Correlating brain signal to intentional and unintentional changes in brain state

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 the benefit of U.S. Provisional Patent Application No. 61 / 590,235, filed Jan. 24, 2012, and U.S. Patent Application No. 13 / 749,619, filed Jan. 24, 2013, the contents of which are hereby incorporated by reference in their entirety.

[0002] Field of the Invention The present invention relates to an analytical method for extracting and evaluating brain data collected from a subject animal, including a human, for detecting intentional brain signals as well as unintentional brain signals and other unexpected brain signals. These signals are associated with higher-order brain cognitive functions or unintentional potential harmful events such as strokes or seizures, and are also associated with the conversion of these signals into a defined inducing event or task. More specifically, the present invention relates to the acquisition of physiological data from EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or other signals obtained from peripheral channels that may be regulated by or regulate the brain activity of the subject.

Background Art

[0003] Background of the Invention An electroencephalogram (EEG) is a tool used to measure the electrical activity generated by the brain. The functional activity of the brain is collected by electrodes placed on the scalp. EEG has conventionally provided important information about the function of a patient's brain. Scalp EEG is thought to measure the collective current present postsynaptically in the extracellular space, which is caused by the flow of ions from or to the dendrites linked by neurotransmitters. Thus, EEG and similar modalities are mainly used in neurology as diagnostic tools for epilepsy, but the technology can be used in the study of other pathological conditions, including sleep disorders.

[0004] Recent advancements in the detection of EEG and other signals have enabled real-time automatic detection in sleep and wake states through normalization and other operations on brain activity data. Furthermore, such applications and methods can also be used to automatically access the state of illness and the effects of drug treatment. Related technologies have made it possible to access such data in real time using single-channel detectors. This has now provided an opportunity to further examine sleep and wake states, including clarifying the distinction between REM and deep sleep. To assist in the efficient collection of such data, a head and harness system has been developed that uses single-channel wireless data transmission. These disclosures are hereby incorporated by reference in their entirety, for example, 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) (Non-Patent Document 1). To date, this technology has been mainly applied to sleep-related diagnostic applications as well as the effects of pathological conditions and drug treatment.

[0005] Development continues in the area of exoskeletons and related prostheses, which support the promise of enabling paralyzed patients to walk again and perform other tasks they are currently unable to do. Additionally, such devices may be useful to able-bodied individuals such as soldiers in the battlefield, first responders, and construction workers. Companies such as Esko Bionics, Parker Hannifan, and Argo Medical Technologies have been consistently advancing such technologies. See, for example, U.S. Patent No. 8,096,965 (Patent Document 5), International Application Publication No. 2010101595 (Patent Document 6), and U.S. Patent Application No. 11 / 600,291 filed on November 15, 2006 (Patent Document 7). Further, other devices are being utilized to enable individuals severely disabled by ALS, MS, etc. to communicate using voice synthesis devices, etc. Such devices typically operate by movements of the cheek muscles, eye movements using an Eye Tracker, etc.

[0006] Therefore, there is a need for a non-invasive method for detecting intentional and unintentional communication from subjects, including impaired individuals, in order to evaluate and possibly respond to or prepare for the physiological significance of these changes in the brain state.

Prior Art Documents

Patent Documents

[0007]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Patent Document 5

Patent Document 6

Patent Document 7

Non-Patent Document

[0008]

Non-Patent Document 1

Summary of the Invention

[0009] The present invention provides a method for non-invasively detecting intentional and unintentional communications from healthy subjects and subjects with diseases (including individuals impaired by neurological diseases such as ALS, MS, etc.) in the form of physiological data such as EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, etc., and a method for associating these intentional and unintentional signals with changes in brain states including higher cognitive functions. Also, there is a need to utilize such intentional communications, for example, to simulate speech or move a prosthesis. Further, there is a need to access unintentional signals from subjects with pathological conditions such as epilepsy (or a disease of the body that causes a change in brain activity) for use in associating with the pathological condition and optionally activating an alarm and / or intervening in changes, suppression, or preparation for unintentional events.

[0010] In a preferred method of the present invention, an intentional brain signal from a subject is detected by attaching at least a single sensor to the subject, acquiring data indicating brain activity, analyzing the data indicating brain activity, and associating the analyzed data with an intentional higher cognitive function from the subject. Preferably, the data is acquired non-invasively by applying at least a single sensor to the subject, more preferably by applying at least a single dry sensor or at least a single wet sensor. Further, the data is preferably received from at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel regulated by the intention of the subject. In an alternative embodiment, the data is received through a multi-channel detector. Further, it is preferable that the data is communicated and received wirelessly.

