Correlating intentional and unintentional changes in brain states with brain signals

Abstract

To provide methods of correlating a brain signal to intentional and unintentional changes in a brain state.SOLUTION: Disclosed herein are methods of analysis to extract and assess brain data collected from subject animals, including humans, to detect intentional and unintentional brain activity and other unexpected signals. These signals are correlated with higher cognitive brain functions or unintended, potentially adverse events, such as a stroke or seizure, and with translation of those signals into defined trigger events or tasks.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] cross reference This application claims priority to U.S. Provisional Patent Application No. 61 / 590,235, filed January 24, 2012, and U.S. Patent Application No. 13 / 749,619, filed January 24, 2013, the contents of which are incorporated herein in their entireties.

[0002] FIELD OF THE INVENTION The present invention relates to analytical methods for extracting and evaluating brain data collected from animal subjects, including humans, to detect intentional as well as unintentional and other unexpected brain signals that are associated with higher brain cognitive functions or potentially unintentional adverse events, such as stroke or seizure, and to the transformation of these signals into defined trigger events or tasks. More specifically, the present invention relates to the acquisition of physiological data from EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or other signals acquired from peripheral channels that are modulated by or modulate a subject's brain activity. [Background technology]

[0003] Background of the Invention An electroencephalogram (EEG) is a tool used to measure electrical activity produced by the brain. Functional brain activity is collected by electrodes placed on the scalp. EEG has traditionally provided important information about a patient's brain function. Scalp EEG is thought to measure the collection of currents present at postsynapses in the extracellular space, resulting from the flow of ions to and from dendrites connected by neurotransmitters. Thus, while EEG and similar modalities are primarily used in neurology as diagnostic tools for epilepsy, the technique can be used to study other pathological conditions, including sleep disorders.

[0004] Recent advances in EEG and other signal detection have enabled real-time automated detection of sleep and wake states through normalization and other manipulation of brain activity data. Furthermore, such applications and methods can be used to automatically assess disease states and the effects of drug treatments. Related technology has made it possible to access such data in real time using single-channel detectors. This, in turn, has provided opportunities for further scrutiny of sleep and wake states, including clarifying the distinction between REM and deep sleep. To aid in the efficient collection of such data, head-and-harness systems using single-channel wireless data transmission have been developed. See, e.g., International Application No. 2006 / 018120 (Patent Document 1); International Application No. 2009 / 064632 (Patent Document 2); International Application No. 2010 / 054346 (Patent Document 3); U.S. Patent Application No. 8,073,574 (Patent Document 4); and Low, Philip Steven (2007). "A new way to look at sleep: separation and convergence." Published Thesis, University of California San Diego Electronic Theses and Dissertations (Identified: b6635681). To date, this technology has been applied primarily to sleep-related diagnostic applications and the effects of pathological conditions and drug treatments.

[0005] Development continues in the area of ​​exoskeletons and related prosthetics, holding promise for enabling paraplegics to walk again and perform other tasks currently incapable. Furthermore, such devices may also be useful for able-bodied individuals, such as soldiers in the field, first responders, and construction workers. Companies such as Esko Bionics, Parker Hannifan, and Argo Medical Technologies are consistently advancing such technology. See, for example, U.S. Patent No. 8,096,965, International Application Publication No. 2010101595, and U.S. Patent Application No. 11 / 600,291, filed November 15, 2006. Furthermore, other devices are being utilized to enable individuals severely impaired by ALS, MS, and the like to communicate using voice synthesizers and the like. Such devices are typically activated by movement of the cheek muscles, eye movements using an eye tracker, or the like.

[0006] Therefore, there is a need for non-invasive methods to detect intentional and unintentional communication from subjects, including individuals with disabilities, in order to assess and possibly respond to or prepare for the physiological implications of these changes in brain state. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] International Application No. 2006 / 018120 [Patent Document 2] International Application No. 2009 / 064632 [Patent Document 3] International Application No. 2010 / 054346 [Patent Document 4] U.S. Patent Application No. 8,073,574 [Patent Document 5] U.S. Patent No. 8,096,965 [Patent Document 6] International Application Publication No. 2010101595 [Patent Document 7] U.S. Patent Application No. 11 / 600,291 [Non-patent literature]

[0008] [Non-Patent Document 1] Low, Philip Steven (2007). "A new way to look at sleep: separation and convergence". Published Thesis, University of California San Diego Electronic Theses and Dissertations (Identified: b6635681) Summary of the Invention

[0009] The present invention provides methods for noninvasively detecting intentional and unintentional communications from healthy and diseased subjects (including individuals impaired by neurological disorders such as ALS, MS, etc.) in the form of physiological data, e.g., from EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, etc., and for correlating these intentional and unintentional signals with changes in brain state, including higher cognitive functions. There is also a need to utilize such intentional communications, for example, to simulate speech or power prosthetic devices. There is also a need to access unintentional signals from subjects with pathological conditions, such as epilepsy (or physical illnesses that cause changes in brain activity), for correlation with the pathological condition and, optionally, for use in triggering alarms and / or intervening to alter, suppress, or prepare for unintentional events.

[0010] In a preferred method of the present invention, intentional brain signals from a subject are detected by attaching at least one sensor to the subject, acquiring data indicative of brain activity, analyzing the data indicative of brain activity, and correlating the analyzed data with intentional higher-order cognitive functions from the subject. Preferably, the data is acquired non-invasively by applying at least one sensor to the subject, more preferably by applying at least one dry sensor or at least one wet sensor. Furthermore, the data is preferably received from at least one channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel modulated by the subject's intention. In an alternative embodiment, the data is received through a multi-channel detector. Furthermore, the data is preferably communicated and received wirelessly.

[0011] In another preferred embodiment, the data is analyzed by normalizing a spectrogram of the data (including a normalized spectrogram) by time across frequency at least once, and normalizing a spectrogram of the same data (including a normalized spectrogram) by frequency across time at least once, where both normalizations can be performed in either order or repeatedly. In a further preferred embodiment, the data is analyzed by calculating a spectrogram of the data, normalizing the spectrogram, performing independent component analysis or principal component analysis of the normalized spectrogram, and identifying clusters. Furthermore, the analyzing step may also include performing temporal fragmentation analysis, preferred frequency analysis, repeated (preferably two or more) preferred frequency analysis, and / or spectral fragmentation analysis.

[0012] In particularly preferred embodiments, the methods of the present invention further comprise transforming the analyzed data to perform tasks associated with higher cognitive functions, including, but not limited to, intention, speech, recollection, thinking, imagination, and planning (including, but not limited to, movement). Importantly, tasks resulting from transforming the analyzed data include simulating speech on a display, simulating speech with a speech synthesizer, or moving an artificial prosthesis or exoskeleton, or the like.

