common and causes many difficulties in life for patients. Color vision deficiency is caused by problems occurring to the development cone cells, and the root cause is the deletion of genes on the chromosomes. It is a genetic disease which cannot be treated currently, and thus it is difficult to completely restore the level of a person with color vision deficiency to that of a normal person. The types of color vision deficiency may include anomalous trichromacy, dichromacy and monochromacy. People with said color vision deficiency may encounter difficulties in distinguishing colors in the images or any real time visual content. Color vision deficiency type or status may be indicated as normal, completely color-blind, or bichromatic color blindness.
In one embodiment, the method further comprises comparing presence of metamers and/or a response to metameric stimuli in said subject having or suspected of having a color vision deficiency to a control response to metameric stimuli.
In one embodiment, when the metamers and/or response to metameric stimuli is within a specific range, the metamers and/or response indicates that said subject has or is likely to have a color vision deficiency.
A second aspect relates to a device for assessing color vision. The device uses metamers identified via steady-state visual evoked potentials (SSVEPs).
This aspect may be in accordance with the previously described aspect.
In one embodiment, the device further includes a brain computer interface (BCI) to automatically identify metamers.
The brain computer interface may include any device or measure suitable to capture information on brain structure and function. For example, the brain computer interface may include Magnetic Resonance Imaging (MRI), functional Magnetic Resonance Imaging (fMRI), magnetoencephalography (MEG), electroencephalogram (EEG), or any combination thereof. In one embodiment, the BCI is an EEG measurement.
Methods, devices and systems of the disclosed technology can implement wireless SSVEP data acquisition and processing. Methods, devices and systems described herein may include a noninvasive platform for continuously monitoring high temporal resolution brain dynamics without requiring conductive gels applied to the scalp. For example, microelectromechanical system EEG sensors, low-power signal acquisition, amplification and digitization, wireless telemetry, and real-time processing may be used. In addition, the methods, devices, and systems described herein may include analytical techniques, such as independent component analysis, which can improve detectability of SSVEP signals.
In one embodiment, the device includes providing alternating metameric stimuli. In one embodiment, the metameric stimuli comprise at least two different stimuli, wherein said metameric stimuli differ in color, hue, luminance, saturation, or any combination thereof.
A third aspect relates to a method of treating color vision deficiency. The method includes measuring one or more metamers identified via steady-state visual evoked potentials (SSVEPs), and administering a treatment for color vision deficiency.
This aspect may be in accordance with the previously described aspects.
In one embodiment, the method further includes providing alternating metameric stimuli in accordance with the previously described aspects. In one embodiment, the metameric stimuli comprise at least two different stimuli, wherein said metameric stimuli differ in color, hue, luminance, saturation, or any combination thereof.
As used herein, the term “effective amount” includes an amount of a compound or pharmaceutical agent that will elicit the biological or medical response of a cell, tissue, system, animal, or human that is being sought, for instance, by a researcher or clinician. The term “therapeutically effective amount” means any amount which, as compared to a corresponding subject who has not received such amount, results in improved treatment, healing, prevention, or amelioration of a disease, disorder, or side effect, or a decrease in the rate of advancement of a disease or disorder. The term also includes within its scope amounts effective to enhance normal physiological function. For use in therapy, therapeutically effective amounts of the compound of the present disclosure (e.g., metamers), as well as salts, solvates, and physiological functional derivatives thereof, may be administered as the raw chemical. Additionally, the active ingredient may be presented as a pharmaceutical composition.
For purposes of this and other aspects of the disclosure, the target “subject” encompasses any vertebrate, such as an animal, preferably a mammal, more preferably a human. In the context of administering a composition of the disclosure for purposes of assessing color vision in a subject comprising in a subject, the target subject encompasses any subject that is a target for color vision assessing. Subjects may include infants, juveniles, adults, or elderly adults. In one embodiment, the subject is an infant, a juvenile, or an adult. In one embodiment, the method is performed in a subject having a preexisting condition or, alternatively, may be performed in a subject having no preexisting condition. The method may also be performed on a subject who has been previously treated for a color vision deficits.
As used herein, the phrase “therapeutically effective amount” means an amount that elicits the biological or medicinal response that is being sought in a tissue, system, animal, individual, or human by a researcher, veterinarian, medical doctor, or other clinician. As such, the therapeutic effect can be a decrease in the severity of symptoms associated with the disorder and/or inhibition (partial or complete) of progression of the disorder, or improved treatment, healing, prevention or elimination of a disorder, or side-effects. The amount needed to elicit the therapeutic response can be determined based on the age, health, size, and sex of the subject. Optimal amounts can also be determined based on monitoring of the subject's response to treatment.