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

[0012] In a particularly preferred embodiment, the method of the present invention further includes converting the analyzed data to perform tasks related to higher cognitive functions including, but not limited to, intention, speech, recollection, thought, imagination, and planning (including but not limited to actions). Importantly, the tasks resulting from converting the analyzed data include simulating speech on a display, simulating speech by a speech synthesizer, or moving an artificial prosthesis or exoskeleton, etc.

[0013] In another more preferred embodiment of the method of the present invention, at least a single sensor is attached to the subject, data indicating brain wave activity is acquired, the data indicating brain activity is analyzed, and the analyzed data is associated with at least one unintended event, whereby the brain signal from the subject is associated with at least one unintended event. In a further embodiment, an alarm is activated after associating the unintended event with the data. Alternatively, after associating the unintended event with the data, a response for improving the effect of the unintended event can be initiated, which may include changing, suppressing, or preparing for the unintended event (and may also be activating an alarm). This response will be particularly effective in the case of unintended events, high fragmentation events, changes in the fragmentation of events, surprise, tremors, convulsions, injuries, or pathological conditions including but not limited to epileptic seizures, migraines, strokes, heart attacks, or infarcts.

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

[0015] Unless defined otherwise, all terms in this specification have the same meaning as commonly understood by one of ordinary skill 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 may also be used. The materials and methods, as well as the examples, are illustrative only and not intended to be limiting. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the definition including this specification shall prevail.

[0016] These and other embodiments of the present invention will be recognized and understood when considered in conjunction with the following description and the accompanying drawings. However, it should be understood that the following description, while showing various embodiments of the present invention and many of its specific details, is by way of illustration and not limitation. Many substitutions, changes, additions, and / or recombinations may be made within the scope of the present invention without departing from its spirit, and the present invention includes all such substitutions, changes, additions, and / or recombinations. [The present invention 1001] A method for detecting an intentional brain signal from a subject, comprising the following steps: (a) attaching at least a single sensor to the subject; (b) acquiring data indicative of brain activity; (c) The step of analyzing the data indicating brain activity; and (d) The step of associating the analyzed data with the intentional higher cognitive function from the subject. [Invention 1002] The method of Invention 1001, wherein the acquired data is received non-invasively by applying the at least single sensor to the subject. [Invention 1003] The method of Invention 1002, wherein the at least single sensor is selected from the group consisting of a single dry sensor or a single wet sensor. [Invention 1004] The method of Invention 1002, wherein the acquired data is received from at least a single channel of EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel regulated by the intention of the subject. [Invention 1005] The method of Invention 1004, wherein the acquired data is received from at least a single channel of EEG. [Invention 1006] The method of 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 regulated by the intention of the subject. [Invention 1007] The method of Invention 1002, wherein the acquired data is received wirelessly. [Invention 1008] The step of analyzing includes (a) The step of normalizing the spectrogram of the data, including the normalized spectrogram, at least once over time across frequencies; and (b) The step of normalizing the spectrogram of the same data, including the normalized spectrogram, at least once over frequency across time The method of Invention 1001 including. [Invention 1009] The step of analyzing includes (a) The step of 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 on the normalized spectrogram The method of the present invention 1008 including the above steps. [The present invention 1010] The method of the present invention 1001, wherein the analyzing step includes analyzing temporary fragmentation. [The present invention 1011] The method of the present invention 1001, wherein the analyzing step includes analyzing a preferred frequency. [The present invention 1012] The method of the present invention 1011, wherein the analysis of the preferred frequency is performed on a spectrogram that has been normalized at least twice. [The present invention 1013] The method of the present invention 1001, wherein the analyzing step includes analyzing spectral fragmentation. [The present invention 1014] The method of the present invention 1001, further including, after the associating step, converting the analyzed data to perform a task related to a higher cognitive function. [The present invention 1015] The method of the present invention 1001, wherein the higher cognitive function is selected from the group consisting of intention, speech, recollection, planned movement, thought, and imagination. [The present invention 1016] The method of the present invention 1011, wherein the task is selected from the group consisting of simulating speech on a display, simulating speech by a speech synthesizer, and moving a prosthesis. [The present invention 1017] A method for detecting brain signals related to at least one unintentional event from a subject, including the following steps: (a) attaching at least a single sensor to the subject; (b) acquiring data indicating brain wave activity; (c) analyzing the data indicating brain activity; and (d) associating the analyzed data with at least one unintended event. [Invention 1018] The method of Invention 1017, further comprising, after the associating step, activating an alarm. [Invention 1019] The method of Invention 1017, further comprising, after the associating step, responding to the unintended event to improve its effect. [Invention 1020] The method of Invention 1019, wherein the response to the unintended event includes intervening to change, suppress, or prepare for the unintended event. [Invention 1021] The method of Invention 1017, wherein the unintended event is selected from the group consisting of high fragmentation events, changes in event fragmentation, surprise, tremors, convulsions, injuries, or pathological conditions including but not limited to epileptic seizures, migraines, strokes, heart attacks, or infarcts. [Invention 1022] The method of Invention 1017, wherein the response to the unintended event enhances or replaces a polygraph test. [Invention 1023] The method of Invention 1017, wherein the analyzing step includes analyzing temporary fragmentation. [Invention 1024] The method of Invention 1017, wherein the analyzing step includes analyzing a preferred frequency. [Invention 1025] The method of Invention 1024, wherein the analysis of the preferred frequency is performed on at least two normalized spectrograms. [Invention 1026] The method of Invention 1017, wherein the analyzing step includes analyzing spectral fragmentation. [Invention 1027] The analyzing step is (a) Normalizing the spectrogram of the data, including the normalized spectrogram, at least once over time across frequencies; and (b) Normalizing the spectrogram of the same data, including the normalized spectrogram, at least once over frequency across time The method of the present invention 1017 including the above. [The present invention 1028] The step of analyzing includes (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 on the normalized spectrogram The method of the present invention 1027 including the above. [The present invention 1029] A method for detecting an intentional signal from a subject, including the following steps: (a) Attaching at least one detector capable of detecting an intentional signal to the subject; (b) Obtaining data indicating the detected activity using EEG, EMG, EOG, MEG, ECG, ECoG, iEEG, LFP, fMRI, or a peripheral channel regulated by the intention of the subject; (c) Analyzing the data indicating the detected activity; and (d) Associating the analyzed data with the intentional higher cognitive function from the subject.