[0013] In yet another preferred embodiment of the method of the present invention, brain signals from the subject are associated with at least one unintentional event by attaching at least one sensor to the subject, acquiring data indicative of electroencephalographic activity, analyzing the data indicative of brain activity, and associating the analyzed data with at least one unintentional event. In a further embodiment, after associating the data with the unintentional event, an alarm is activated. Alternatively, after associating the data with the unintentional event, a response to ameliorate the effects of the unintentional event may be initiated, which may include modifying, suppressing, or preparing for the unintentional event (and may also activate an alarm). This response may be particularly effective in the case of an unintentional event, a high fragmentation event, a change in fragmentation of the event, startle, tremor, convulsion, injury, or a pathological condition, including, but not limited to, an epileptic seizure, migraine, stroke, heart attack, or infarction.

[0014] In another preferred embodiment, the method of the present invention detects an intentional signal from a subject by attaching to the subject at least one detector capable of detecting the intentional signal, acquiring data indicative of the detected activity using EEG, EMG, EOG, MEG, ECG, ECoG, iEEG, LFP, fMRI, or a peripheral channel modulated by the intentional signal from the subject, analyzing the data indicative of the detected activity, and correlating the analyzed data with an intentional higher-order cognitive function from the subject. It is contemplated that any method, system, or information described herein can be implemented with respect to any other method, system, or information described herein.

[0015] Unless otherwise defined, all terms in this specification have the same meaning as commonly understood by those skilled in the art to which this invention belongs.Methods and materials are described herein for use in the present invention, and other suitable methods and materials known in the art can also be used.The materials and methods, as well as the examples, are merely illustrative and are not intended to be limiting.All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety.In the case of conflict, the present specification, including definitions, will control.

[0016] These and other embodiments of the present invention will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following description, while indicating various embodiments of the present invention and many specific details thereof, is given by way of illustration and not limitation. Many substitutions, modifications, additions, and / or alterations may be made within the scope of the present invention without departing from the spirit thereof, and the present invention includes all such substitutions, modifications, additions, and / or alterations. [The present invention 1001] 1. A method for detecting intentional brain signals from a subject, comprising: (a) attaching at least one sensor to the object; (b) obtaining data indicative of brain activity; (c) analyzing the data indicative of brain activity; and (d) correlating the analyzed data with an intentional higher-order cognitive function from the subject. [The present invention 1002] 10. The method of claim 10, wherein said acquired data is received non-invasively by applying said at least one sensor to said subject. [The present invention 1003] 1002. A method according to claim 1002, wherein said at least one sensor is selected from the group consisting of a single dry sensor or a single wet sensor. [The present invention 1004] The method of the present invention 1002, wherein the acquired data is received from at least one channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel modulated by the subject's intention. [The present invention 1005] The method of claim 1004, wherein said acquired data is received from at least a single channel of an EEG. [The present invention 1006] The method of the present invention 1005, wherein the acquired data is received from a multi-channel detector including at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel modulated by the subject's intent. [The present invention 1007] The method of the present invention 1002, wherein the acquired data is received wirelessly. [The present invention 1008] The analyzing step comprises: (a) normalizing a spectrogram of said data at least once in time across frequency, including a normalized spectrogram; and (b) normalizing the spectrograms of the same data at least once in frequency over time, including the normalized spectrograms. The method of the present invention 1001, comprising: [The present invention 1009] The analyzing step comprises: (a) calculating the spectrogram of the data; (b) normalizing the spectrogram one or more times; and (c) performing principal component analysis and / or independent component analysis of the normalized spectrogram. The method of the present invention 1008, comprising: [The present invention 1010] 1001. A method according to claim 1001, wherein said analyzing step comprises performing a temporal fragmentation analysis. [The present invention 1011] 1001. A method according to claim 1001, wherein said analyzing step comprises performing a preferred frequency analysis. [The present invention 1012] 1012. A method according to claim 1011, wherein said analysis of preferred frequencies is performed on a spectrogram that has been normalized at least twice. [The present invention 1013] 1001. A method according to claim 1001, wherein said analyzing step comprises performing a spectral fragmentation analysis. [The present invention 1014] The method of claim 1001, further comprising, after said correlating step, transforming said analyzed data to perform a task associated with higher cognitive function. [The present invention 1015] 1001. The method of claim 1001, wherein said higher cognitive function is selected from the group consisting of intention, speech, recollection, planned action, thinking, and imagination. [The present invention 1016] 1012. The method of claim 1011, wherein said task is selected from the group consisting of simulating speech on a display, simulating speech with a speech synthesizer, and moving an artificial prosthesis. [The present invention 1017] 1. A method for detecting a brain signal associated with at least one unintentional event from a subject, comprising: (a) attaching at least one sensor to the object; (b) obtaining data indicative of electroencephalographic activity; (c) analyzing the data indicative of brain activity; and (d) correlating the analyzed data with at least one unintended event. [The present invention 1018] The method of claim 1017, further comprising the step of activating an alarm after said associating step. [The present invention 1019] The method of claim 1017, further comprising, after said associating step, a step of responding to said unintentional event to ameliorate its effect. [The present invention 1020] The method of claim 1019, wherein said response to said unintended event comprises intervening to alter, suppress, or prepare for said unintended event. [The present invention 1021] The method of the present invention 1017, wherein the unintended event is selected from the group consisting of a high fragmentation event, a change in fragmentation event, startle, tremor, convulsion, injury, or a pathological condition including, but not limited to, an epileptic seizure, migraine, stroke, heart attack, or infarction. [The present invention 1022] The method of claim 1017, wherein the response to the unintentional event enhances or replaces a polygraph test. [The present invention 1023] 1017. A method according to claim 1017, wherein said analyzing step comprises performing a temporal fragmentation analysis. [The present invention 1024] 1017. A method according to claim 1017, wherein said analyzing step comprises performing a preferred frequency analysis. [The present invention 1025] 1024. A method according to claim 1024, wherein said analysis of preferred frequencies is performed on a spectrogram that has been normalized at least twice. [The present invention 1026] 1017. A method according to claim 1017, wherein said analyzing step comprises performing a spectral fragmentation analysis. [The present invention 1027] The analyzing step comprises: (a) normalizing a spectrogram of said data at least once in time across frequency, including a normalized spectrogram; and (b) normalizing the spectrograms of the same data at least once in frequency over time, including the normalized spectrograms. The method of the present invention 1017, comprising: [The present invention 1028] The analyzing step comprises: (a) calculating the spectrogram of the data; (b) normalizing the spectrogram one or more times; and (c) performing principal component analysis and / or independent component analysis of the normalized spectrogram. The method of the present invention 1027, comprising: [The present invention 1029] 1. A method for detecting an intentional signal from a subject, comprising: (a) attaching to said subject at least one detector capable of detecting an intentional signal; (b) acquiring data indicative of the detected activity using EEG, EMG, EOG, MEG, ECG, ECoG, iEEG, LFP, fMRI, or a peripheral channel modulated by the subject's intent; (c) analyzing the data indicative of the detected activity; and (d) correlating the analyzed data with an intentional higher-order cognitive function from the subject. [Brief explanation of the drawings]

