Patent Publication Number: US-8977110-B2

Title: Methods and apparatus for providing video with embedded media

Description:
RELATED APPLICATIONS 
     This patent arises from a continuation of U.S. patent application Ser. No. 12/357,302, filed Jan. 21, 2009, the entirety of which is hereby incorporated by reference. This patent is related to U.S. patent application Ser. No. 12/357,315 and U.S. patent application Ser. No. 12/357,322. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to providing video with embedded media. 
     BACKGROUND 
     A variety of conventional systems are available for delivering and manipulating video. In some instances, personal video recorders or digital video recorders store video and audio to allow user playback and/or manipulation of the video. A user may fast forward, rewind, skip forward, and/or play video back at varying speeds. In other instances, video discs may hold video for playback and/or manipulation on video disc players. Video disc players may similarly allow a user to fast forward, rewind, skip forward, and/or play video back at varying speeds. Computing systems may also hold video in memory that allows playback and manipulation of the video. 
     Although a variety of video delivery and manipulation mechanisms are available, the ability to embed media in video is limited. Consequently, it is desirable to provide improved methods and apparatus for embedding media in video for user playback and manipulation. 
    
    
     
       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 example embodiments. 
         FIG. 1  illustrates one example of a system for providing video embedded media. 
         FIGS. 2A-K  illustrate examples of different portions of an embedded image and video. 
         FIG. 3  illustrates one example of a series of video frames. 
         FIG. 4  illustrates another example of a series of video frames. 
         FIG. 5  illustrates one example of a system for analyzing video embedded media. 
         FIG. 6  illustrates one example of a technique for embedding media in video. 
         FIG. 7  illustrates one example of technique for performing data analysis for video embedded media. 
         FIG. 8  provides one example of a system that can be used to implement one or more mechanisms. 
     
    
    
     DESCRIPTION OF PARTICULAR EMBODIMENTS 
     Reference will now be made in detail to some specific examples including the best modes contemplated by the inventors for carrying out the invention. Specific examples are illustrated in the accompanying drawings. While specific examples are described below, it will be understood that it is not intended to limit the claimed invention 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 claimed invention as defined by the claims of this patent. 
     For example, techniques and mechanisms are described in the context of embedding media such as images into video. However, it should be noted that the techniques and mechanisms of the claimed invention apply to a variety of different types of media such as video and audio. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the disclosure. Particular examples 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 description. 
     Various techniques and mechanisms are sometimes 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 claimed invention unless otherwise noted. Furthermore, the example techniques and mechanisms described below 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 
     An example system modifies video by embedding portions of media, such as subsets of image pixels, in video frames. When the video is played at normal speed, the media is not discernible. However, when the video is played at an accelerated rate in the forward or reverse direction, the portions of images embedded in video frames coalesce into discernible media. The embedded media may be simple text, images, video, audio, or other media. The system may also evaluate base videos as well as insertion media using neuro-response measurements to determine how and what type of media to embed. The media may be embedded in real-time or near real-time into video for delivery to a user for playback on devices such as digital video recorders, computer systems, software and hardware players, cable boxes, etc. 
     EXAMPLES 
     Conventional mechanisms for embedding media in video are limited or non-existent. In some systems, a frame of video may be replaced in its entirety with a different substitute frame. The single frame may be processed subconsciously by a viewer. However, replaced frames are generally not looked upon positively. Furthermore, they may have limited effectiveness and may not be noticed at all, particularly if the video is being viewed in a time accelerated manner. In other systems, video includes watermarking or faint media. However, this media may only be discernible upon close examination. 
     Viewers will often fast forward or rewind video data or playback video data at accelerated rates. Viewers will also often use these mechanisms to skip commercials or portions of content that they do not want to see. As commercial skipping becomes more prevalent, the example techniques recognize that it is useful to provide advertisers, content providers, and service providers with mechanisms for introducing additional discernible content to viewers. In some examples, media can be introduced without any hardware or player modifications. This allows image embedding with minimal investment, as no new equipment is required. 
