Analysis of marketing and entertainment effectiveness using central nervous system, autonomic nervous sytem, and effector data

Abstract

An example system includes an analyzer to identify a degree of amplitude synchrony between a first pattern in a first frequency band in first neuro-response data and a second pattern in a second frequency band in the first neuro-response data, the first neuro-response data gathered via a first modality of collection from a subject while the subject is exposed to media, and modify the degree of amplitude synchrony in response to activity in second neuro-response data, the second neuro-response data gathered via a second modality of collection from the subject while the subject is exposed to the media, the activity corresponding in time to at least a portion of the first pattern or the second pattern. The example system includes an estimator to determine an effectiveness of the media based on the modified degree of amplitude synchrony.

Description

FIELD OF THE DISCLOSURE

The present disclosure relates to the analysis of the effectiveness of marketing and entertainment using central nervous system, autonomic nervous system, and effector measurement mechanisms.

BACKGROUND

Conventional systems for measuring the effectiveness of entertainment and marketing including advertising, brand messages, and product placement rely on either survey based evaluations or limited neurophysiological measurements used in isolation. These conventional systems provide some useful data but are highly inefficient and inaccurate due to a variety of semantic, syntactic, metaphorical, cultural, social, and interpretative errors and biases. The systems and techniques themselves used to obtain neurophysiological measurements are also highly limited.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the following description taken in conjunction with the accompanying drawings, which illustrate particular examples.

FIG. 1 illustrates one example of a system for determining the effectiveness of marketing and entertainment by using central nervous system, autonomic nervous system, and effector measures.

FIG. 2 illustrates a particular example of a system having an intelligent protocol generator and presenter device and individual mechanisms for intra-modality response synthesis.

FIG. 3 illustrates a particular example of an intra-modality synthesis mechanism for Electroencephalography (EEG).

FIG. 4 illustrates another particular example of synthesis for Electroencephalography (EEG).

FIG. 5 illustrates a particular example of a cross-modality synthesis mechanism.

FIG. 6 is one example of a sample flow process diagram showing a technique for obtaining neurological and neurophysiological data.

FIG. 7 provides one example of a system that can be used to implement one or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of the disclosure including the best modes contemplated by the inventors for carrying out the teachings of the disclosure. Examples of these specific examples are illustrated in the accompanying drawings. While the disclosure is described in conjunction with these specific examples, it will be understood that it is not intended to limit the disclosure to the described examples. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the disclosure as defined by the appended claims.

For example, the techniques and mechanisms of the present disclosure will be described in the context of evaluating entertainment and marketing effectiveness. However, it should be noted that the techniques and mechanisms of the present disclosure apply to a variety of different types of entertainment and marketing such as video and audio streams, media advertising, product placement, brand effectiveness, printed advertisements, etc. It should be noted that various mechanisms and techniques can be applied to any type of stimuli. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. Particular examples of the present disclosure may be implemented without some or all of these specific details. In other instances, well known process operations have not been described in detail in order not to unnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure will sometimes be described in singular form for clarity. However, it should be noted that some examples include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. For example, a system uses a processor in a variety of contexts. However, it will be appreciated that a system can use multiple processors while remaining within the scope of the present disclosure unless otherwise noted. Furthermore, the techniques and mechanisms of the present disclosure will sometimes describe a connection between two entities. It should be noted that a connection between two entities does not necessarily mean a direct, unimpeded connection, as a variety of other entities may reside between the two entities. For example, a processor may be connected to memory, but it will be appreciated that a variety of bridges and controllers may reside between the processor and memory. Consequently, a connection does not necessarily mean a direct, unimpeded connection unless otherwise noted.

Overview

Consequently, it is desirable to provide improved methods and apparatus for measuring and analyzing neurological and neurophysiological data, such as central nervous system, autonomic nervous system, and effector data obtained during evaluation of the effectiveness of entertainment and marketing materials.

Central nervous system, autonomic nervous system, and effector data is measured and analyzed to determine the effectiveness of marketing and entertainment stimuli. A data collection mechanism including multiple modalities such as Electroencephalography (EEG), Electrooculography (EOG), Galvanic Skin Response (GSR), etc., collects response data from subjects exposed to marketing and entertainment stimuli. A data cleanser mechanism filters the response data. The response data is enhanced using intra-modality response synthesis and/or a cross-modality response synthesis.

Examples

Conventional mechanisms for obtaining information about the effectiveness of various types of stimuli such as marketing and entertainment materials have generally relied on focus groups and surveys. Subjects are provided with oral and written mechanisms for conveying their thoughts and feelings elicited in response to a particular advertisement, brand, media clip, etc. These oral and written mechanisms provide some limited information on the effectiveness of the marketing and entertainment materials, but have a variety of limitations. For example, subjects may be unable or unwilling to express their true thoughts and feelings about a topic, or questions may be phrased with built in bias. Articulate subjects may be given more weight than nonexpressive ones. A variety of semantic, syntactic, metaphorical, cultural, social and interpretive biases and errors prevent accurate and repeatable evaluation.