[0017] So that the invention may be clearly understood and readily practiced, the invention will now be described in conjunction with the following drawings, in which like reference numerals indicate the same or similar elements, and in which:

[0018] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication containing color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0019] [Figure 1-1] 1 is a series of graphs showing examples of brain signal analysis. The left column shows a conventional analysis and the right column shows an analysis using the method of the present invention for closing the eyes (A), clenching the left hand (B), clenching the right hand (C), clenching the left foot (D), and clenching the right foot (E). The last two graphs (F) show the conventional analysis in the left column and the analysis using the method of the present invention in the right column for subjects in a resting state. [Figure 1-2] This is a graph showing a continuation of Figure 1-1. [Figure 2] A-E show data for the same task shown in Fig. 1A-E (without the resting state in Fig. 1F) analyzed by the method of the present invention. The green line indicates the timing of the verbal cue to start the 2- or 4-second task, and the red line indicates the timing of the verbal cue to stop the task and relax for 10 seconds. [Figure 3] A-E are data for the same task shown in Figure 1A-E (without the resting state in Figure 1F) analyzed using temporal fragmentation using the method of the present invention. The green line indicates the timing of the verbal cue to begin the 2- or 4-second task, and the red line indicates the time of the verbal cue to stop the task and relax for 10 seconds. [Figure 4] A-E are data for the same task shown in Figure 1A-E, analyzed using one or more normalizations (without the resting state in Figure 1F). Specifically, the graphs plot summed high-frequency power in the gamma and very high gamma (hgamma) ranges. The green lines indicate the timing of the verbal cue to begin the 2- or 4-second task, and the red lines indicate the time of the verbal cue to stop the task and relax for 10 seconds. [Figure 5]A-E are data for the same tasks shown in Figure 1A-E, analyzed using one or more normalizations (without the resting state in Figure 1F). Specifically, the graphs plot total alpha frequency power against total gamma frequency power for Figure 5A-D, while delta frequency was used for Figure 5F (eyes closed). The green line indicates the timing of the verbal cue to begin the 2- or 4-second task, and the red line indicates the time of the verbal cue to stop the task and relax for 10 seconds. [Figure 6] 1 is a flow chart illustrating the application of the method of the present invention to an intentional signal. [Figure 7] 1 is a flow chart illustrating the application of the method of the present invention to unintentional signals, for example in epilepsy. [Figure 8] Data are analyzed for gamma frequency power (A), alpha frequency power (B), the sum of alpha and gamma frequency power (C), and the ratio of the sum of alpha and very high gamma frequency power using one or more normalizations and alternating between visualizing kicking a football and looking at the bedroom. The green line indicates the timing of the verbal cue to start, and the red line indicates the time of the verbal cue to stop for a 10-second interval. [Figure 9] FIG. 9 is a screenshot of a computer interface using the method of the present invention. [Figure 10] Two-sample Kolmogorov-Smirnov (KS) tests of cross-axially replicated, 10-fold normalized preferred frequency spectrograms from each task listed in Figure 1A–E performed by an immobile ALS subject show that multiple intentional events can be discriminated. Each spectrogram comes from a distribution distinct from that of any other task (though to a lesser extent for this particular test in this subject: left hand grasping and right foot grasping). DETAILED DESCRIPTION OF THE INVENTION

[0020] Detailed Description It will be understood that the drawings and description of the invention have been simplified for purposes of clarity to show elements relevant to a clear understanding of the invention, excluding other elements that are well known. A detailed description is provided herein below with reference to the accompanying drawings.

[0021] A detailed description of one or more embodiments of the present invention is provided below along with accompanying figures that illustrate the principles of the invention. While the present invention will be described in connection with such embodiments, the present invention is not limited to any embodiment. The scope of the present invention is limited only by the claims, and the present invention encompasses numerous alternatives, modifications, and equivalents. Numerous specific details are set forth in the following specification to provide a thorough understanding of the present invention. These details are provided for purposes of example, and the present invention may be practiced according to the claims without some or all of these specific details. For purposes of clarity, technical matters known in the art related to the present invention have not been described in detail so as not to unnecessarily obscure the present invention.

[0022] In this application, the term "subject" refers to both animals and humans. Referring now to FIG. 6, a flowchart 100 illustrating a preferred embodiment of the present method is disclosed. Specifically, the subject imagines and generates intentional brain signals that are received by at least one sensor 101 according to the present invention. Preferably, the sensor includes a single wet electrode or a single dry electrode. The input signals are relayed to a computing device, such as a computer, where they are analyzed (102) to determine whether a defined unintentional event has occurred (103). Preferably, this indicates brain activity from the subject associated with higher cognitive functions. The computer then generates signals (104) that are relayed to one or more receivers 105 in a peripheral device. Examples of peripheral devices may include speech synthesizers, prosthetic devices including exoskeletons, and the like. As shown in FIG. 6, these methods preferably operate in real time and provide a continuous response and feedback loop between the subject's intentional signals and the translation of those signals into commands associated with peripheral devices controlled by the subject's intentions. In a particularly preferred embodiment, data signals from the subject are received from at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel controlled by the subject's intent. In a preferred alternative, data is received through a multi-channel detector wirelessly connecting to a computing device and a peripheral device.

[0023] Referring now to Figure 7, another flowchart 106 is disclosed illustrating an alternative preferred embodiment of the present method. Specifically, a subject is monitored for the presence of an unintentional event (e.g., stroke or seizure) via brain signals received by at least one sensor 107 according to the present invention. Preferably, the sensor includes a single wet electrode or a single dry electrode. The input signals are relayed to a computing device, such as a computer, where they are analyzed 108 to determine whether a defined unintentional event has occurred 109. There are many examples of potentially unintentional adverse events, including, but not limited to, startle, tremors, convulsions, injury, or pathological conditions, including, but not limited to, epileptic seizures, migraines, strokes, heart attacks, or infarctions.