     In some examples, video frames are modified to include different portions of an image. In some such examples, the different portions may be different subsets of image pixels or different components of an audio image. The different image portions may be blended with surrounding imagery to somewhat match hue, saturation, value and/or other image qualities. When the video is viewed at normal or near normal speeds, the portions of the image and the media itself are not easily discernible. However, when the video is played back at an accelerated speed in either the forward or reverse direction, the different portions of an image coalesce to form a discernible image or video. In one example, different segments of a line are embedded onto consecutive frames of video. When the frames are played back at 4×, 8×, or 60× speed, the segments combine to create a discernible line. In another example, a subset of the pixels in a company logo is embedded in the frames of an advertisement. In some examples, when the video is played at normal speeds, the logo is not discernible. However, when the video is played at accelerated speeds, the different subsets of pixels in the different frames combine to form a discernible company logo. In some examples, the company logo is shown when a user fast forwards through a commercial. 
     In still other examples, a video stream is embedded in video content so that the video stream may be viewed when playback is accelerated. The video stream may be used to enhance a viewing experience, provide alternative messages, commercial messages, additional information, or unrelated information altogether. In some examples, the embedded media may be text providing location information or a summary about a portion of show being fast forwarded. Instead of watching a 10 minute scene, a viewer may read a summary of the scene or see a title of the scene while fast forwarding through the 10 minute scene at 8× speed. The summary or title would not be discernible when the scene is played at normal speed. In some examples, a discernibility threshold is determined to evaluate whether media is discernible. In some such examples, survey data is used to determine a discernibility threshold. In other examples, neuro-response data is used to determine the discernibility threshold. In some examples, a discernibility threshold is reached when 90% of test subjects notice the embedded media. In other examples, a discernibility threshold is reached when 95% of test subjects have neuro-response data indicating a salient feature at the time embedded media is shown during accelerated playback. 
     In some examples, it may be difficult to effectively introduce media into video so that the media is discernible primarily only when play back occurs at an accelerated rate. Consequently, the example techniques and mechanisms also optionally provide a neuro-response analyzer to determine the effectiveness of embedded media. The system may also determine what type of media to embed and how to embed the media. The system may also analyze the effectiveness of the resulting video. The video with embedded media may be played on a variety of devices such as digital video recorders, software players, cable boxes, hardware players, etc. Although media may be embedded, in some examples, media may be hidden in a video stream and played when a decoder receives a fast forward action. 
       FIG. 1  illustrates one example of a system for embedding images in a video. Although one particular example of embedding images is illustrated, it should be noted that a variety of media types such as audio, changing images, logos, and video can be embedded. Although insertion media is described as being embedded in a base video, in other examples, a base video can also be embedded onto the insertion media. In some examples, the base video may be streaming, file-based, analog, digital, real-time, time-delayed, etc. In some such examples, a video library  111  provides video to a video decoder  113 . In some instances, video may not require decoding. In other examples, video may need to be decompressed and expressed as sequential frames. The system may include a database  121  for images. In some examples, the database may be a media database that provides media including text, data, logos, pictures, images, and video to an image portion generator  123 . The image portion generator  123  selects portions of the imagery for inclusion in video frames. In some examples, the image portion generator  123  randomly selects subsets of pixels of the image for inclusion in sequential frames. In some such examples, the image portion generator  123  intelligently selects subsets of pixels of the image for inclusion in sequential frames. 
     In some examples, the image portion generator  123  may be connected to a video decoder  113  to obtain information about the video itself. The image portions and video frames are passed to a combined image and video blender  131 . The combined image and video blender  131  melds the image portions onto the video In some examples, boundaries and colors between the image portions and video are blended. The combined image and video blender may also identify particular locations in frames for embedding the image. In some examples, images are embedded in portions of video that are relatively static and uniform, such as a part of a video frame showing a blue sky or a blank wall. Image portions may be made more transparent, blurred, or generated with lower contrast colors before embedding them on the video to make the image portions less visible during regular playback. In other examples, images may be outlined more clearly, made more opaque, or generated with higher contrast colors before embedding them on video to make the images more discernible during accelerated playback. In some examples, survey based and/or neuro-response analysis is used to determine the optimal combination or clarity, opacity, and contrast. In other examples, neuro-response analysis is used to determine the optimal combination of hue, saturation, and value for various pixels in the image and image portions. 