One goal of treatment is the amelioration, either partial or complete, either temporary or permanent, of patient symptoms. Any amelioration is considered successful treatment. This is especially true as amelioration of some magnitude may allow reduction of other medical or surgical treatment which may be more toxic or invasive to the patient. The treatment as described herein may further be used to train color vision in healthy individuals. The treatment described herein may be conducted non-invasively or invasively.
As used herein a sample may include any sample obtained from a living system or subject, including, for example, blood, serum, and/or tissue. In one embodiment, a sample is obtained through sampling by minimally invasive or non-invasive approaches (for example, by eye excretion, urine collection, stool collection, blood drawing, needle aspiration, and other procedures involving minimal risk, discomfort, or effort). Alternatively, samples may be gaseous (for example, breath that has been exhaled) or liquid fluid. Liquid samples may include, for example, eye secretion, urine, blood, serum, interstitial fluid, edema fluid, saliva, lacrimal fluid, inflammatory exudates, synovial fluid, abscess, empyema or other infected fluid, cerebrospinal fluid, sweat, pulmonary secretions (sputum), seminal fluid, feces, bile, intestinal secretions, nasal excretions, and other liquids. Samples may also include a clinical sample such as serum, plasma, other biological fluid, or tissue samples, and also includes cells in culture, cell supernatants, and cell lysates. In one embodiment, the sample is selected from the group consisting of whole blood, serum, urine, and nasal excretion. Samples may be in vivo or ex vivo.
A treatment for color vision deficiency may include any method, device, or system that improves and/or treats a color vision deficiency. For example, a treatment may include SVEP-based operant conditioning protocol as described herein to improve color vision in people with color vision deficiency. A treatment may further include any additional treatment known to those skilled in the art for improving color vision deficiency, including, for example, wearing a pair of smart glasses, which, may include a device as described herein.
A fourth aspect relates to a method of treating color vision deficiency. The method includes measuring a response to metameric stimuli identified by the device described herein, and administering a treatment for color vision deficiency.
This aspect may be in accordance with the previously described aspects.
A fifth aspect relates to a system for identifying a response to one or more metameric stimuli. The system includes providing one or more steady-state visual evoked potentials (SSVEP), and identifying one or more metamers.
This aspect may be in accordance with the previously described aspects.
In one embodiment, the system is used to visualize information processing in the cortex of a subject. In one embodiment, a brain computer interface (BCI) is used to identify said response to one or more metameric stimuli in accordance with the previously described aspects.
In one embodiment, the one or more metameric stimuli comprise a light source in accordance with the previously described aspects. In one embodiment, the light source comprises a wavelength between 400 nm and 700 nm. In one embodiment, the metameric stimuli comprise a monochromatic light source or a dichromatic light source. In one embodiment, the subject has or is suspected of having a color vision deficiency.
In one embodiment, the metamers and/or response to metameric stimuli in said subject having or suspected of having a color vision deficiency is compared to a control metamer and/or response to metameric stimuli. In one embodiment, when said metamer and/or response to metameric stimuli in said subject having or suspected of having a color vision deficiency is within a specific range, said response indicates that said subject has or is likely to have a color vision deficiency.
In one embodiment, the system provides real-time analysis of brain activity. The system described herein may, in one embodiment, elicit and analyze a subject's SSVEP in real-time while iteratively adjusting the luminances of the green and red LEDs that produce the dichromatic source until the SSVEP is minimized, thereby identifying a metamer to the monochromatic amber source (the luminance of which is fixed throughout). This process of identifying the specific settings of the dichromatic source that are metameric to the monochromatic source is a two-dimensional optimization problem, which may be solved using gradient descent based on finite differences.
In one embodiment, a relative color of two light sources is adjusted until one or more metamers are identified.
A sixth aspect relates to a method of individually modifying color vision. The method includes utilizing feedback of brain activity elicited in response to metamers and colors that are close to being metamers identified by the methods described herein.
This aspect may be in accordance with the previously described aspects.
A seventh aspect relates to a method for assessing color vision. The method includes measuring neural activity using a human-computer interface or brain-computer interface.
This aspect may be in accordance with the previously described aspects.
In one embodiment, the measuring uses neural responses to flashing lights.