[0024] The computer then generates a signal (110) that is relayed to one or more receivers of a peripheral device. In this example, the peripheral device is a stimulator 111 used to ameliorate the effects of dangerous seizures in epilepsy patients by stimulating a subject's brain region 112. As will be appreciated by those skilled in the art, the peripheral device 111 may also inhibit or not alter (or affect) the subject 112, for example, activating an alarm to alert the subject or a caregiver. In a particularly preferred embodiment, data signals from the subject are received from at least one channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel that is modulated by the unintentional occurrence of the monitored unintentional event. In a preferred alternative, data is received through a multi-channel detector wirelessly connecting the computing device and the peripheral device.

[0025] In another preferred embodiment of the method shown in both Figures 6 and 7, the data is analyzed by normalizing a spectrogram of the data (including the normalized spectrogram) by time across frequency at least once and normalizing a spectrogram of the same data (including the normalized spectrogram) by frequency across time at least once, where both normalizations may be performed in either order or repeatedly. In a further preferred embodiment, the data is analyzed by calculating a spectrogram of the data, normalizing the spectrogram, performing independent component analysis or principal component analysis of the normalized spectrogram, and identifying clusters. Furthermore, the analysis may also include performing temporal fragmentation analysis, preferred frequency analysis, repeated (preferably two or more) preferred frequency analysis, and / or spectral fragmentation analysis.

[0026] In a preferred embodiment, detecting brain signals (e.g., high frequency signals detected from the brain) and correlating the detected signals with higher cognitive functions. Examples of higher cognitive functions include, among others, directed tasks involving intention, speech, recollection, thinking, imagination, and planning (including but not limited to movement), as well as imagination and other cognitive processes and functions.

[0027] In some embodiments, the subject's brain signals are converted into speech. For example, the subject imagines one or more language elements, including, but not limited to, letters, numbers, words, or symbols, and brain signals associated with the language element or elements are detected to determine the language element or elements. In another example, a speech-impaired subject is taught to associate images, symbols, words, letters, or numbers with imagined actions. The subject imagines an imagined action, and brain signals associated with the imagined action are detected to determine the word, letter, or number associated with the imagined action. In one embodiment, the subject's imagined action controls a cursor on a display that selects the image, symbol, word, letter, or number the subject intends to use. In some embodiments, the determined image, symbol, word, letter, or number is used with one or more other determined images, symbols, words, letters, or numbers to form grammatically correct speech. In some embodiments, the determined image, symbol, word, letter, or number is communicated and / or displayed using a speech synthesizer. In some embodiments, the brain signal and / or output is assigned a non-verbal value, including but not limited to a tone, a series of tones, a minute pitch, a color, an image, an electrical stimulus, or a one-dimensional or multi-dimensional graphic. In some embodiments, the brain signal and / or output is assigned to a brain signal. In some embodiments, the brain signal follows, precedes, and / or occurs concurrently with one or more endogenous and / or exogenous events and / or conditions (including but not limited to pathological and / or abnormal events and / or conditions).

[0028] In some embodiments, the subject's brain signals are converted into movement of the prosthetic device, e.g., detected brain signals associated with imagined movements are used at least in part to control the prosthetic device.

[0029] In some embodiments, one or more physiological recordings, including but not limited to EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or peripheral channels modulated by a subject's intention, or reading unintentional signals related to unintentional events, are used to detect one or more brain signals. For example, a single-channel iBrain® EEG recording is performed on a highly functional 70-year-old ALS patient attempting to move one of his limbs following a verbal cue (left and right hand and foot). To detect brain signals, the EEG signal is analyzed with algorithms, including the SPEARS algorithm. Simultaneous video recordings may also be acquired. During the movement attempt, the subject's brain activity exhibits broad-spectrum pulses ranging from the gamma and very high gamma ranges. Such pulses are present even in the absence of actual movement and are absent when the subject is not attempting a movement. Activity in the alpha range is detected when the subject closes their eyes. Such high-bandwidth biomarkers open the possibility of linking intentional movements to a word library and translating them into speech, providing ALS patients with a communication tool that utilizes brain signals.

[0030] In some embodiments, distinct, broad spectral patterns of activity across many frequencies can be detected for real or imagined movements compared to distinct patterns for a resting state. These patterns coincide with the timing of a subject's real or imagined movements. Traditional spectral analysis does not reveal such patterns. Analysis of physiological data can be used to detect brain signals timed and associated with a subject's real or imagined movements. In some embodiments, these signals are transmitted and analyzed in real time to provide additional flexibility for brain-based communication.

[0031] The methods described herein are disclosed in detail in International Application Nos. 2006 / 018120; 2009 / 064632; 2010 / 054346; U.S. Patent Application No. 8,073,574; and Low, Philip Steven (2007). "A new way to look at sleep: separation and convergence." Published Thesis, University of California, San Diego Electronic Theses and Dissertations (Identified: b6635681), the disclosures of which are incorporated herein by reference in their entireties.

[0032] The present invention utilizes systems and methods for acquiring and classifying EEG data in both animals and humans. The acquired EEG signals are low power frequency signals and follow a 1 / f distribution, whereby the power in the signal is inversely related, e.g., inversely proportional to frequency.

[0033] EEG signals are often examined in successive time periods called epochs. Epochs can be segmented into different sections using scanning windows that define the time series units of the different sections. The scanning window can move through a sliding window, where the sections of the sliding window have overlapping time series sequences. Alternatively, an epoch can span the entire time series, for example.

[0034] In a preferred embodiment of the present invention, a single channel of EEG was sufficient to obtain data indicative of intentional (or unintentional or other unexpected) brain activity.

[0035] Typically, the source data acquired by the methods of the present invention are adjusted to increase the dynamic range of power in at least one low-power frequency range of the frequency spectrum of the source data relative to a second, higher-power frequency range. Many adjustment techniques, including normalization and frequency weighting, as described herein, may be used.

[0036] In one embodiment, the electroencephalography source data is normalized to increase data with lower power in the higher frequency range compared to data with higher power in the lower frequency range, or more generally, to normalize the power of different parts of the signal.

[0037] After the source data has been adjusted, various other processing may be performed. For example, a visualization of the adjusted source data may be presented. Additionally, low-power frequency information may be extracted from the adjusted source data. For example, low-power frequency information may be extracted from the adjusted electroencephalography source data. Also, high-power frequency information may be extracted from the adjusted source data.