     Video frames embedded with image portions are then passed to video encoder  133 . In some examples, no video encoding is required. The video with embedded imagery is then stored in a video with embedded imagery library  135 . In some such examples, the video is transmitted in real-time to consumers without any storage mechanism. 
       FIG. 2A  illustrates one example of an image that can be embedded onto video. In some examples, the image is a letter “A” in pixel form.  FIG. 2B  shows one frame of a video of a ball rolling down a ramp against a night time sky.  FIGS. 2C-2F  illustrate portions of an image of the letter “A”. In some examples, a subset of pixels of the image are selected for embedding on each frame. When the frames are viewed at normal speed, no image is discernible. However, when the frames are played at accelerated speeds, the pixels coalesce to form an image.  FIGS. 2G-2J  show video frames with embedded image portions.  FIGS. 2G-2J  include embedded images in  FIGS. 2C-2F  respectively.  FIG. 2K  shows a full image of the letter “A” embedded on a frame in  2 K. In some examples, the full image of the letter “A” is what is discernible when the frames are played at an accelerated rate. 
       FIG. 3  illustrates one example of a sequence of frames. Video includes frames  311 ,  313 ,  315 ,  317 ,  319 ,  321 ,  323 ,  325 ,  327 ,  329 ,  331 , and  333 . Image portions  301 ,  303 ,  305 , and  307  are provided for inclusion in video frames. In some examples, image portion  301  is included in frame  313 , image portion  303  is included in frame  315 , image portion  305  is included in frame  317 , and image portion  307  is included in frame  319 . In some such examples, image portions are included in sequential frames. However, in many instances, not every frame needs to have embedded image portions. In some examples, multiple frames in a sequence include the same image portion. 
       FIG. 4  illustrates another example of a sequence of frames. Many video encoding mechanisms include different types of frames. In some examples, frames include intra-coded frames (I-frames), predicted frames (P-frames), and bi-predictive frames (B-frames). I-frames provide substantially all of the data needed to present a full picture. On the other hand, P-frames and B-frames provide information about differences between the predictive frame and an I-frame. Predictive frames such as B-frames and P-frames are smaller and more bandwidth efficient than I-frames. In some examples, the techniques modify only I-frames. In some such examples, only I-frames are embedded with media portions. 
     In some examples, frames sequences  411 ,  413 ,  415 ,  417 ,  419 ,  421 ,  423 ,  425 ,  427 ,  429 ,  431 , and  433  include I-frames  411 ,  419 ,  425 , and  433 . The frame sequence also includes predictive frames including P-frames  413 ,  417 ,  421 ,  423 , and  427  as well as B-frames  415 ,  429 , and  431 . In some such examples, image portions are embedded on I-frames. Pixel subsets are shown as examples of portions of an image A. Image portion  401  is blended with I-frame  411 , image portion  403  is blended with I-frame  419 , image portion  405  is blended with I-frame  425 , and image portion  407  is blended with I-frame  433 . 
     A variety of survey based and neuro-response based mechanisms can be used to determine the effectiveness of embedding media into video. Using feedback from survey based and/or neuro-response based mechanisms can allow adjustment of the type of media, the saliency of the media in the video, the location of the media, duration and size of the media, and dynamism of the media. For example, survey based and/or neuro-response mechanisms may determine that media is hardly noticed by a majority of viewers even when the video is played at a variety of accelerated speeds. In some such examples, the media contrast and size may be increased. In other examples, survey based and/or neuro-response mechanisms may indicate that media is noticeable and distracting even during playback at regular speeds. Additional blending mechanisms may be applied to an image to reduce the noticeability of an image during regular playback. The position or size of the image may also be adjusted. 
       FIG. 5  illustrates one example of a system for evaluating imagery embedded in video using central nervous system, autonomic nervous system, and/or effector measures. In some examples, the video embedded imagery system includes a stimulus presentation device  501 . In some such examples, the stimulus presentation device  501  is merely a display, monitor, screen, etc., that displays stimulus material to a user. The stimulus material may be videos with embedded media or the media itself. Continuous and discrete modes are supported. In some examples, the stimulus presentation device  501  also has protocol generation capability to allow intelligent customization of stimuli provided to multiple subjects in different markets. 