Some efforts have been made to use isolated neurological and neurophysiological measurements to gauge subject responses. Some examples of central nervous system measurement mechanisms include Functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG). fMRI measures blood oxygenation in the brain that correlates with increased neural activity. However, current implementations of fMRI have poor temporal resolution of few seconds. EEG measures electrical activity associated with post synaptic currents occurring in the milliseconds range. Subcranial EEG can measure electrical activity with the most accuracy, as the bone and dermal layers weaken transmission of a wide range of frequencies. Nonetheless, surface EEG provides a wealth of electrophysiological information if analyzed properly.

Autonomic nervous system measurement mechanisms include Galvanic Skin Response (GSR), Electrocardiograms (EKG), pupillary dilation, etc. Effector measurement mechanisms include Electrooculography (EOG), eye tracking, facial emotion encoding, reaction time, etc.

Some conventional mechanisms cite a particular neurological or neurophysiological measurement characteristic as indicating a particular thought, feeling, mental state, or ability. For example, one mechanism purports that the contraction of a particular facial muscle indicates the presence of a particular emotion. Others measure general activity in particular areas of the brain and suggest that activity in one portion may suggest lying while activity in another portion may suggest truthfulness. However, these mechanisms are severely limited in their ability to accurately reflect a subject's actual thoughts. It is recognized that a particular region of the brain can not be mapped to a particular thought. Similarly, a particular eye movement can not be mapped to a particular emotion. Even when there is a strong correlation between a particular measured characteristic and a thought, feeling, or mental state, the correlations are not perfect, leading to a large number of false positives and false negatives.

Consequently, the techniques and mechanisms of the present disclosure intelligently blend multiple modes and manifestations of precognitive neural signatures with cognitive neural signatures and post cognitive neurophysiological manifestations to more accurately access the effectiveness of marketing and entertainment materials. In some examples, autonomic nervous system measures are themselves used to validate central nervous system measures. Effector and behavior responses are blended and combined with other measures.

Intra-modality measurement enhancements are made in addition to the cross-modality measurement mechanism enhancements. According to various examples, brain activity is measured not just to determine the regions of activity, but to determine interactions and types of interactions between various regions. The techniques and mechanisms of the present disclosure recognize that interactions between neural regions support orchestrated and organized behavior. Thoughts and abilities are not merely based on one part of the brain but instead rely on network interactions between brain regions.

The techniques and mechanisms of the present disclosure further recognize that different frequency bands used for multi-regional communication can be indicative of the effectiveness of stimuli. For example, associating a name to a particular face may entail activity in communication pathways tuned to particular frequencies. According to various examples, select frequency bands are analyzed after filtering. The techniques and mechanisms of the present disclosure also recognize that high gamma band frequencies have significance. Inter-frequency coupling in the signals have also been determined to indicate effectiveness. Signals modulated on a carrier wave have also been determined to be important in evaluating thoughts and actions. In particular examples, the types of frequencies measured are subject and/or task specific. For example, particular types of frequencies in specific pathways are measured if a subject is being exposed to a new product.

In particular examples, evaluations are calibrated to each subject and synchronized across subjects. In particular examples, templates are created for subjects to create a baseline for measuring pre and post stimulus differentials. According to various examples, stimulus generators are intelligent, and adaptively modify specific parameters such as exposure length and duration for each subject being analyzed.

Consequently, the techniques and mechanisms of the present disclosure provide a central nervous system, autonomic nervous system, and effector measurement and analysis system that can be applied to evaluate the effectiveness of materials such as marketing and entertainment materials. Marketing materials may include advertisements, commercials, media clips, brand messages, product brochures, company logos, etc. An intelligent stimulus generation mechanism intelligently adapts output for particular users and purposes. A variety of modalities can be used including EEG, GSR, EKG, pupillary dilation, EOG, eye tracking, facial emotion encoding, reaction time, etc. Individual modalities such as EEG are enhanced by intelligently recognizing neural region communication pathways. Cross modality analysis is enhanced using a synthesis and analytical blending of central nervous system, autonomic nervous system, and effector signatures. Synthesis and analysis by mechanisms such as time and phase shifting, correlating, and validating intra-modal determinations allow generation of a composite output characterizing the effectiveness of various stimuli.

FIG. 1 illustrates one example of a system for determining the effectiveness of marketing and entertainment by using central nervous system, autonomic nervous system, and effector measures. According to various examples, the neuroanalysis system includes a protocol generator and presenter device 101. In particular examples, the protocol generator and presenter device 101 is merely a presenter device and merely presents stimuli to a user. The stimuli may be a media clip, a commercial, a brand image, a magazine advertisement, a movie, an audio presentation, particular tastes, smells, textures and/or sounds. The stimuli can involve a variety of senses and occur with or without human supervision. Continuous and discrete modes are supported. According to various examples, the protocol generator and presenter device 101 also has protocol generation capability to allow intelligent customization of stimuli provided to a subject.