[0038] The method described in this or any of the other examples may be a computer-implemented method, performed via computer-executable instructions in one or more computer-readable media. Any actions shown may be performed by software embedded within a signal processing system or any other signal data analysis system. For example, the present invention may be implemented in a variety of ways, including as a process using an apparatus; a system; an entity; a computer program product embodied in a computer-readable storage medium; and / or a processor (e.g., a processor configured to execute instructions stored in and / or provided by a memory connected to the processor). In this regard, FIG. 9 is a screenshot of a computer interface utilizing the method and output of the present invention. These embodiments, or any other form the present invention may take, may be referred to herein as technology. In general, the order of steps in a disclosed process may be changed within the scope of the present invention. Unless otherwise specified, components, such as a processor or memory, described as configured to perform a task may be implemented as general components temporarily configured to perform that task at a given time, or as specific components manufactured to perform that task. As used herein, the term "processor" refers to one or more devices, circuits, and / or processing cores configured to process data, such as computer program instructions.

[0039] Another embodiment uses multiple normalizations to further increase the dynamic range. Normalization can be done by normalizing frequency over time or by normalizing time over frequency.

[0040] For example, electroencephalography data of at least one low-power frequency range may be received. Artifacts in the data may be removed from the source data. For example, the artifact data may be manually removed from the source data or automatically removed from the source data via filtering (e.g., DC filtering) or data smoothing techniques. The source data may also be preprocessed by component analysis (e.g., principal component analysis or independent component analysis). The source data may be segmented into one or more epochs, where each epoch is a portion of data from a series of data. For example, the source data may be segmented into multiple time segments via various separation techniques. Scanning windows and sliding windows may be used to separate the source data in time series units. One or more epochs may be normalized in time for the difference in power within the one or more epochs. For example, the power in each epoch at one or more frequencies may be normalized in time to determine an appropriate frequency window for extracting information. Such normalization may reveal low-power, statistically significant power shifts in one or more frequencies (e.g., delta, gamma, alpha, etc.). Any frequency range can be identified and utilized for analysis. After an appropriate frequency window is established, information is calculated for each of one or more epochs. Such information may include low-frequency power (e.g., delta power), high-frequency power (e.g., gamma power), standard deviation, maximum amplitude (e.g., maximum absolute value of peaks), etc. Further calculations can be performed on the information calculated for each of one or more epochs to generate information such as gamma power / delta power, time derivative of delta, time derivative of gamma power / delta power, etc. Time derivatives can be calculated across preceding and subsequent epochs. After calculating the information, the information can then be normalized across one or more epochs. Various data normalization techniques can be performed, including z-scoring and other similar techniques.

[0041] The results of adjusting the source data to account for differences in power across the spectrum of frequencies over time can be shown as one or more epochs of data, for example, frequency-weighted epochs can be shown as adjusted source data.

[0042] Electroencephalography data of a subject is acquired and input, and the data is segmented into one or more epochs. In practice, the epochs are of similar (e.g., the same) length. The length of the epochs can be adjusted via a configurable parameter. The one or more epochs are then input, and the frequency data in the one or more epochs is normalized across time, thereby frequency-weighting the electroencephalography data of the one or more epochs. The frequency-weighted one or more epochs are then input into a classifier, which classifies the data into a state of intention and a state of relaxation or unintentionality.

[0043] For example, electroencephalography (EEG) data related to a subject is received. For example, the electroencephalography data may be received, in which the dynamic range of power in at least one low-power first frequency range in a frequency spectrum is lower than that in a second frequency range in the frequency spectrum. The electroencephalography data related to the subject is segmented into one or more epochs. For example, various separation techniques may be used to segment the EEG data into one or more epochs. Scanning windows and sliding windows may be used to separate the EEG data into one or more epochs. The source data may also be actively DC filtered during, before, or after segmentation. The source data may also be preprocessed using component analysis (e.g., principal component analysis or independent component analysis). In the EEG data for the entire night, higher frequencies (e.g., gamma) exhibit lower power than lower frequencies (e.g., alpha, delta, theta, etc.) in the EEG data for the entire night. The frequency power of one or more epochs is weighted in the time direction. For example, the power of each epoch at one or more frequencies may be normalized in time to determine an appropriate frequency window for extracting information. Such normalization may reveal statistically significant power shifts of low power at one or more frequencies (e.g., alpha, delta, gamma, etc.). Furthermore, each epoch may be represented by the frequency with the highest relative power over time to determine an appropriate frequency window for extracting information. Alternatively, component analysis (e.g., principal component analysis (PCA) or independent component analysis (ICA)) may be used after normalization to further determine an appropriate frequency window for extracting information. Any frequency range may be identified and used for analysis.

[0044] After an appropriate frequency window is established (e.g., after frequency weighting), information may be calculated for each of one or more epochs. Such information may include low-frequency power (e.g., alpha power), high-frequency power (e.g., gamma power), standard deviation, maximum amplitude (e.g., maximum absolute value of peaks), etc. Further calculations may be performed on the information calculated for each of one or more epochs to generate information such as gamma power / alpha power, time derivative of delta, time derivative of gamma power / alpha power, etc. Time derivatives may be calculated across preceding and subsequent epochs. After calculating the information, the information may then be normalized across one or more epochs. Various data normalization techniques may be performed, including z-scoring, etc., where higher frequency data is more clearly visible.

[0045] The subject's intended state is classified based on one or more frequency-weighted epochs. For example, the one or more frequency-weighted epochs can be clustered using any of a variety of clustering techniques, including K-means clustering. The clustering can be based on information calculated from the epochs (e.g., alpha power, gamma power, standard deviation, maximum amplitude (gamma / alpha), time derivative of delta, time derivative of gamma / alpha, etc.). Component analysis (e.g., PCA or ICA) can be used to determine the parameter space (e.g., type of information used) in the clustering.

[0046] Following clustering, epochs can be assigned an intentional state designation. Epochs designated as intentional states can then be presented as an indication of the subject's intentional and relaxed (unintentional) states for the period represented by the epoch. Classification can also incorporate manually determined intentional states (e.g., manually determined "intentional activity" vs. "relaxed" states). Additionally, artifact information can be utilized in classification.

[0047] Artifact data can also be used in intent state classification. For example, artifacts can be used to analyze whether an epoch that was originally assigned an intent state designation can be reassigned a new intent state designation due to adjacent artifact data. Thus, for example, artifact data can be used in data smoothing techniques.

[0048] Any of a variety of data smoothing techniques can be used during the assignment of intent states. For example, numbers (e.g., 0 and 1) can be used to represent designated intent states. Brain state designation numbers for adjacent epochs can then be averaged to determine whether one of the epochs has been incorrectly assigned an intent state designation. Thus, if a group of epochs are assigned intent state designations that show rapid fluctuations in brain states, smoothing techniques can be applied to improve the accuracy of the assignments.