     In some examples, stimulus presentation device  501  could include devices such as televisions, cable consoles, computers and monitors, projection systems, display devices, speakers, tactile surfaces, etc., for presenting the video from different networks, local networks, cable channels, syndicated sources, websites, internet content aggregators, portals, service providers, etc. 
     In some examples, the subjects  503  are connected to data collection devices  505 . The data collection devices  505  may include a variety of neuro-response measurement mechanisms including neurological and neurophysiological measurements systems. In some examples, neuro-response data includes central nervous system, autonomic nervous system, and effector data. 
     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. 
     In some examples, the techniques and mechanisms intelligently blend multiple modes and manifestations of precognitive neural signatures with cognitive neural signatures and post cognitive neurophysiological manifestations to more accurately allow assessment of embedded imagery in video. 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. In some examples, central nervous system, autonomic nervous system, and effector system measurements are aggregated into a measurement that allows definitive evaluation stimulus material. 
     In some examples, the data collection devices  505  include EEG  511 , EOG  513 , and GSR  515 . In some instances, only a single data collection device is used. Data collection may proceed with or without human supervision. 
     The data collection device  505  collects neuro-response 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 some examples, data collected is digitally sampled and stored for later analysis. In some examples, the data collected could be analyzed in real-time. In some examples, the digital sampling rates are adaptively chosen based on the neurophysiological and neurological data being measured. 
     In some examples, the video embedded imagery system includes EEG  511  measurements made using scalp level electrodes, EOG  513  measurements made using shielded electrodes to track eye data, GSR  515  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 some examples, the data collection devices are clock synchronized with a stimulus presentation device  501 . In some such examples, the data collection devices  505  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. In some examples, the data collection devices include mechanisms for not only monitoring subject neuro-response to stimulus materials, but also include mechanisms for identifying and monitoring the stimulus materials. For example, data collection devices  505  may be synchronized with a set-top box to monitor channel changes. In other examples, data collection devices  505  may be directionally synchronized to monitor when a subject is no longer paying attention to stimulus material. In still other examples, the data collection devices  505  may receive and store stimulus material generally being viewed by the subject, whether the stimulus is a program, a commercial, printed material, or a scene outside a window. The data collected allows analysis of neuro-response information and correlation of the information to actual stimulus material and not mere subject distractions. 
     In some examples, the video embedded imagery system also includes a data cleanser and analyzer device  521 . In some such examples, the data cleanser and analyzer device  521  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, e.g. a phone ringing while a subject is viewing a video) and endogenous artifacts (where the source could be neurophysiological, e.g. muscle movements, 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). 
     In some examples, the data cleanser and analyzer device  521  is implemented using hardware, firmware, and/or software. 
     The data analyzer portion uses a variety of mechanisms to analyze underlying data in the system to determine resonance. In some examples, the data analyzer customizes and extracts the independent neurological and neuro-physiological parameters for each individual in each modality, and blends the estimates within a modality as well as across modalities to elicit an enhanced response to the presented stimulus material. In some such examples, the data analyzer aggregates the response measures across subjects in a dataset. 
     In some examples, neurological and neuro-physiological signatures are measured using time domain analyses and frequency domain analyses. Such analyses use parameters that are common across individuals as well as parameters that are unique to each individual. The analyses could also include statistical parameter extraction and fuzzy logic based attribute estimation from both the time and frequency components of the synthesized response. 
     In some examples, statistical parameters used in a blended effectiveness estimate include evaluations of skew, peaks, first and second moments, distribution, as well as fuzzy estimates of attention, emotional engagement and memory retention responses. 
     In some examples, the data analyzer may include an intra-modality response synthesizer and a cross-modality response synthesizer. In some such examples, 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 some examples, the intra-modality response synthesizer also aggregates data from different subjects in a dataset. 
     In some examples, the cross-modality response synthesizer or fusion device blends different intra-modality responses, including raw signals and signals output. The combination of signals enhances the measures of effectiveness within a modality. The cross-modality response fusion device can also aggregate data from different subjects in a dataset. 