According to various examples, the subjects are connected to data collection devices 105. The data collection devices 105 may include a variety of neurological and neurophysiological measurement mechanisms such as EEG, EOG, GSR, EKG, pupillary dilation, eye tracking, facial emotion encoding, and reaction time devices, etc. In particular examples, the data collection devices 105 include EEG 111, EOG 113, and GSR 115. In some instances, only a single data collection device is used. Data collection may proceed with or without human supervision.

The data collection device 105 collects neuro-physiological data from multiple sources. This includes a combination of devices such as central nervous system sources (EEG), autonomic nervous system sources (GSR, EKG, pupillary dilation), and effector sources (EOG, eye tracking, facial emotion encoding, reaction time). In particular examples, data collected is digitally sampled and stored for later analysis. In particular examples, the data collected could be analyzed in real-time. According to particular examples, the digital sampling rates are adaptively chosen based on the neurophysiological and neurological data being measured.

In one particular example, the neurological and neurophysiological analysis system includes EEG 111 measurements made using scalp level electrodes, EOG 113 measurements made using shielded electrodes to track eye data, GSR 115 measurements performed using a differential measurement system, a facial muscular measurement through shielded electrodes placed at specific locations on the face, and a facial affect graphic and video analyzer adaptively derived for each individual.

In particular examples, the data collection devices are clock synchronized with a protocol generator and presenter device 101. The data collection system 105 can collect data from a single individual (1 system), or can be modified to collect synchronized data from multiple individuals (N+1 system). The N+1 system may include multiple individuals synchronously tested in isolation or in a group setting. In particular examples, the data collection devices also include a condition evaluation subsystem that provides auto triggers, alerts and status monitoring and visualization components that continuously monitor the status of the subject, data being collected, and the data collection instruments. The condition evaluation subsystem may also present visual alerts and automatically trigger remedial actions.

According to various examples, the neurological and neurophysiological analysis system also includes a data cleanser device 121. In particular examples, the data cleanser device 121 filters the collected data to remove noise, artifacts, and other irrelevant data using fixed and adaptive filtering, weighted averaging, advanced component extraction (like PCA, ICA), vector and component separation methods, etc. This device cleanses the data by removing both exogenous noise (where the source is outside the physiology of the subject) and endogenous artifacts (where the source could be neurophysiological like muscle movement, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectively isolate and review the response data and identify epochs with time domain and/or frequency domain attributes that correspond to artifacts such as line frequency, eye blinks, and muscle movements. The artifact removal subsystem then cleanses the artifacts by either omitting these epochs, or by replacing these epoch data with an estimate based on the other clean data (for example, an EEG nearest neighbor weighted averaging approach).

According to various examples, the data cleanser device 121 is implemented using hardware, firmware, and/or software. It should be noted that although a data cleanser device 121 is shown located after a data collection device 105 and before synthesis devices 131 and 141, the data cleanser device 121 like other components may have a location and functionality that varies based on system implementation. For example, some systems may not use any automated data cleanser device whatsoever. In other systems, data cleanser devices may be integrated into individual data collection devices.

The data cleanser device 121 passes data to the intra-modality response synthesizer 131. The intra-modality response synthesizer 131 is configured to customize and extract the independent neurological and neurophysiological parameters for each individual in each modality and blend the estimates within a modality analytically to elicit an enhanced response to the presented stimuli. In particular examples, the intra-modality response synthesizer also aggregates data from different subjects in a dataset.

According to various examples, the cross-modality response synthesis or fusion device 141 blends different intra-modality responses, including raw signals and signals output from synthesizer 131. The combination of signals enhances the measures of effectiveness within a modality. The cross-modality response fusion device 141 can also aggregate data from different subjects in a dataset.

According to various examples, the system also includes a composite enhanced effectiveness estimator (CEEE) that combines the enhanced responses and estimates from each modality to provide a blended estimate of the effectiveness of the marketing and entertainment stimuli for various purposes. Stimulus effectiveness measures are output at 161.

FIG. 2 illustrates a particular example of a system having an intelligent protocol generator and presenter device (where the intelligence could include a feedback based on prior responses) and individual mechanisms for intra-modality response synthesis.

According to various examples, the system includes a protocol generator and presenter device 201. In particular examples, the protocol generator and presenter device 201 is merely a presenter device and merely presents preconfigured stimuli to a user. The stimuli may be media clips, commercials, brand images, magazine advertisements, movies, audio presentations, particular tastes, textures, smells, and/or sounds. The stimuli can involve a variety of senses and occur with or without human supervision. Continuous and discrete modes are supported. According to various examples, the protocol generator and presenter device 201 also has protocol generation capability to allow intelligent modification of the types of stimuli provided to a subject. In particular examples, the protocol generator and presenter device 201 receives information about stimulus effectiveness measures from component 261.