Brief Description of Drawings

[0017] For the present invention to be clearly understood and easily implemented, the present invention is described in conjunction with the following drawings, where like reference numerals indicate the same or similar elements, and the drawings are incorporated herein and constitute a part thereof.

[0018] The patent or application file includes at least one drawing finished in color. Copies of the published gazette of the patent or patent application including color drawings are provided by the office upon payment of the claim and necessary fees.

[0019]

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MODE FOR CARRYING OUT THE INVENTION

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

[0021] A detailed description of one or more embodiments of the present invention is provided below together with the accompanying drawings showing the principles of the present invention. The present invention is described in relation to such embodiments, but the present invention is not limited to any particular 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 description to provide a complete understanding of the present invention. These details are provided for illustrative purposes only, and the present invention may be practiced without some or all of these specific details, in accordance with the claims. For the purpose of clarity, technical content known in the technical field related to the present invention is not described in detail so that the present invention is not unnecessarily obscured.

[0022] As used in this application, the term "subject" refers to both animals and humans. Referring now to FIG. 6, there is disclosed a flowchart 100 showing a preferred embodiment of the method. Specifically, the subject imagines and generates an intentional brain signal received by at least a single sensor 101 according to the present invention. Preferably, this sensor includes a single wet electrode or a single dry electrode. The input signal is relayed to a computing device, such as a computer, where it is analyzed (102) to determine (103) whether a defined unintentional event has occurred. Preferably, this indicates brain activity associated with higher cognitive functions from the subject. The computer then generates a signal that is relayed to one or more receivers 105 in a peripheral device (104). Examples of peripheral devices can include a voice synthesizer, a prosthetic device including an exoskeleton, and the like. As shown in FIG. 6, these methods are preferably performed in real time and provide a continuous response and feedback loop between the subject's intentional signal and the conversion of these signals into commands associated with a peripheral device controlled by the subject's intention. In a particularly preferred embodiment, the data signal from the subject is 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 intention. In a preferred alternative, the data is received through a multi-channel detector that wirelessly connects the computing device and the peripheral device.