In one embodiment, the measuring is based on a neural imaging system and a flashing stimulator.
An eighth aspect relates to a method for assessing color vision using neural activity as a means to personalize visual displays.
This aspect may be in accordance with the previously described aspects.
A ninth aspect relates to a method for assessing light sensitive cells in the nervous system using flashing lights.
This aspect may be in accordance with the previously described aspects.
In the following description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments which may be practiced. These embodiments are described in detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that structural, logical and electrical changes may be made without departing from the scope of the present disclosure. The following description of example embodiments is, therefore, not to be taken in a limited sense.
The present disclosure may be further illustrated by reference to the following examples. The examples are intended to illustrate, but by no means are intended to limit, the scope of the present disclosure as set forth in the appended claims.
The following examples are intended to illustrate, but by no means are intended to limit, the scope of the present disclosure as set forth in the appended claims.
All experiments were approved by the institutional review board of the New York State Department of Health's Wadsworth Center.
Participants—Nineteen people were studied (five females and fourteen males, 20-72 years of age). Nine people completed Experiment One; nine people completed Experiment Two; three people completed Experiment Three; and three people completed Experiment Four. One person completed both Experiment One and Experiment Two (E1 (i.e. excluded)=S205), one person completed both Experiment Two and Experiment Four S201=S401), and two people completed experiments one, two, and four (S107=S206=S402 and S105=S207=S403). The data from one participant was excluded from Experiment One (E1) due to a technical issue. Data from two participants were excluded from Experiment Two because they did not generate a measurable SSVEP. The color vision of each of the participants was screened using the first 25 plates of the 38 plate Ishihara pseudoisochromatic test [26] and/or the Farnsworth D-15 test [27]. One of the participants without CVDs completed their screening online and two others self-reported having no CVD.
EEG recording—EEG was recorded using a 16-channel g.USB (g.tec Medical Engineering GmbH, Austria) B-series amplifier. EEG electrodes were located in a mesh cap (Electro-Cap International, Inc. Eaton, Ohio) at the following 10-10 international system locations: F3, Fz, F4, T7, C3, CZ, C4, T8, CP3, CP4, P3, Pz, P4, PO7, PO8, and Oz [28]. During the recordings, the EEG data were referenced to an electrode over the right mastoid and a ground electrode was placed on the left mastoid. Conductive gel was used to reduce to impedance to <40 kΩ. All data were acquired using BCI2000 [29] at a sampling rate of 256 Hz. No bandpass or notch filters were used during acquisition.
Stimulation system—A custom-built stimulation system, consisting of a stimulator, microcontroller, and software interface was used to elicit SSVEPs. Diagrams of the stimulator (including the emitter) and BCI system are shown in
The stimulator was controlled by a Teensy microcontroller (PJRC, Sherwood, Oreg.) running Arduino (Arduino LLC, Somerville, Mass.). A software interface was developed with MATLAB (The Mathworks Inc. Natick, Mass.) to allow the experimenters to digitally adjust the luminance of each LED using pulse-width modulation (PWM; see
PWM adjusts the proportion of the time that the voltage input to the LED is high (i.e. on). In the design, PWM was adjusted with 10-bits of precision (i.e. there were 1024 possible luminance settings for each LED). These potential settings are referred to as D/A units. A setting of 0 denoted that the input to the LED was high 0% of the time and a setting of 1023 represented that the input voltage to the LED was high 100% of the time.
Digital control of the luminance of the LEDs starts at the computer (
Procedure—All experiments were conducted at the David Axelrod Institute, Wadsworth Center, Albany, N.Y. in a room with consistent ambient lighting (˜300 lux).
After completing the informed consent process, participants were asked to sit in a comfortable desk chair for the duration of the study. A chin rest was used during all of the sessions to stabilize the position of each participant relative to the stimulator. Participants were about one foot from the stimulator; it subtended a visual angle of ˜10°.
Experiment one—Experiment One compared behaviorally-identified metamers with those identified using SSVEPs.
Behavioral session—To account for individual differences in color [3, 30], a behavioral session (Exp. 1A) was used to find a combination of red and green lights that had the same color as the amber light at a predetermined luminance setting (600 D/A units). This behavioral session was completed in multiple phases (
Initialization—During initialization, the amber light (monochromatic source) was always set to have an D/A setting of 600. The red and green lights (dichromatic source), however, were randomly set to have D/A settings between 0 and 255. After initialization, the participant was asked for guidance on how to adjust the stimulator (e.g. is the test source too green?). Based on this guidance, the experimenter could choose one of two different actions. For example, if the participant indicated that the test source was too green, the experimenter could either (1) decrease the luminance of the green LED or (2) increase the luminance of the red LED. The choice of action was left to the discretion of the experimenter.