The protocol generator and presenter device 201 dynamical adapts stimuli presentation by using information from the analysis of attention, analysis of emotional engagement, analysis of memory retention, analysis of overall visual, audio, other sensory effectiveness, and ad, show, or content effectiveness, implicit analysis of brand impact, implicit analysis of brand meaning, implicit analysis of brand archetype, implicit analysis of brand imagery, implicit analysis of brand words, explicit analysis of brand impact, explicit analysis of brand meaning, explicit analysis of brand archetype, explicit analysis of brand imagery, explicit analysis of brand words; analysis of characters in the ad, analysis of emotive response to characters in the ad/show/content, analysis of character interaction in the ad/show/content; elicitation of core components of the ad/show/content for print purposes, elicitation of core components of the ad/show/content for billboard purposes; elicitation of the ocular metrics like hot-zones in the ad/show/content by eye dwell time, micro and macro saccade separation, saccadic returns to points of interest; elicitation of points for product placement, elicitation of points for logo and brand placement; analysis of game effectiveness, analysis of product placement in games; analysis of website effectiveness, webpage dropoff in a site. According to various examples, the information is provided by component 261. In particular examples, the protocol generator and presenter device 201 can itself obtain some of this information

The protocol generator and presenter device 201 uses a data model along with linguistic and image tools like valence, arousal, meaning matched word/phrase generators, valence and arousal matched image/video selectors to generate parameters regarding the experiment. In particular examples, the protocol generator and presenter device 201 may vary individual presentation parameters like time and duration of the experiment, the number of repetitions of the stimuli based on signal to noise requirements, and the number and repetitions of the stimuli for habituation and wear-out studies, the type and number of neuro-physiological baselines, and the self reporting surveys to include.

In particular examples, the protocol generator and presenter device 201 customizes presentations to a group of subjects or to individual subjects. According to various examples, the subjects are connected to data collection devices 205. The data collection devices 205 may involve any type of neurological and neurophysiological mechanism such as EEG, EOG, GSR, EKG, pupillary dilation, eye tracking, facial emotion encoding, reaction rime, etc. In particular examples, the data collection devices 205 include EEG 211, EOG 213, and GSR 215. In some instances, only a single modality is used. In other instances, multiple modalities are used and may vary depending on the type of effectiveness evaluation. Data collection may proceed without or without human supervision.

The data collection device 205 automatically collects neuro-physiological data from multiple sources. This includes a combination of devices such as central nervous system sources (EEG), autonomic nervous system sources (GSR, EKG, pupillary dilation), and effector sources (EOG, eye tracking, facial emotion encoding, reaction time). In particular examples, data collected is digitally sampled and stored for later analysis. The digital sampling rates are adaptively chosen based on the type of neurophysiological and neurological data being measured.

In particular examples, the system includes EEG 211 measurements made using scalp level electrodes, EOG 213 measurements made using shielded electrodes to track eye data, GSR 215 measurements performed using a differential measurement system, and a facial affect graphic and video analyzer adaptively derived for each individual.

According to various examples, the data collection devices are clock synchronized with a protocol generator and presenter device 201. The data collection system 205 can collect data from a single individual (1 system), or can be modified to collect synchronized data from multiple individuals (N+1 system). The N+1 system could include multiple individuals synchronously recorded in a group setting or in isolation. In particular examples, the data collection devices also include a condition evaluation subsystem that provides auto triggers, alerts and status monitoring and visualization components that continuously monitor the status of the data being collected as well as the status of the data collection instruments themselves. The condition evaluation subsystem may also present visual alerts and automatically trigger remedial actions.

According to various examples, the system also includes a data cleanser device 221. In particular examples, the data cleanser device 221 filters the collected data to remove noise, artifacts, and other irrelevant data using fixed and adaptive filtering, weighted averaging, advanced component extraction (like PCA, ICA), vector and component separation methods, etc. This device cleanses the data by removing both exogenous noise (where the source is outside the physiology of the subject) and endogenous artifacts (where the source could be neurophysiological like muscle movement, eye blinks).

The artifact removal subsystem includes mechanisms to selectively isolate and review the output of each of the data and identify epochs with time domain and/or frequency domain attributes that correspond to artifacts such as line frequency, eye blinks, and muscle movements. The artifact removal subsystem then cleanses the artifacts by either omitting these epochs, or by replacing these epoch data with an estimate based on the other clean data (for example, an EEG nearest neighbor weighted averaging approach), or removes these components from the signal.

According to various examples, the data cleanser device 221 is implemented using hardware, firmware, and/or software. It should be noted that although a data cleanser device 221 is shown located after a data collection device 205 and before synthesis devices 231 and 241, the data cleanser device 221 like other components may have a location and functionality that varies based on system implementation. For example, some systems may not use any automated data cleanser device whatsoever. In other systems, data cleanser devices may be integrated into individual data collection devices.

The data cleanser device 221 passes data to the intra-modality response synthesizer 231. The intra-modality response synthesizer is configured to customize and extract the independent neurological and neurophysiological parameters for each individual in each modality and blend the estimates within a modality analytically to elicit an enhanced response to the presented stimuli. In particular examples, the intra-modality response synthesizer also aggregates data from different subjects in a dataset. According to various examples, various modules perform synthesis in parallel or in series, and can operate on data directly output from a data cleanser device 221 or operate on data output from other modules. For example, EEG synthesis module 233 can operate on the output of EOG synthesis module 235. GSR module 237 can operate on data output from EEG module 233.