     In some examples, the data analyzer 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. In some such examples, blended estimates are provided for each exposure of a subject to stimulus materials. The blended estimates are evaluated over time to assess resonance characteristics. In some examples, numerical values are assigned to each blended estimate. The numerical values may correspond to the intensity of neuro-response measurements, the significance of peaks, the change between peaks, etc. Higher numerical values may correspond to higher significance in neuro-response intensity. Lower numerical values may correspond to lower significance or even insignificant neuro-response activity. In other examples, multiple values are assigned to each blended estimate. In still other examples, blended estimates of neuro-response significance are graphically represented to show changes after repeated exposure. 
     In some examples, a data analyzer passes data to a resonance estimator that assesses and extracts resonance patterns. In some such examples, the resonance estimator determines entity positions in various stimulus segments and matches position information with eye tracking paths while correlating saccades with neural assessments of attention, memory retention, and emotional engagement. In some examples, the resonance estimator stores data in the priming repository system. As with a variety of the components in the system, various repositories can be co-located with the rest of the system and the user, or could be implemented in remote locations. 
       FIG. 6  illustrates an example of a technique for providing video with embedded media such as imagery. In some examples, static or changing imagery is embedded in video so that the image is only discernible when the video is being viewed at an accelerated rate such as during fast forward or rewind. At  601 , an image is received. The selected image may be text, graphics, or other data. In some instances, multiple images can be selected for a single video. The multiple images may also be selected frames of another video. At  603 , the image is divided into portions. In some examples, a subset of image pixels are selected for each image portion, so that a sequence of image portions would include substantially all of the image pixels. In some such examples, the number of subsets of image pixels generated is equal to the standard accelerated fast forward or rewind rate. For example, if the standard fast forward rate is 4×, four subsets of image pixels are generated where each subset includes approximately one quarter of the total image pixels. In another example, eight subsets of image pixels are generated where each subset include approximately one sixth of the total image pixels. Survey based and neuro-response based feedback can be used to select the number of subsets generated and the percentage of total image pixels to include in each subset. 
     At  605 , video is received and decoded. The video may include intra-coded frames as well as predictive frames. In other examples, the video is analog video and may not require decoding. At  609 , video frames are blended with image portions. In some examples, only intra-coded frames are blended with image portions and predictive frames remain unchanged. Hue, saturation, and value, etc. of image portion pixels may be blended with surrounding video pixels. Value may be associated with brightness and intensity or contrast. In some examples, hue relates to different dominant wavelengths of light, such as red, purple, blue, etc. 
     The way a viewer perceives color may also vary along other dimensions. One of the dimensions is value, or lightness and darkness. In terms of a spectral definition of color, value describes the overall intensity or strength of the light. Another dimension is saturation. Saturation refers to the dominance of hue in the color. Desaturated colors constitute different scales of gray, running from white to black. Individual pixels in the plurality of image portions may be adjusted for hue, saturation, and value in order to blend the image effectively. At  611 , video may be encoded. 
     The size, type, location of images as well as the amount of blending to use can be determined for particular images and video using survey based and neuro-response based feedback. 
       FIG. 7  illustrates one example of using neuro-response based feedback for providing video embedded media. At  701 , stimulus material is provided to multiple subjects. In some examples, stimulus includes streaming video and audio. In some such examples, subjects view stimulus in their own homes in group or individual settings. In some examples, verbal and written responses are collected for use without neuro-response measurements. In other examples, verbal and written responses are correlated with neuro-response measurements. At  703 , subject neuro-response measurements are collected using a variety of modalities, such as EEG, ERP, EOG, etc. At  705 , data is passed through a data cleanser to remove noise and artifacts that may make data more difficult to interpret. In some examples, the data cleanser removes EEG electrical activity associated with blinking and other endogenous/exogenous artifacts. 
     In some examples, data analysis is performed. Data analysis may include intra-modality response synthesis and cross-modality response synthesis to enhance effectiveness measures. It should be noted that in some particular instances, one type of synthesis may be performed without performing other types of synthesis. For example, cross-modality response synthesis may be performed with or without intra-modality synthesis. 