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

[0024] The computer then generates a signal that is relayed to one or more receivers of the peripheral device (110). In this example, the peripheral device is a stimulation device 111 that is used to improve the effect of a dangerous seizure in an epileptic patient by stimulating the subject's brain region 112. Also, as will be recognized by those skilled in the art, the peripheral device 111 may also not suppress or change (or affect) the subject 112. For example, activation of an alarm to warn the subject or caregiver. In a particularly preferred embodiment, the data signal from the subject is received from at least a single channel of an EEG, EMG, EOG, MEG, ECoG, iEEG, fMRI, LFP, or a peripheral channel regulated by the unintended occurrence of the unintended event being monitored. In a preferred alternative, the data is received through a multi-channel detector that is wirelessly connected to the computing device and the peripheral device.

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

[0026] In a preferred embodiment, detecting a brain signal (e.g., a high-frequency signal detected from the brain) and associating the detected signal with a higher cognitive function are disclosed. Examples of higher cognitive functions include, inter alia, intention, speech, recall, thought, imagination, and (including but not limited to actions) planning, as well as directed tasks including imagination and other cognitive processes and functions.

[0027] In some embodiments, the subject's brain signal is converted into speech. For example, when the subject imagines one or more language elements including, but not limited to, letters, numbers, words, or symbols, the brain signal associated with this language element or these language elements is detected, and this language element or these language elements are determined. In another example, a subject with a speech impairment is taught to associate an image, symbol, word, letter, or number with an imaginary action. When the subject imagines the imaginary action, the brain signal associated with the imaginary action is detected, and the word, letter, or number associated with the imaginary action is determined. In one embodiment, the subject's imaginary action controls a cursor on a display to select an image, symbol, word, letter, or number that 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 a grammatically correct utterance. In some embodiments, the determined image, symbol, word, letter, or number is transmitted 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 micro-pitch, a color, an image, an electrical stimulus, a one-dimensional or multi-dimensional graphic. In some embodiments, the brain signal and / or output is assigned to a certain brain signal. In some embodiments, the brain signal follows and / or precedes and / or occurs simultaneously with one or more endogenous and / or exogenous events and / or states including, but not limited to, (pathological and / or abnormal events and / or states).

[0028] In some embodiments, the subject's brain signal is converted into the movement of a prosthesis. For example, the detected brain signal associated with an imaginary action is used, at least in part, to control a prosthesis.

[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 regulated by the subject's intention, or the reading of unintentional signals related to unintentional events, are used to detect one or more brain signals. For example, in a highly functional 70-year-old ALS patient attempting to move one of their limbs after a verbal cue (left and right hands and feet), a single-channel iBrain® EEG recording is taken. To enable the detection of brain signals, the EEG signals are analyzed with an algorithm including the SPEARS algorithm. A simultaneous video recording may be obtained. During the attempted movement, the subject's brain activity shows broad spectral pulses spanning the gamma and very high gamma ranges. Such pulses exist even without actual movement and do not exist when the subject is not attempting movement. Activity in the alpha range is detected when the subject closes their eyes. Such high-band biomarkers open the possibility of associating intentional movements with a library of words and converting them into speech, providing a communication tool using brain signals to ALS patients.

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

[0031] The methods described herein are disclosed in detail in International Application No. 2006 / 018120; International Application No. 2009 / 064632; International Application No. 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 hereby incorporated by reference in their entirety.

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

[0033] EEG signals have often been examined in successive time intervals called epochs. Epochs can be segmented into different sections using a scanning window that defines the time series units of the different sections. The scanning window can move via a sliding window, and the sections of the sliding window have an array of overlapping time series. Alternatively, an epoch can, for example, span the entire time series.

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

[0035] Typically, the source data obtained by the method of the present invention is adjusted to increase the dynamic range with respect to the power within at least one low-power frequency range of the frequency spectrum of the source data as compared to a second, higher-power frequency range. Many adjustment techniques described herein, including normalization and frequency weighting, may be used.

[0036] In one embodiment, the source data of the electroencephalogram recording is normalized to increase the data in the higher-frequency range of lower power as compared to the data in the higher-power, lower-frequency range, and more generally, the power of different portions of the signal is normalized.