According to various examples, the cross-modality response synthesis or fusion device 241 blends different intra-modality responses, including raw signals as well as signals output from synthesizer 231. The combination of signals enhances the measures of effectiveness within a modality. The cross-modality response fusion device 241 can also aggregate data from different subjects in a dataset.

According to various examples, the neuro analysis system also includes a composite enhanced effectiveness estimator (CEEE) that combines the enhanced responses and estimates from each modality to provide a blended estimate of the effectiveness of the marketing and advertising stimuli for various purposes. Stimulus effectiveness measures are output at 261. A portion or all of the effectiveness measures (intra-modality synthesizer, cross modality fusion device, and/or the CEEE) can be provided as feedback to a protocol generator and presenter device 201 to further customize stimuli presented to users 203.

FIG. 3 illustrates a particular example of an intra-modality synthesis mechanism. In particular examples, EEG response data is synthesized to provide an enhanced assessment of marketing and entertainment effectiveness. According to various examples, EEG measures electrical activity resulting from thousands of simultaneous neural processes associated with different portions of the brain. EEG data can be classified in various bands. According to various examples, brainwave frequencies includes delta, theta, alpha, beta, and gamma frequency ranges. Delta waves are classified as those less than 4 Hz and are prominent during deep sleep. Theta waves have frequencies between 3.5 to 7.5 Hz and are associated with memories, attention, emotions, and sensations. Theta waves are typically prominent during states of internal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around 10 Hz. Alpha waves are prominent during states of relaxation. Beta waves have a frequency range between 14 and 30 Hz. Beta waves are prominent during states of motor control, long range synchronization between brain areas, analytical problem solving, judgment, and decision making. Gamma waves occur between 30 and 60 Hz and are involved in binding of different populations of neurons together into a network for the purpose of carrying out a certain cognitive or motor function, as well as in attention and memory. Because the skull and dermal layers attenuate waves in this frequency range, brain waves above 75-80 Hz are difficult to detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosure recognize that analyzing high gamma band (kappa-band: Above 60 Hz) measurements, in addition to theta, alpha, beta, and low gamma band measurements, enhances neurological attention, emotional engagement and retention component estimates. In particular examples, EEG measurements including difficult to detect high gamma or kappa band measurements are obtained, enhanced, and evaluated at 301. At 303, subject and task specific signature sub-bands in the theta, alpha, beta, gamma and kappa bands are identified to provide enhanced response estimates. According to various examples, high gamma waves (kappa-band) above 80 Hz (typically detectable with sub-cranial EEG and magnetoencephalography) can be used in inverse model-based enhancement of the frequency responses to the stimuli.

Various examples of the present disclosure recognize that particular sub-bands within each frequency range have particular prominence during certain activities. A subset of the frequencies in a particular band is referred to herein as a sub-band. For example, a sub-band may include the 40-45 Hz range within the gamma band. In particular examples, multiple sub-bands within the different bands are selected while remaining frequencies are band pass filtered. In particular examples, multiple sub-band responses may be enhanced, while the remaining frequency responses may be attenuated.

At 305, inter-regional coherencies of the sub-band measurements are determined. According to various examples, inter-regional coherencies are determined using gain and phase coherences, Bayesian references, mutual information theoretic measures of independence and directionality, and Granger causality techniques of the EEG response in the different bands. In particular examples, inter-regional coherencies are determined using fuzzy logic to estimate effectiveness of the stimulus in evoking specific type of responses in individual subjects.

At 307, inter-hemispheric time-frequency measurements are evaluated. In particular examples, asymmetries in specific band powers, asymmetries in inter-regional intra-hemispheric coherences, and asymmetries in inter-regional intra-hemisphere inter-frequency coupling are analyzed to provide measures of emotional engagement.

At 309, inter-frequency coupling assessments of the response are determined. In particular examples, a coupling index corresponding to the measure of specific band activity in synchrony with the phase of other band activity is determined to ascertain the significance of the marketing and advertising stimulus or sub-sections thereof. At 313, a reference scalp power frequency curve is determined using a baseline electrocorticogram (ECoG) power by frequency function driven model. The reference scale power frequency curve is compared to an individual scalp record power by frequency curve to derive scaled estimates of marketing and entertainment effectiveness. According to various examples, scaled estimates are derived used fuzzy scaling.

At 315, an information theory based band-weighting model is used for adaptive extraction of selective dataset specific, subject specific, task specific bands to enhance the effectiveness measure. Adaptive extraction may be performed using fuzzy scaling. At 321, stimuli can be presented and enhanced measurements determined multiple times to determine the variation or habituation profiles across multiple presentations. Determining the variation and/or habituation profiles provides an enhanced assessment of the primary responses as well as the longevity (wear-out) of the marketing and entertainment stimuli. At 323, the synchronous response of multiple individuals to stimuli presented in concert is measured to determine an enhanced across subject synchrony measure of effectiveness. According to various examples, the synchronous response may be determined for multiple subjects residing in separate locations or for multiple subjects residing in the same location.