     A variety of mechanisms can be used to perform data analysis. In some such examples, a stimulus attributes repository is accessed to obtain attributes and characteristics of the stimulus materials, along with purposes, intents, objectives, etc. In some examples, EEG response data is synthesized to provide an enhanced assessment of effectiveness. In some 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. In some examples, brainwave frequencies include 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 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 some examples, EEG measurements including difficult to detect high gamma or kappa band measurements are obtained, enhanced, and evaluated. Subject and task specific signature sub-bands in the theta, alpha, beta, gamma and kappa bands are identified to provide enhanced response estimates. In some examples, high gamma waves (kappa-band) above 80 Hz (typically detectable with sub-cranial EEG and/or magnetoencephalograophy) can be used in inverse model-based enhancement of the frequency responses to the stimuli. 
     Various examples 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 embodiments, multiple sub-bands within the different bands are selected while remaining frequencies are band pass filtered. In particular embodiments, multiple sub-band responses may be enhanced, while the remaining frequency responses may be attenuated. 
     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. Stimuli can be presented and enhanced measurements determined multiple times to determine the variation profiles across multiple presentations. Determining various profiles provides an enhanced assessment of the primary responses as well as the longevity (wear-out) of the marketing and entertainment stimuli. The synchronous response of multiple individuals to stimuli presented in concert is measured to determine an enhanced across subject synchrony measure of effectiveness. In some 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. 
     Although intra-modality synthesis mechanisms provide enhanced significance data, additional cross-modality synthesis mechanisms can also be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR, EOG, and facial emotion encoding are connected to a cross-modality synthesis mechanism. Other mechanisms as well as variations and enhancements on existing mechanisms may also be included. In some examples, data from a specific modality can be enhanced using data from one or more other modalities. In particular embodiments, EEG typically makes frequency measurements in different bands like alpha, beta and gamma to provide estimates of significance. However, the techniques recognize that significance 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 significance including but not limited to attention, emotional engagement, and memory retention. In some 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. In some examples, time corrected GSR measures are used to scale and enhance the EEG estimates of significance 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. In some 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 embodiments, 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&#39;s gaze path, location and dwell on specific objects of stimulus. In some 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 significance of the EOG and eye tracking measures. In particular embodiments, 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. In some 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 significance measures. 
     In some 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 embodiments, 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 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. 
     In some examples, post-stimulus versus pre-stimulus differential measurements of ERP time domain components in multiple regions of the brain (DERP) are measured at  707 . The differential measures give a mechanism for eliciting responses attributable to the stimulus. For example the messaging response attributable to an advertisement or the brand response attributable to multiple brands is determined using pre-resonance and post-resonance estimates. 
     At  709 , target versus distracter stimulus differential responses are determined for different regions of the brain (DERP). At  711 , event related time-frequency analysis of the differential response (DERPSPs) are used to assess the attention, emotion and memory retention measures across multiple frequency bands. In some examples, the multiple frequency bands include theta, alpha, beta, gamma and high gamma or kappa. At  713 , priming levels and resonance for various products, services, and offerings are determined at different locations in the stimulus material. In some examples, priming levels and resonance are manually determined. In other examples, priming levels and resonance are automatically determined using neuro-response measurements. In some examples, video streams are modified with different inserted advertisement images for various products and services to determine the effectiveness of the inserted advertisement images based on priming levels and resonance of the source material. 
     At  717 , multiple trials are performed to enhance priming and resonance measures. In some examples, stimulus. In some examples, multiple trials are performed to enhance resonance measures. 
     In particular examples, the priming and resonance measures are sent to a priming repository  719 . The priming repository  719  may be used to automatically select images for insertion into video. 
     In some 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. 8  provides one example of a system that can be used to implement one or more mechanisms. For example, the system shown in  FIG. 8  may be used to implement an video embedded imagery system. 
     According to particular examples, a system  800  includes a processor  801 , a memory  803 , an interface  811 , and a bus  815  (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the processor  801  is responsible for such tasks such as pattern generation. Various specially configured devices can also be used in place of a processor  801  or in addition to processor  801 . The complete implementation can also be done in custom hardware. The interface  811  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. 
     According to particular examples, the system  800  uses memory  803  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 invention 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 examples have 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 claims of this patent. Therefore, the described examples are to be considered as illustrative and not restrictive and the claimed invention is not limited to the details given herein. The claims of this patent are to be given their full scope including equivalents.