[0037] After the source data is adjusted, various other processes may be performed. For example, visualization of the adjusted source data may be presented. Further, low-power frequency information may be extracted from the adjusted source data. For example, low-power frequency information may be extracted from the adjusted electroencephalogram recording source data. Also, high-power frequency information may be extracted from the adjusted source data.

[0038] The method described in any of this or other examples may be a computer-implemented method performed via computer-executable instructions in one or more computer-readable media. Any of the actions shown may be performed by software incorporated within a signal processing system or any other signal data analysis system. For example, the present invention may be implemented in various ways including an apparatus; a system; an assembly; 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 a memory connected to the processor and / or instructions provided by the memory) as a process. In this regard, FIG. 9 is a screen shot of a computer interface that utilizes the method and output of the present invention. In this specification, these embodiments or any other form that the present invention may take may be referred to as a technique. Generally, the order of steps of the disclosed process may be changed within the scope of the present invention. Unless otherwise specified, components such as a processor or a memory described as being configured to perform a task are implemented as general components temporarily configured to perform the task at a given time or as specific components manufactured to perform the 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 for further dynamic range increase. Normalization can be performed by normalizing frequency in the time direction or normalizing time in the frequency direction.

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

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

[0042] EEG recording 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. Next, the one or more epochs are input, and the frequency data in the one or more epochs is normalized over the entire time, whereby the EEG recording data of the one or more epochs is weighted by frequency. Next, the one or more epochs weighted by frequency are input into a classifier, and the data is classified into an intended state and a relaxed or unintended state.

[0043] For example, electroencephalogram (EEG) data regarding a subject is received. For example, data of an electroencephalogram recording may be received in which a dynamic range of power in at least one first low-power frequency range in a frequency spectrum is low as compared to a second frequency range in the frequency spectrum. The electroencephalogram recording data regarding the subject is segmented into one or more epochs. For example, the EEG data may be segmented into one or more epochs via various separation techniques. A scanning window and a sliding window can be used to separate the EEG data into one or more epochs. Also, the source data can be actively filtered with direct current during, before, or after segmentation. Also, the source data is preprocessed by cause analysis (for example, principal component analysis or independent component analysis). In the overall EEG data at night, higher frequencies (for example, gamma) exhibit lower power than lower frequencies (for example, alpha, delta, theta, etc.) in the EEG data over the whole 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 can be normalized in the time direction to determine an appropriate frequency window for extracting information. Such normalization can reveal a statistically significant power shift of low power at one or more frequencies (for example, alpha, delta, gamma, etc.). Further, each epoch can be represented by the frequency having the highest relative power over all time to determine an appropriate frequency window for extracting information. Alternatively, cause analysis (for example, principal component analysis (PCA) or independent component analysis (ICA)) can be utilized after normalization to further determine an appropriate frequency window for extracting information. Ranges of all frequencies can be revealed and used for analysis.

[0044] After an appropriate frequency window has been established (e.g., after frequency weighting), information can be calculated for each of one or more epochs. Such information can include low-frequency power (e.g., alpha power), high-frequency power (e.g., gamma power), standard deviation, maximum amplitude (e.g., maximum of the absolute value of the peak), etc. For the information calculated for each of one or more epochs, further calculations can be performed to generate information such as gamma power / alpha power, time derivative of delta, time derivative of gamma power / alpha power, etc. The time derivative may be calculated across preceding and subsequent epochs. After calculating the information, subsequently, the information may be normalized across one or more epochs. Various data normalization techniques including z-scoring etc. can be implemented. Here, higher-frequency data can be seen more clearly.

[0045] The intended state in the subject is classified based on one or more epochs weighted by frequency. For example, one or more epochs weighted by frequency can be clustered by any of various clustering techniques including K-means clustering. The clustering can be made 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.). Cause analysis (e.g., PCA or ICA) can be used to determine the parameter space (e.g., the type of information used) in the clustering.

[0046] Following the clustering, an assignment of an intended state designation to the epochs can be made. The intended state and the designated epochs can then be shown as an indication of the intended state and the relaxed (unintended) state in the subject during the period indicated by the epochs. Also, the classification can incorporate manually determined intended states (e.g., manually determined "intentional activity" state vs. "relaxed" state). Further, artifact information can be utilized in the classification.

[0047] Artifact data can also be used in the classification of intended states. For example, an artifact can be used to analyze whether a new designation of an intended state is reassigned due to the data of adjacent artifacts for an epoch initially assigned a designation of an intended state. Thus, for example, artifact data can be utilized in data smoothing techniques.