Although a variety of synthesis mechanisms are described, it should be recognized that any number of mechanisms can be applied in sequence or in parallel with or without interaction between the mechanisms. In some examples, processes 321 and 323 can be applied to any modality. FIG. 4 illustrates a particular example of synthesis for Electroencephalography (EEG) data, including ERP and continuous EEG.

ERPs can be reliably measured using electroencephalography (EEG), a procedure that measures electrical activity of the brain. Although an EEG reflects thousands of simultaneously ongoing brain processes, the brain response to a certain stimulus may not be visible using EEG. ERP data includes cognitive neurophysiological responses that manifests after the stimulus is presented. In many instances, it is difficult to see an ERP after the presentation of a single stimulus. The most robust ERPs are seen after tens or hundreds of individual presentations are combined. This combination removes noise in the data and allows the voltage response to the stimulus to stand out more clearly. In addition to averaging the example includes techniques to extract single trial evoked information from the ongoing EEG.

While evoked potentials reflect the processing of the physical stimulus, event-related potentials are caused by the “higher” processes, that might involve memory, expectation, attention, or changes in the mental state, among others. According to various examples, evidence of the occurrence or non-occurrence of specific time domain components in specific regions of the brain are used to measure subject responsiveness to specific stimulus.

According to various examples, ERP data can be enhanced using a variety of mechanisms. At 401, event related time-frequency analysis of stimulus response—event related power spectral perturbations (ERPSPs)—is performed across multiple frequency bands such as theta, delta, alpha, beta, gamma and high gamma (kappa). According to various examples, a baseline ERP is determined. At 403, a differential event related potential (DERP) is evaluated to assess stimulus attributable differential responses.

At 405, a variety of analysis techniques including principal component analysis (PCA), independent component analysis (ICA), and Monte Carlos analysis can be applied to evaluate an ordered ranking of the effectiveness across multiple stimuli. In particular examples, PCA is used to reduce multidimensional data sets to lower dimensions for analysis. ICA is typically used to separate multiple components in a signal. Monte Carlo relies on repeated random sampling to compute results. According to various examples, an ERP scenario is developed at 407 to determine a subject, session and task specific response baseline. The baseline can then be used to enhance the sensitivity of other ERP responses to the tested stimuli.

At 421, stimuli can be presented and enhanced measurements determined multiple times to determine the variation or habituation profiles across multiple presentations. Determining the variation and/or habituation profiles provides an enhanced assessment of the primary responses as well as the longevity (wear-out) of the marketing and entertainment stimuli. At 423, the synchronous response of multiple individuals to stimuli presented in concert is measured to determine an enhanced across subject synchrony measure of effectiveness. According to various examples, the synchronous response may be determined for multiple subjects residing in separate locations or for multiple subjects residing in the same location.

A variety of processes such as processes 421, and 423 can be applied to a number of modalities, including EOG, eye tracking, GSR, facial emotion encoding, etc. In addition, synthesis of data from mechanisms such as EOG and eye tracking can also benefit from the grouping objects of interest into temporally and spatially defined entities using micro and macro saccade patterns. Gaze, dwell, return of eye movements to primarily center around the defined entities of interest and inhibition of return to novel regions of the material being evaluated are measured to determine the degree of engagement and attention evoked by the stimulus.

Although intra-modality synthesis mechanisms provide enhanced effectiveness data, additional cross-modality synthesis mechanisms can also be applied. FIG. 5 illustrates a particular example of a cross-modality synthesis mechanism. A variety of mechanisms such as EEG 501, Eye Tracking 503, GSR 505, EOG 507, and facial emotion encoding 509 are connected to a cross-modality synthesis mechanism. Other mechanisms as well as variations and enhancements on existing mechanisms may also be included. According to various examples, data from a specific modality can be enhanced using data from one or more other modalities. In particular examples, EEG typically makes frequency measurements in different bands like alpha, beta and gamma to provide estimates of effectiveness. However, the techniques of the present disclosure recognize that effectiveness measures can be enhanced further using information from other modalities.

For example, facial emotion encoding measures can be used to enhance the valence of the EEG emotional engagement measure. EOG and eye tracking saccadic measures of object entities can be used to enhance the EEG estimates of effectiveness including but not limited to attention, emotional engagement, and memory retention. According to various examples, a cross-modality synthesis mechanism performs time and phase shifting of data to allow data from different modalities to align. In some examples, it is recognized that an EEG response will often occur hundreds of milliseconds before a facial emotion measurement changes. Correlations can be drawn and time and phase shifts made on an individual as well as a group basis. In other examples, saccadic eye movements may be determined as occurring before and after particular EEG responses. According to various examples, time corrected GSR measures are used to scale and enhance the EEG estimates of effectiveness including attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domain difference event-related potential components (like the DERP) in specific regions correlates with subject responsiveness to specific stimulus. According to various examples, ERP measures are enhanced using EEG time-frequency measures (ERPSP) in response to the presentation of the marketing and entertainment stimuli. Specific portions are extracted and isolated to identify ERP, DERP and ERPSP analyses to perform. In particular examples, an EEG frequency estimation of attention, emotion and memory retention (ERPSP) is used as a co-factor in enhancing the ERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specific objects of stimulus. Eye tracking measures the subject's gaze path, location and dwell on specific objects of stimulus. According to various examples, EOG and eye tracking is enhanced by measuring the presence of lambda waves (a neurophysiological index of saccade effectiveness) in the ongoing EEG in the occipital and extra striate regions, triggered by the slope of saccade-onset to estimate the effectiveness of the EOG and eye tracking measures. In particular examples, specific EEG signatures of activity such as slow potential shifts and measures of coherence in time-frequency responses at the Frontal Eye Field (FEF) regions that preceded saccade-onset are measured to enhance the effectiveness of the saccadic activity data.