[0049] The previous embodiments have shown how normalization, for example, using z-scoring, can be used to enable the analysis of more information from brain activity signals. The analyses performed so far have normalized power information in the frequency direction. While z-scoring is preferred for normalization, any other type of data normalization can be used. The normalization used is preferably unitless, such as z-scoring. As is well known in the art, z-scoring can be used to normalize a distribution without changing the shape of the distribution's envelope. The z-scores are essentially converted to units of standard deviation. The normalized units of each z-score reflect the amount of power in a signal relative to the signal's mean. By subtracting the mean from each score, the scores are converted to a mean-deviation form. The scores are then normalized relative to the standard deviation. All z-scored normalized units have a standard deviation equal to unity.

[0050] Although the above describes normalization using Z-scores, it should be understood that other normalizations can be performed, including T-scoring, etc. Multiple normalizations can also be used. Normalization can be performed by normalizing frequency over time or time over frequency.

[0051] The above embodiments describe normalizing the power at each frequency within a specific range. The range can be from 0 to 100 Hz, or 128 Hz, or 500 Hz. The frequency range is limited only by the sampling rate. With a typical sampling rate of 30 KHz, analysis can be done up to 15 KHz.

[0052] According to this embodiment, an additional normalization is performed to normalize the power in the time direction for each frequency. This results in frequency- and time-normalized information, which is used to create a normalized spectrogram. This embodiment can obtain additional information from EEG data, and describes automatically detecting different periods of intention and relaxation from the analyzed data. According to an important feature, a single channel of EEG activity (acquired from a single location on the human cranium) is used for analysis. As mentioned above, the acquired data may be one-channel EEG information from a human or other subject. The acquired EEG data may be collected using, for example, a 256 Hz sampling rate, or may be sampled at a higher rate. The data is divided into epochs, for example, 30-second epochs, and characterized according to frequency.

[0053] First, frequency normalization is performed. Power information is normalized at each frequency bin using a z-scoring technique. In this embodiment, the bins may range from 1 to 100 Hz, with 30 bins per Hertz. This normalization is performed in the time direction, thereby creating a normalized spectrogram or NS in which each frequency band from the signal has approximately the same weighting. In this embodiment, each 30-second epoch is represented by a "preferred frequency," which is the frequency with the largest z-score within that epoch.

[0054] This generates a specific frequency space, called the preferred frequency space. Analysis of how these patterns are formed and their characteristics can be performed. Thus, different brain states can be defined according to a discriminant function that looks for activity in some regions and inactivity in other regions. The function can evaluate brain states according to which frequencies in a region have activity and which do not.

[0055] However, more generally, any form of dynamic spectral scoring can be performed on the corrected data. The discriminant function may require a feature value, or may simply require that a certain amount of activity be present or absent in each of multiple frequency ranges. The discriminant function may simply be a frequency response envelope match. The discriminant function may also look at spectral and temporal fragmentation.

[0056] A second normalization is performed in the frequency direction. The second normalization produces a doubly normalized spectrogram. This creates a new frequency space in which bands are even more pronounced. The values ​​of the doubly normalized spectrogram can be used to generate a filter that maximally separates the values ​​in space.

[0057] A clustering technique performed on doubly normalized frequencies. For example, the clustering technique may be the K-means technique as described in the previous embodiments. Each cluster may represent an intent state.

[0058] The clusters are in fact multidimensional clusters and can themselves be graphed to find additional information. The number of dimensions can depend on the number of clustering variables. This shows how doubly normalized spectrograms allow for even more measurement properties.

[0059] It is also possible to measure the average spread in frequency-normalized power, which indicates spectral fragmentation. Alternatively, the fragmentation value can be based on the temporal fragmentation for different states and can also be used as part of the discriminant function.

[0060] These two functions are evaluated on doubly normalized spectra and rely on a uniform increase in gain at all frequencies, which in singly normalized spectra would cause motion artifacts and result in abnormally elevated fragmentation values. These fragmentation values ​​can be used as part of a discriminant function. Importantly, as noted above, this discriminant function is typically not evident from any previous analytical techniques, including traditional manual techniques.

[0061] The calculations can be characterized by segmentation, or can use overlapping or sliding windows to increase time alignment. This enables many techniques that were not possible before. By performing characterization on the fly, this makes it possible to use dynamic spectral scoring to distinguish between states of relaxation and states of intention using only the EEG signature.

[0062] The above exemplary methods of data analysis were combined with standard non-invasive EEG methods for humans. The result is the ability to non-invasively extract attenuated rhythms in animals, automatically analyze brain activity from single-channel EEG, and fully classify the animals' brain state parameters. [Example]

[0063] Single-channel iBrain® EEG recordings were performed on a highly functional 70-year-old ALS patient attempting to move one of his limbs following a verbal cue (left and right hand and foot). The raw EEG signals were analyzed with the SPEARS algorithm, in part to detect high-frequency / low-spectral-power signals. Concurrent video recordings were acquired. During the movement attempt, the subject's brain activity exhibited clear, broad spectral pulses spanning the gamma and very high gamma ranges. Such pulses were present even in the absence of actual movement and were absent when the subject was not attempting a movement. As expected, activity in the alpha range was detected when the subject closed his eyes. Using such high-bandwidth biomarkers based on intentional movements against a library of words allows for the translation of signals into speech, thus providing ALS patients with a communication tool that relies more on the brain than the body.

[0064] Specifically, application of the method of the present invention reveals high-frequency patterns consistent with the timing of a subject's actual, imagined, or intended movements. In an exemplary application, a frequency spectrum is generated from time-series data and normalized to reveal these higher frequencies not revealed by standard methods. This application to brain EEG data is illustrated in Figure 1. In this example, a highly functional 70-year-old subject with ALS who is unable to move was asked to close his eyes, remain still, or imagine his hands or feet. For each of these tasks, the subject was given a verbal cue to begin the task and another cue 4 seconds later to stop. The task was repeated 6 seconds later, for a total of 12 attempts and 120 seconds per task. Figure 1 shows the standard frequency power spectrum (i) and the enhanced frequency power spectrum (ii) of the ALS subject while performing these tasks. Stronger signals appear redder, while weaker signals are reduced in intensity in shades of orange, yellow, and blue. Distinct bands of high-frequency activity appear at high intensity in the enhanced spectra, corresponding to the timing of cues for task initiation and completion. Figure 1A illustrates this for a 4-second eye-closing task. Figure 1B illustrates this for a 4-second eye-closing task, corresponding to imagining or attempting to clench the left hand for 4 seconds and relaxing for 6 seconds. Figure 1C, D, and E illustrate similar tasks, but for imagining or attempting to clench the right hand, left foot, and right foot, respectively. In all cases, the spectrograms generated in this application clearly reveal high-frequency spectral content that corresponds to the timing of the subject's attempts to perform the instructed task. Figure 1F shows the same spectral analysis of the same subject at rest, displaying high-frequency content at quite different times, which may represent environmental noise, background talk, and / or baseline electrical activity. [Example]