Single-LED calibration—A two-interval discrimination task (2IDT) was used to adjust the dichromatic source to have the same color as the monochromatic source (
Continuing this example, if the participant chooses Alternative 1, the new 2IDT would be between Alternative 1's current red and green D/A settings and a new alternative (i.e. Alternative 3) with D/A settings of 300 and 50. If the participant chooses Alternative 3, the process continues. If the participant chooses Alternative 1 again, the size of the increment is reduced (from 50 to 25 after the choice is repeated once, and then from 25 to 10 D/A units after the choice is repeated a second time). The process is then resumed with a new alternative (i.e. Alternative 4, with settings of 275 and 50]). Calibration of the red light is stopped when the increment equaled 10 and the same alternative is chosen twice in a row. This process is then repeated for the green light.
Dual-LED calibration—Dual-LED calibration (where both the red and green LEDs were adjusted simultaneously) was based on heterochromatic flicker photometry (HFP) [31-33]. Originally described by Walsh [31], HFP is a method for comparing the brightness of two light sources [32]. When flickered sufficiently fast (i.e. above the critical flicker fusion rate [31]) light sources of equal brightness have minimal perceived flicker. In the present disclosure, participants were asked to minimize the flicker between two light sources at a single location using a 2IDT (
Stopping criteria—Both the single-LED and dual-LED calibrations were each repeated until three stopping criteria were met. First, both the single-LED and dual-LED calibrations were completed at least once. Second, the total difference between the current red and green light D/A settings were no more than ten units different from the red or green light D/A settings from the previous single-LED or dual-LED calibration. Third, the participant confirmed that the monochromatic source and dichromatic source appeared to be equivalent. If all of these stopping criteria were met, then the adjustment was considered complete and the red and green settings determined during the behavioral session were saved for use during the SSVEP session. If the stopping criteria were not met and single-LED calibration was just completed, then dual-LED calibration proceeded using the current red and green D/A settings. If the stopping criteria were not met and dual-LED calibration was just completed, then single-LED calibration proceeded using the current red and green D/A settings.
SSVEP session—After the behavioral session, participants completed an SSVEP session (Exp. 1B). For three of the participants, both sessions were completed on the same day. The other six participants completed the SSVEP session on a different day (within one week). During the SSVEP session, EEG was recorded from the participants as described in Methods: EEG Recording. Following setup, each participant was given the opportunity to make final adjustments to the settings of the stimulator. After this, participants completed three runs of stimulation.
Each run of stimulation consisted of 54 six-second trials with an interstimulus interval (ISI) of one second. Previous research has shown that canonical correlation analysis (CCA) can classify SSVEPs with >75% accuracy in 2.25 s (36). The goal was to estimate the size of SSVEPs, which it was thought may require more data than classification. Thus, it was chosen to make each trial 6 s. The onset of each of these trials was detected using the digital input line of the g.USB amplifier. The order of the trials was the same in every run for every participant. Randomizing the order of the trials could have resulted in large relative luminance changes from trial to trial (see
Experiment Two—There were two key differences between Experiment One and Experiment Two. First, during Experiment Two, the SSVEP session came before the behavioral session. Second, during the SSVEP session of Experiment Two, only the monochromatic source was fixed. The method used during the behavioral session of Experiment Two (Exp. 2A) was identical to Experiment One.
SSVEP Session (grid search)—In Experiment Two: SSVEP Session (grid search; Exp. 2B), the D/A settings of the red light and the green light that each person perceived as being metameric with the monochromatic source were assumed to be unknown. Therefore, the settings of the red light and green light that minimized the SSVEP were determined using two grid searches
The length of each trial, ISI, and EEG recording parameters of Experiment Two: SSVEP Session were identical to those used in Experiment One: SSVEP Session.
Experiment Three—The procedure for Experiment Three was identical to that of Experiment Two: SSVEP Session, except that the participants did not complete the fine-grid search.