GSR typically measures the change in general arousal in response to stimulus presented. According to various examples, GSR is enhanced by correlating EEG/ERP responses and the GSR measurement to get an enhanced estimate of subject engagement. The GSR latency baselines are used in constructing a time-corrected GSR response to the stimulus. The time-corrected GSR response is co-factored with the EEG measures to enhance GSR effectiveness measures.

According to various examples, facial emotion encoding uses templates generated by measuring facial muscle positions and movements of individuals expressing various emotions prior to the testing session. These individual specific facial emotion encoding templates are matched with the individual responses to identify subject emotional response. In particular examples, these facial emotion encoding measurements are enhanced by evaluating inter-hemispherical asymmetries in EEG responses in specific frequency bands and measuring frequency band interactions. The techniques of the present disclosure recognize that not only are particular frequency bands significant in EEG responses, but particular frequency bands used for communication between particular areas of the brain are significant. Consequently, these EEG responses enhance the EMG, graphic and video based facial emotion identification.

FIG. 6 is a flow process diagram showing a technique for obtaining neurological and neurophysiological data. At 601, a protocol is generated and stimulus is provided to one or more subjects. According to various examples, stimulus includes streaming video, media clips, printed materials, individual products, etc. The protocol determines the parameters surrounding the presentation of stimulus, such as the number of times shown, the duration of the exposure, sequence of exposure, segments of the stimulus to be shown, etc. Subjects may be isolated during exposure or may be presented materials in a group environment with or without supervision. At 603, subject responses are collected using a variety of modalities, such as EEG, ERP, EOG, GSR, etc. In some examples, verbal and written responses can also be collected and correlated with neurological and neurophysiological responses. At 605, data is passed through a data cleanser to remove noise and artifacts that may make data more difficult to interpret. According to various examples, the data cleanser removes EEG electrical activity associated with blinking and other endogenous/exogenous artifacts.

At 611, intra-modality response synthesis is performed to enhance effectiveness measures. At 613, cross-modality response synthesis is performed to further enhance effectiveness measures. It should be noted that in some particular instances, one type of synthesis may be performed without performing the other type of synthesis. For example, cross-modality response synthesis may be performed with or without intra-modality synthesis. At 615, a composite enhanced effectiveness estimate is provided. At 621, feedback is provided to the protocol generator and presenter device for additional evaluations. This feedback could be provided by the cross-modality response synthesizer or other mechanisms.

According to various examples, various mechanisms such as the data collection mechanisms, the intra-modality synthesis mechanisms, cross-modality synthesis mechanisms, etc. are implemented on multiple devices. However, it is also possible that the various mechanisms be implemented in hardware, firmware, and/or software in a single system. FIG. 7 provides one example of a system that can be used to implement one or more mechanisms. For example, the system shown in FIG. 7 may be used to implement a data cleanser device or a cross-modality responses synthesis device.

According to particular examples, a system 700 suitable for implementing particular examples of the present disclosure includes a processor 701, a memory 703, an interface 711, and a bus 715 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the processor 701 is responsible for such tasks such as pattern generation. Various specially configured devices can also be used in place of a processor 701 or in addition to processor 701. The complete implementation can also be done in custom hardware. The interface 711 is typically configured to send and receive data packets or data segments over a network. Particular examples of interfaces the device supports include host bus adapter (HBA) interfaces, Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such as fast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as data synthesis.

According to particular examples, the system 700 uses memory 703 to store data, algorithms and program instructions. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store received data and process received data.

Because such information and program instructions may be employed to implement the systems/methods described herein, the present disclosure relates to tangible, machine readable media that include program instructions, state information, etc. for performing various operations described herein. Examples of machine-readable media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks and DVDs; magneto-optical media such as optical disks; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM) and random access memory (RAM). Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. Therefore, the present examples are to be considered as illustrative and not restrictive and the disclosure is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.

Claims

1. A system for transforming neuro-response data collected using a first modality of collection and a second modality of collection into a measure of effectiveness of media, the system comprising: an analyzer to: identify a degree of amplitude synchrony between a first pattern in a first frequency band in first neuro-response data and a second pattern in a second frequency band in the first neuro-response data, the first neuro-response data gathered via the first modality of collection from a subject while the subject is exposed to the media; andmodify the degree of amplitude synchrony in response to activity in second neuro-response data, the second neuro-response data gathered via the second modality of collection from the subject while the subject is exposed to the media, the activity corresponding in time to at least a portion of the first pattern or the second pattern; andan estimator to determine an effectiveness of the media based on the modified degree of amplitude synchrony.