[0065] Application of the method of the present invention to the ALS patient described in Example 1 reveals components of the data that approximate the time of events. Figure 2 illustrates one such application of the method of the present invention (here, by component analysis of a single channel in a doubly normalized spectrogram) to reveal unrelated, independent data components. Figures 2A-2E show data from the same tasks shown in Figures 1A-1E. Each plot shows the resulting extracted independent components, after all the described analyses, with peaks that approximate individual tasks attempted by the subject. The green line indicates the timing of the verbal cue to begin the 4-second task, and the red line indicates the time of the verbal cue to stop the task and relax for 6 seconds. The peaks in each component roughly align with the onset of the task being performed (indicated by peak points in, on, or immediately after the green line). This analysis may also be combined with the analysis in Example 1 to enhance or reinforce event detection and timing. [Example]

[0066] This paper provides the application of the methods of the present invention to single-channel brain EEG data from an ALS patient described in Example 1 to assess data stability and identify changes in data stability indicative of intentional activity. Specifically, applying the methods of the present invention to generate temporal fragmentation reveals changes in stability that approximate the timing and duration of intentional activity. Figures 3A-E show temporal fragmentation for the same task as shown in Figures 1A-E. In each plot, shifts in points from negative to positive represent decreases in stability. These shifts approximate both the verbal cue to begin the task (green line), where stability begins to decrease and the positive shift begins, and the verbal cue to end the task (red line), where data begins to stabilize and the positive shift ends. Zero-line crossings (purple dots) indicate increases in unstable periods (during or immediately after the task) and decreases in stable period counts (relaxation periods before or after each task). These shifts and line crossings can also be combined with the methods of Example 1 to enhance or reinforce event detection and timing. [Example]

[0067] Detection of high-frequency events and event timing relative to low-frequency events in an ALS patient according to Example 1. Generation of a spectrogram, followed by one or more rounds of normalization across one or both data axes, followed by feature sharpening using the doubly normalized spectrogram, followed by extraction of standard known frequencies, frequency ranges, their sums, their ratios, and / or other frequency relationships reveals the timing, intervals, and / or duration of events. Figures 4A-4E illustrate the tasks in Figures 1A-4B, plotting the sum of high-frequency power in the gamma and very high gamma (hgamma) range frequencies (all above 30 Hz). In the brain's EEG, increased power at these frequencies is associated with heightened concentration, such as occurs when imagining moving a limb. Peaks in the sum of high-frequency power approximate the timing of tasks, indicated by the green (task start) and red (task end) columns of lines. Figure 5A-D shows the tasks in Figure 1 for grasping the right hand, left hand, right foot, and left foot, respectively, plotting the ratio of total alpha frequency (8-13 Hz) power to total gamma frequency power (30-50 Hz). Increased power in alpha frequency in the brain EEG is associated with relaxation of mental effort, such as after a period of attempting to grasp the limb. Thus, alpha and gamma frequency power are inversely related for these tasks, with peaks in the ratio of alpha to gamma appearing between tasks (between the red line indicating cessation and the green line indicating resumption). Figure 5E shows an analysis of delta frequency (<5 Hz) during the eyes-closing task. Delta frequency power correlates with changes in eyes-open and closed, approximating the timing of subjects closing (green line) and opening (red line) their eyes. These and other frequency analyses may be combined with each other and with the methods in Examples 1, 2, and 3 to enhance or augment event detection and timing, and to characterize events with known associated brain states indicated by the analyzed frequencies. [Example]

[0068] Simultaneous detection of multiple simultaneous events in a second ALS patient. Multiple events are revealed in a single analysis by generating a spectrogram, then normalizing one or more times across one or both data axes, followed by feature sharpening, followed by extraction of standard known frequencies, frequency ranges, their sums, their ratios, and / or other frequency relationships. Figure 8 shows data from a task designed to induce both increased focus (increased gamma frequency power) and increased shifts in thought (changes in alpha frequency power), indicating decreased and increased relaxation. A non-mobile ALS patient subject was instructed to alternate between two 10-second imaginings (kicking a football: green line, and looking at the bedroom: red line) five times. Figure 8A plots normalized and enhanced gamma frequency power, showing a sharp increase at the beginning of the alternating sequence (first green line, 13 seconds) and a subsequent decrease at the end of all subsequent sequences (last red line, 103 seconds). Figure 8B plots normalized and enhanced alpha frequency power, showing the change in relaxation state (peaks between the colored lines) between two different imaginations. Figure 8C plots normalized and enhanced alpha and gamma power, showing the simultaneous detection of both the plateau in gamma frequency power during the imagination sequence and the peaks in alpha as imaginations change, as two unique signals detected in a single analysis. Figure 8D plots normalized and enhanced alpha to very high gamma (hgamma) ratios, with a rapid drop in ratio at the beginning of the sequence, individual peaks during each imagination, and a rise in ratio after the sequence. [Example]

[0069] Use of iteratively normalized spectrograms to distinguish and characterize multiple types of intentional events. Analysis involving the application of the SPEARS algorithm followed by a two-sample Kolmogorov-Smirnov (KS) test for sampling at the same distribution between any two spectrograms reveals distinguishable imagined motor actions. This application allows for multiple degrees of freedom based on at least one event type being distinguishable from the others. p-values ​​of the KS test for the cross-axially 10-fold normalized spectrograms from each task listed in Figure 1A-E performed by an immobile ALS subject indicate that each spectrogram comes from a distribution distinct from that of resting (P<0.01) (Table 1) and from that of most other tasks (P<0.05) (Figure 10), making it an effective application for characterizing the same or multiple events during detection, although to a lesser extent for the left hand and right foot in this particular trial.

[0070] Table 1. p-values ​​of the K-S test for the imaginary task versus the resting state TIFF0007825094000001.tif73128

[0071] Throughout this application, various publications, patents, and / or patent applications are referenced in order to more fully describe the state of the art to which this invention pertains. The disclosures of these publications, patents, and / or patent applications are hereby incorporated by reference in their entireties and to the same or preceding sentences as if each individual publication, patent, and / or patent application was specifically and individually indicated to be incorporated by reference with respect to subject matter specifically referenced herein.