[0048] Any of a variety of data smoothing techniques can be used during the assignment of intended states. For example, numbers (e.g., 0 and 1) can be used to represent the designated intended states. The designation numbers of the brain states of adjacent epochs can then be averaged to determine whether one of the epochs has been incorrectly assigned to an intended state designation. Thus, if a designation of an intended state indicating a sharp change in the brain state is assigned to a group of epochs, a smoothing technique can be applied to improve the accuracy of the assignment.

[0049] The previous embodiments have shown, for example, how to normalize using z-scoring to enable the analysis of more information from brain activity signals. The analysis performed so far has normalized power information in the frequency direction. Normalization preferably used z-scoring, but 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 envelope of the distribution. The z-score is essentially changed to units of standard deviation. Each unit normalized by each z-score reflects the amount of power in the signal relative to the mean of the signal. By subtracting the mean from each score, the score is converted into the form of a mean deviation. The score is then normalized based on the standard deviation. All z-scored normalized units have a single equal standard deviation.

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

[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 up to 128 hz, or up to 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, additional normalization is performed to normalize the power in the time direction for each frequency. Thereby, information normalized in the frequency direction and the time direction is provided, which is used to create a normalized spectrogram. This embodiment can obtain additional information from the EEG data, and this embodiment describes automatically detecting different periods of intention and relaxation from the analyzed data. According to an important feature, the EEG activity of a single channel (obtained from a single location on the human skull) is used for analysis. As described above, the acquired data may be EEG information of one channel from a human or other subject. The acquired EEG data can be collected, for example, using a sampling rate of 256 Hz, or can be sampled at a higher rate. The data is divided into epochs, for example, epochs of 30 seconds, and characterized according to frequency.

[0053] The first frequency normalization is performed. In each frequency bin, the power information is normalized using the z-score technique. In this embodiment, the bin can range from 1 to 100 Hz and can be 30 bins per hertz. The normalization is performed in the time direction. Thereby, a normalized spectrogram or NS is created in which each frequency band from the signal has approximately the same weighting. In this embodiment, each epoch of 30 seconds is represented by the "preferred frequency", which is the frequency having the largest z-score within that epoch.

[0054] Thereby, a specific frequency space called the preferred frequency space is generated. Analysis of how these patterns are formed and analysis of the characteristics of the patterns can be performed. Thus, different brain states can be defined according to a discrimination function that explores the activity in one region and the inactivity in other regions. The function can evaluate the brain state according to which frequencies in the region have activity and which frequencies do not have activity.

[0055] However, more generally, any form of dynamic spectrum scoring can be performed on the corrected data. The discrimination function may require characteristic values, or may simply require that a certain amount of activity is present or absent in each of a plurality of frequency ranges. The discrimination function can simply be to match the envelope of the frequency response. Also, the discrimination function can see spectral fragmentation and temporary fragmentation.

[0056] A second normalization is performed in the frequency direction. The second normalization generates a doubly normalized spectrogram. Thereby, a new frequency space is generated in which the bands are even clearer. The values of the doubly normalized spectrogram can be used to generate a filter that maximally separates the values within the space.

[0057] Clustering techniques performed at doubly normalized frequencies. For example, the clustering technique may be a K-means technique as described in previous embodiments. Each cluster may represent an intended state.

[0058] The clusters are actually multi-dimensional clusters and can graph themselves to find additional information. The number of dimensions may depend on the number of clustering variables. This shows how the doubly normalized spectrogram enables more measurement characteristics.

[0059] It is also possible to have an average measurement spread in the power normalized in the frequency direction indicating spectral fragmentation. Alternatively, the fragmentation value can be based on the temporary fragmentation for different states and can also be used as part of a discriminant function.

[0060] These two functions are evaluated on the doubly normalized spectrum and depend on a uniform increase in gain at all frequencies, which would cause motion artifacts and result in abnormally elevated fragmentation values in a singly normalized spectrum. These fragmentation values can be used as part of a discriminant function. Importantly, as described above, this discriminant function is typically not obvious from any previous analysis techniques, including conventional manual techniques.

[0061] The calculation can be characterized by segmentation or overlapping windows or sliding windows can be used to increase the time alignment. This enables many techniques that were not previously possible. By performing the characterization on the fly, this makes it possible to distinguish between a relaxed state and an intended state using only electroencephalogram signals using the scoring of the dynamic spectrum.