Experiment Four—Experiment Four used the same EEG and Data Analysis settings as the other experiments. As opposed to conducting a grid search, however, the setting that minimized the SSVEP was identified using gradient descent based on finite differences. The stimulator was initialized to three settings chosen by the experimenter. For S401, these settings were 150 green and 150 red; 15 green and 30 red; and 0 green and 100 red. For S402, these settings were 0 green and 0 red; 0 green and 100 red; and 100 green and 0 red. For S403, these settings were 40 green and 50 red; 30 green and 100 red; and 100 green and 40 red. The system then sampled the settings surrounding the initial setting, computed a gradient, and updated its current estimate of the settings that minimized the SSVEP. This process was then repeated and continued until the difference between the different settings surrounding the current estimate was sufficiently small (i.e., a stopping criterion was met).
Data Analysis—Data analysis for all four experiments was performed using Matlab. The raw EEG data from each participant was zero-phase bandpass filtered using a 4th order IIR Butterworth filter between 3 and 45 Hz. The data was also notch filtered at 60 Hz to remove powerline noise. After filtering, individual six second trials were extracted from the EEG data. Each trial was analyzed using canonical correlation analysis (CCA) (36). CCA is a technique—widely used in BCI—for detecting SSVEPs. Here CCA is used as a relative measure of the size of the SSVEP elicited during each trial (assuming that the noise in the EEG is stable across trials). Details on the use of CCA for detecting SSVEPs was based on those described by Norton et al. (38). The CCA analysis included all 16 channels of EEG data and reference variables (sine and cosine waves) at the first, second, third, fourth, and fifth harmonic frequencies (i.e., 10, 20, 30, 40, and 50 Hz) of the stimulation. Although CCA calculates multiple canonical correlations (the number of canonical correlates is the lesser of the number of reference variables and the number of EEG channels), all but the maximum canonical correlation was discarded in the analysis. After performing CCA on each of the trials, the data from each run were normalized between zero and one.
In Experiment One, behaviorally-identified metamers (Exp. 1A) were compared with those identified using SSVEPs (Exp. 1B). Participants (nine total participants, see Methods) were first asked to identify metamers by manually adjusting the dichromatic source of the stimulator to produce a color that matched the monochromatic light source, which had a fixed luminance setting of 600 digital-to-analog (D/A) units (see Methods;
In Experiment Two (nine participants, see Methods), a more extensive search of the workspace was used to ensure that the results of Exp. 1B were not merely a local minimum. With a two-dimensional grid search, the red and green LED settings were determined (i.e., the two dimensions) of the dichromatic source that minimized the SSVEP (Exp. 2B), and then compared them with the red and green LED settings that the person identified behaviorally (i.e., manually; Exp. 2A;
In Experiment Three, it was investigated whether people with CVDs could be identified using the SSVEP-based method for finding metamers. People with CVDs see colors differently. Thus, their metamers were predicted, as identified by SSVEP, would differ clearly from those of people without CVDs. To test this prediction three people with CVDs (all male) were studied. All three were identified (by the Farnsworth Dichotomous D-15 arrangement test; see (6)) as protans (i.e., they had a defective or missing L-cone). SSVEPs were elicited from these participants using the coarse-grid search of Exp. 2B (see Methods). Their results, shown in
The SSVEP-based grid search is readily amenable to automated closed-loop operation. Thus, in Experiment Four, a prototype SSVEP-based BCI was tested that automatically identifies metamers. All of the tests conducted during Experiment Four were completed online (i.e., closed loop). As described in Methods, this system elicits and analyzes the person's SSVEP in real-time while iteratively adjusting the luminances of the green and red LEDs that produce the dichromatic source until the SSVEP is minimized, thereby identifying a metamer to the monochromatic amber source (the luminance of which is fixed throughout). This process of identifying the specific settings of the dichromatic source that are metameric to the monochromatic source is a two-dimensional optimization problem, which was solved using gradient descent based on finite differences. Examples of the iterative changes made by the automated system are shown in
Starting from three different initial settings, the automated BCI-based system was tested in three Experiment One participants (i.e., three individuals without CVDs; see Methods). Averaged across these three runs, the automated system identified 58 green, 117 red (S401); 76 green, 130 red (S402); and 56 green, 126 red (S403) as the settings that minimized the SSVEP (i.e., were metamers to the monochromatic source) for these three individuals. As illustrated in
In summary, Experiments One and Two show that, in people without CVDs, an SSVEP-based method can identify metameric pairs that match those identified by a standard behavioral method; Experiment Three shows that this SSVEP method can differentiate between people with and without CVDs; and Experiment Four shows that the method can be fully automated. The practical value of this new method for assessing color vision depends on its requirements and capabilities compared to current methods and on its potential for further development.