[0072] While only a few embodiments have been disclosed in detail above, other embodiments are possible and the inventors intend these to be encompassed within this specification. This specification describes specific examples for achieving more general goals that may be achieved in other ways. This disclosure is intended to be exemplary, and the claims are intended to cover any modifications or alternatives that would be predictable to one of ordinary skill in the art. For example, other applications are possible, and other forms of discriminant functions and characterizations are possible. While the above broadly describes characterizing frequencies in terms of their "preferred frequencies," it should be understood that more rigorous characterization of information may be possible. Also, while the above only refers to determining intent states from EEG data and only refers to determining a few different types of intent states, it should be understood that other applications are contemplated.

[0073] While the principles of the present invention have been shown and described in exemplary embodiments, it will be apparent to those skilled in the art that the described embodiments are exemplary and illustrative embodiments and that changes in arrangement and detail may be made without departing from such principles. Techniques from any embodiment may be incorporated into any one or more other embodiments. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

Claims

1. 1. A system for detecting intentional brain signals from a subject, comprising: a sensor for detecting data indicative of brain activity of the subject; 1. A computer memory module containing instructions configured to be executed by a computer processor, the instructions comprising: obtaining the data indicative of brain activity from the sensor; analyzing the acquired data indicative of brain activity, the analyzing the data including calculating a spectrogram of the data, normalizing the spectrogram of the data at least once, and performing a fragmentation analysis; and Correlating the analyzed data with intentional higher-order cognitive functions from the subject. a computer memory module including: a computer processor module configured to execute the instructions in the computer memory module; and A system comprising:

2. The system of claim 1 , wherein the data is acquired non-invasively by attaching the sensor to the subject.

3. 3. The system of claim 2, wherein the data is obtained from at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel.

4. The system of claim 3 , wherein the data is obtained from at least a single channel of an EEG.

5. 3. The system of claim 2, wherein the data is acquired from a multi-channel detector including at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel.

6. The analyzing further comprises: The system of claim 1 , further comprising performing principal component analysis and / or independent component analysis of the normalized spectrogram.

7. The system of claim 1 , wherein the fragmentation analysis includes temporal fragmentation analysis.

8. The system of claim 1 , wherein the analyzing comprises performing a preferred frequency analysis.

9. The system of claim 8 , wherein the analysis of the preferred frequencies is performed on the spectrogram.

10. The system of claim 1 , wherein the fragmentation analysis comprises spectral fragmentation analysis.

11. 10. The system of claim 1, wherein the instructions further comprise, after the correlating, transforming the analyzed data to perform a task associated with the higher cognitive function, the task being selected from the group consisting of simulating, speaking on a display, simulating speech with a speech synthesizer, and moving an artificial prosthesis.

12. 2. The system of claim 1, wherein the higher cognitive function is selected from the group consisting of intention, speech, recollection, planned action, thinking, and imagination.

13. 1. A method for detecting intentional brain signals from a subject, comprising: detecting data indicative of brain activity of the subject; acquiring the data indicative of brain activity from a sensor; analyzing the acquired data indicative of brain activity, the analyzing the data including calculating a spectrogram of the data, normalizing the spectrogram of the data at least once, and performing a fragmentation analysis; Correlating the analyzed data with intentional higher-order cognitive functions from the subject. A method comprising:

14. The method of claim 13 , wherein the data is acquired non-invasively by attaching the sensor to the subject.

15. 15. The method of claim 14, wherein the data is obtained from at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel.

16. The method of claim 15 , wherein the data is obtained from at least a single channel of an EEG.

17. 15. The method of claim 14, wherein the data is acquired from a multi-channel detector including at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel.

18. The analyzing further comprises: The method of claim 13 , comprising performing principal component analysis and / or independent component analysis of the normalized spectrogram.

19. The method of claim 13 , wherein the fragmentation analysis comprises temporal fragmentation analysis.

20. The method of claim 13 , wherein said analyzing comprises performing a preferred frequency analysis.

21. 21. The method of claim 20, wherein the analysis of preferred frequencies is performed on the spectrogram.

22. The method of claim 13 , wherein the fragmentation analysis comprises spectral fragmentation analysis.

23. 14. The method of claim 13, further comprising, after said correlating, transforming the analyzed data to perform a task associated with said higher cognitive function, said task being selected from the group consisting of simulating, speaking on a display, simulating speech with a speech synthesizer, and moving an artificial prosthesis.

24. 14. The method of claim 13, wherein the higher cognitive function is selected from the group consisting of intention, speech, recollection, planned action, thinking, and imagination.

25. A computer program product embodied in a non-transitory machine-readable storage medium containing instructions, the instructions comprising: detecting data indicative of brain activity of the subject; acquiring the data indicative of brain activity from a sensor; analyzing the acquired data indicative of brain activity, the analyzing the data including calculating a spectrogram of the data, normalizing the spectrogram of the data at least once, and performing a fragmentation analysis; Correlating the analyzed data with intentional higher-order cognitive functions from the subject.

1. A computer program product configured to cause one or more data processors to perform a set of actions comprising:

26. 26. The computer program product of claim 25, wherein the data is acquired non-invasively by attaching the sensor to the subject.

27. 27. The computer program product of claim 26, wherein the data is obtained from at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel.

28. 28. The computer program product of claim 27, wherein the data is obtained from at least a single channel of an EEG.

29. 27. The computer program product of claim 26, wherein the data is obtained from a multi-channel detector including at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel.

30. The analyzing further comprises:

26. The computer program product of claim 25, comprising performing principal component analysis and / or independent component analysis of the normalized spectrogram.

31. 26. The computer program product of claim 25, wherein the fragmentation analysis comprises temporal fragmentation analysis.

32. 26. The computer program product of claim 25, wherein said analyzing comprises performing a preferred frequency analysis.

33. 33. The computer program product of claim 32, wherein the analysis of preferred frequencies is performed on the spectrogram.

34. 26. The computer program product of claim 25, wherein the fragmentation analysis comprises spectral fragmentation analysis.

35. 26. The computer program product of claim 25, further comprising, after said correlating, transforming the analyzed data to perform a task associated with said higher cognitive function, said task being selected from the group consisting of simulating, speaking on a display, simulating speech with a speech synthesizer, and moving an artificial prosthesis.

36. 26. The computer program product of claim 25, wherein the higher cognitive function is selected from the group consisting of intention, speech, recollection, planned action, thinking, and imagination.

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