[0062] The exemplary method for data analysis described above was combined with a standard non-invasive EEG method for humans. The result is the ability to non-invasively extract attenuated rhythms in animals, automatically analyze brain activity from a single-channel EEG, and adequately classify the brain state parameters of the animal.

Example

[0063] In a highly functional 70-year-old ALS patient who was attempting to move one of the limbs after a verbal cue (left and right hands and feet), a single-channel iBrain® EEG recording was performed. To enable detection of high-frequency / low-spectrum power signals, the raw EEG signals were partially analyzed with the SPEARS algorithm. A video recording was obtained simultaneously. During the attempted movement, the subject's brain activity showed distinct and extensive spectral pulses spanning the gamma and very high gamma ranges. Such pulses were present even without actual movement and were not present when the subject was not attempting movement. As expected, when the subject closed their eyes, activity in the alpha range was detected. By using such high-band biomarkers based on intentional movements towards a library of words, it becomes possible to translate the signals into speech, and thus a communication tool that depends more on the brain than on the body is provided to ALS patients.

[0064] Specifically, by applying the method of the present invention, high-frequency patterns that match the timing of the actual, imagined, or intended movements of the subject are revealed. In an exemplary application, a frequency spectrum is generated from time-series data, normalized, and these higher frequencies that are not revealed by standard methods are revealed. This application to brain EEG data is shown in FIG. 1. In this example, a 70-year-old subject with high-functioning ALS who cannot move was asked to close their eyes, relax, or imagine moving their hands or feet. For each of these tasks, the subject was given a verbal cue to start the task and another cue 4 seconds later to stop. The task was repeated after 6 seconds, for a total of 12 attempts and 120 seconds per task. FIG. 1 shows the standard frequency power spectrum of the ALS subject during these tasks in (i) and the enhanced frequency power spectrum in (ii). Stronger signals appear redder, while weaker signals decrease in intensity in the orange, yellow, and blue hues. A distinct band of high-frequency activity approximating the timing of the cues for the start and end of the task appears at high intensity in the enhanced spectrum. Panel A of FIG. 1 depicts this for the task of closing the eyes for 4 seconds. Panel B of FIG. 1 shows this for the task of imagining or attempting to squeeze the left hand for 4 seconds and relax for 6 seconds. Panels C, D, and E of FIG. 1 are similar but show this for the tasks of imagining or attempting to squeeze the right hand, left foot, and right foot, respectively. In all cases, the spectrogram created in this application clearly reveals the content of the high-frequency spectrum approximating the timing of the attempts by the subject to perform the instructed task. Panel F of FIG. 1 shows the same spectral analysis at rest for the same subject, displaying high-frequency content at very different timings, which may represent environmental noise, background talk, and / or baseline electrical activity.

Example

[0065] By applying the method of the present invention in the ALS patients described in Example 1, the components of the data approximating the time of the event are revealed. Figure 2 shows one such application of the method of the present invention (here, by the cause analysis of a single channel in a doubly normalized spectrogram) to reveal independent data components that are not related. A to E in Figure 2 are the data from the same tasks as shown in A to E in Figure 1. Each plot shows the resulting, extracted independent component having a peak approximating the individual task attempted by the subject after all the analyses described. The green line indicates the timing of the verbal cue to start the task for 4 seconds, and the red line indicates the time of the verbal cue to stop the task and relax for 6 seconds. The peak in each component generally coincides with the start of the task being performed (indicated by the peak point on, above, or immediately after the green line). Also, this analysis may be combined with the analysis in Example 1 to enhance or reinforce the detection and timing of the event.

Example

[0066] Provided is the application of the method of the present invention to single-channel brain EEG data for evaluating the data stability in ALS patients described in Example 1 and for revealing changes in the data stability indicative of the intended activities. Specifically, by applying the method of the present invention to generate temporary fragmentation, changes in stability approximating the timing and duration of the intended activities are revealed. A through E of FIG. 3 show the temporary fragmentation of the same tasks as shown in A through E of FIG. 1. In each plot, the shift of points from negative to positive represents a decrease in stability. These shifts approximate both the cue (green line) by words to start the task, where stability begins to decrease and the positive shift starts, and the cue (red line) by words to end the task, where the data begins to stabilize and the positive shift ends. The crossing of the zero line (purple dots) indicates that the unstable period increases (during or immediately after the task) and the count of the stable period decreases (relaxation periods before or after each task). Also, these shifts and the crossing of the lines may be combined with the method of Example 1 to enhance or reinforce event detection and timing.