At present, color vision is evaluated mainly by behavioral methods, all of which require the attention and active participation of the person. Most prominent among these is the anomaloscope, which is the only behavioral method able to diagnose both type and severity of red-green and blue-yellow color blindness (reviewed in (7, 6)). In addition to requiring the active participation of the person being examined, the anomaloscope requires extensive training of the examiner and considerable time to administer (8). In contrast, the SSVEP-based method described here does not require the person's participation (i.e., it is a passive BCI (9)); with appropriate signal analysis, it could even be used when the person's eyes are closed (10). In addition, this new method needs minimal examiner training and can be fully automated. Furthermore, initial data (i.e., Exp. 3) indicate that it can detect protan-type CVDs accurately. (The new method can probably detect other types of CVDs as well, but this must be verified empirically.) Given these advantages SSVEP-based assessment of color vision could be particularly useful for identifying CVDs in those who cannot respond behaviorally, such as young children or people with motor or cognitive deficits.
The new SSVEP-based color-vision assessment method described here is fundamentally different from previous electrophysiological studies (11, 12, 13, 14, 15). It has four unique features. First, because it is based on the identification of metamers, it is an objective method for performing color matching, which is the gold standard of color-vision assessment. Second, by using new BCI-based procedures to analyze signals from multiple channels and frequencies, it enhances SSVEP detection and measurement (16). Third, because it poses metamer identification as an optimization problem, this new method can be fully automated and can take advantage of a wide range of powerful optimization algorithms. Fourth, the new method is generalizable; it can identify a metamer to a light source having essentially any spectral distribution.
The last two unique features—automatization and generalization—give the new method wide applicability for at least four important purposes. First, this new method could enable clinical detection and analysis of color vision deficits in young children and in others unable to participate in standard assessment methods. With further development, the method might be incorporated into a simple device that could be part of a standard pediatric examination. Second, the new method could find significant industrial applications in selecting color schemes for products and designing formats for digital displays (see (17)).
Third, because SSVEPs reflect neural activity in cortical and subcortical areas involved in visual function (18, 19), SSVEP-based detection of metamers could help to explore neural mechanisms underlying color vision (20, 21, 22). For example, it might make it possible to isolate and characterize the cortical responses to activation of intrinsically photosensitive retinal ganglion cells (ipRGCs), a photoreceptor type that contributes to visual function (reviewed in (23)).
Fourth, this new method might form the basis for the first therapeutic intervention for people with CVDs. It is now clear that the simplest reflexes are plastic; they can be changed by operant conditioning, and these changes can help to restore useful function to people with spinal cord injury (24). Given that color vision displays comparable plasticity (25), an SSVEP-based operant conditioning protocol might prove able to improve color vision in people with CVDs.
Further studies should confirm the diagnostic ability of SSVEP-based BCIs to identify the type and severity of CVDs. As a part of these studies, improvements should be made to the stimulation system (e.g., optics, calibration) and protocol (e.g., identification of the optimal number and location of EEG electrodes, monitoring of the pupil size). Lastly, these studies should include direct comparisons to existing color vision assessment methods (e.g., the anomaloscope).
In conclusion, this study describes, demonstrates, and validates a novel method for assessing color vision founded on the hypothesis that flickering visual stimuli that alternate between two metamers will not elicit an SSVEP. Unlike standard color vision assessment methods, this SSVEP-based method does not need the active participation of the person being tested. The new method provides results comparable to those of standard methods, can identify those with color vision deficits (CVDs), can be fully automated, and can be applied to a wide variety of light sources. In addition to its clear clinical diagnostic applications, SSVEP-based testing of color vision should have industrial, scientific, and possibly therapeutic applications.
Although preferred embodiments have been depicted and described in detail herein, it will be apparent to those skilled in the relevant art that various modifications, additions, substitutions, and the like can be made without departing from the spirit of the invention and these are therefore considered to be within the scope of the invention as defined in the claims which follow.
List of References cited herein is as follows, all of which are hereby incorporated by reference in their entirety:
This application claims benefit of U.S. Provisional Patent Application Ser. No. 63/207,144, filed Feb. 9, 2021, which is hereby incorporated by reference in its entirety.
This invention was made with government support under 7P41EB018783 awarded by the National Institutes of Health and 5101CX001812 awarded by the Veterans Administration. The government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 63207144 | Feb 2021 | US |