2. The system of claim 1, wherein the analyzer is to identify the degree of amplitude synchrony based on a first amplitude in the first pattern relative to a second amplitude in the second pattern.

3. The system of claim 1, wherein the analyzer is to: determine a first change between a first amplitude in the first frequency band and a second amplitude in the second frequency band;determine a second change between a third amplitude in the second frequency band a fourth amplitude in the second frequency band; andidentify the degree of amplitude synchrony based on a coherence between the first change and the second change.

4. The system of claim 1, wherein the analyzer is to further determine a degree of phase synchrony between the first pattern in the first frequency band and the second pattern in the second frequency band and the estimator to is to further determine the effectiveness of the media based on the degree of phase synchrony.

5. The system of claim 1, wherein the first frequency band has a first frequency range and the second frequency band has a second frequency range, the second frequency range different than the first frequency range.

6. The system of claim 1, wherein the first frequency band and the second frequency band include the same frequency range, the first frequency band gathered from a first hemisphere of a brain of the subject and the second frequency band gathered from a second hemisphere of the brain.

7. The system of claim 1, wherein the first modality of collection includes electroencephalography and the second modality of collection includes galvanic skin response.

8. A tangible machine readable storage device or storage disc comprising instructions that, when executed, cause a machine to at least: identify a degree of amplitude synchrony between a first pattern in a first frequency band in first neuro-response data and a second pattern in a second frequency band in the first neuro-response data, the first neuro-response data gathered via a first modality of collection from a subject while the subject is exposed to media;modify the degree of amplitude synchrony in response to activity in second neuro-response data, the second neuro-response data gathered via a second modality of collection from the subject while the subject is exposed to the media, the activity corresponding in time to at least a portion of the first pattern or the second pattern; anddetermine an effectiveness of the media based on the modified degree of amplitude synchrony.

9. The tangible machine readable storage device or storage disk of claim 8, wherein the instructions, when executed, cause the machine to identify the degree of amplitude synchrony based on a first amplitude in the first pattern relative to a second amplitude in the second pattern.

10. The tangible machine readable storage device or storage disk of claim 8, wherein the instructions, when executed, cause the machine to: determine a first change between a first amplitude in the first frequency band and a second amplitude in the second frequency band;determine a second change between a third amplitude in the second frequency band a fourth amplitude in the second frequency band; andidentify the degree of amplitude synchrony based on a coherence between the first change and the second change.

11. The tangible machine readable storage device or storage disk of claim 8, wherein the instructions cause the machine to: determine a degree of phase synchrony between the first pattern in the first frequency band and the second pattern in the second frequency band; anddetermine the effectiveness of the media based the degree of phase synchrony.

12. The tangible machine readable storage device or storage disk of claim 8, wherein the first frequency band has a first frequency range and the second frequency band has a second frequency range, the second frequency range different than the first frequency range.

13. The tangible machine readable storage device or storage disk of claim 8, wherein the first frequency band and the second frequency band include the same frequency range, the first frequency band gathered from a first hemisphere of a brain of the subject and the second frequency band gathered from a second hemisphere of the brain.

14. An apparatus comprising: memory including machine readable instructions; andprocessor circuitry to execute the instructions to: identify a degree of amplitude synchrony between a first pattern in a first frequency band in first neuro-response data and a second pattern in a second frequency band in the first neuro-response data, the first neuro-response data gathered via a first modality of collection from a subject while the subject is exposed to media;modify the degree of amplitude synchrony in response to activity in second neuro-response data, the second neuro-response data gathered via a second modality of collection from the subject while the subject is exposed to the media, the activity corresponding in time to at least a portion of the first pattern or the second pattern; anddetermine an effectiveness of the media based on the modified degree of amplitude synchrony.

15. The apparatus of claim 14, wherein the processor circuitry is to execute the instructions to identify the degree of amplitude synchrony based on a first amplitude in the first pattern relative to a second amplitude in the second pattern.

16. The apparatus of claim 14, wherein the processor circuitry is to execute the instructions to: determine a first change between a first amplitude in the first frequency band and a second amplitude in the second frequency band;determine a second change between a third amplitude in the second frequency band a fourth amplitude in the second frequency band; andidentify the degree of amplitude synchrony based on a coherence between the first change and the second change.

17. The apparatus of claim 14, wherein the processor circuitry is to execute the instructions to: determine a degree of phase synchrony between the first pattern in the first frequency band and the second pattern in the second frequency band; anddetermine the effectiveness of the media based the degree of phase synchrony.

18. The apparatus of claim 14, wherein the first frequency band has a first frequency range and the second frequency band has a second frequency range, the second frequency range different than the first frequency range.

19. The apparatus of claim 14, wherein the first frequency band and the second frequency band include the same frequency range, the first frequency band gathered from a first hemisphere of a brain of the subject and the second frequency band gathered from a second hemisphere of the brain.

20. The apparatus of claim 14, wherein the first modality of collection includes electroencephalography and the second modality of collection includes galvanic skin response.