Example

[0067] Detection of high-frequency events and event timing associated with low-frequency events in ALS patients of Example 1. Generation of spectrograms, subsequent normalization one or both data axes one or more times, subsequent sharpening of features using the doubly normalized spectrogram, and subsequent extraction of standard known frequencies, frequency ranges, their sums, their ratios, and / or other frequency relationships reveals event timing, intervals, and / or durations. A-E of FIG. 4 show the tasks in A-B of FIG. 1, plotting the sum of high-frequency power in the gamma and very high gamma (hgamma) ranges of frequencies (all greater than 30 Hz). In the EEG of the brain, 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 the tasks indicated by the columns of the green (task start) and red (task end) lines. A-D of FIG. 5 show the tasks in FIG. 1 regarding gripping the right hand, left hand, right foot, and left foot respectively, plotting the ratio of the sum of alpha frequency (8-13 Hz) power to the sum of gamma frequency power (30-50 Hz). Increased power at the alpha frequency in the brain EEG is associated with relaxation of mental effort such as after a period of attempting to grip a limb. Thus, alpha frequency power and gamma frequency power are inversely related with respect to these tasks, with peaks in the ratio of alpha to gamma appearing between tasks (between the red line indicating stop and the green line indicating restart). FIG. 5E shows an analysis of delta frequency (<5 Hz) during the task of closing the eyes. Delta frequency power is associated with changes in the open or closed state of the eyes and approximates the timing when the subject closes (green line) and opens (red line) the eyes. These frequency analyses and other frequency analyses may be combined with each other and / or with the methods in Examples 1, 2, and 3 to enhance or reinforce event detection and timing and to characterize events in known associated brain states indicated by the frequencies analyzed.

Example

[0068] Simultaneous detection of multiple events occurring simultaneously in a second ALS patient. Generation of spectrograms, subsequent normalization one or both data axes one or more times, subsequent sharpening of features, and subsequent extraction of standard known frequencies, frequency ranges, their sums, their ratios, and / or other frequency relationships reveals multiple events in one analysis. FIG. 8 shows data from a task designed to induce both an increase in concentration (increase in gamma frequency power) and an increase in a shift in thinking indicating a decrease and increase in relaxation (change in alpha frequency power). Paralyzed ALS patient subjects were instructed to alternately repeat two 10-second visualizations (kicking a football: green line, and looking at a bedroom: red line) five times. Panel A of FIG. 8 plots the normalized and enhanced gamma frequency power, showing a sharp increase at the start of the alternating sequence (first green line, 13 seconds) and a decrease at the end of all subsequent sequences (last red line, 103 seconds). Panel B of FIG. 8 plots the normalized and enhanced alpha frequency power, showing changes in the relaxation state between the two different visualizations (peaks between the colored lines). Panel C of FIG. 8 plots the normalized and enhanced alpha and gamma powers, showing the simultaneous detection of both a flat portion of the gamma frequency power during a series of visualizations and a peak in alpha when changing visualizations, as two distinct signals detected in one analysis. Panel D of FIG. 8 plots the normalized and enhanced ratio of alpha to very high gamma (hgamma), with a sharp drop in the ratio at the start of the sequence, individual peaks during each visualization, and an increase in the ratio after the sequence.

Example

[0069] Use of a repeatedly normalized spectrogram to distinguish and characterize multiple types of intentional events. Distinguishable imaginary motor movements are revealed by analysis including application of the SPEARS algorithm and subsequent two-sample Kolmogorov-Smirnov (KS) testing regarding sampling at the same distribution between any two spectrograms. By this application, multiple degrees of freedom are enabled based on at least one event type distinguishable from others. The p-values of the KS tests for the spectrograms normalized tenfold in the intersecting axial directions, derived from each of the tasks listed in A-E of FIG. 1 performed by immobile ALS subjects, are such that each spectrogram is from a distribution different from rest (P<0.01) (Table 1), and from the distribution of almost any other task (P<0.05) (FIG. 10), and, while not so much for the left hand and right foot in this particular trial, is shown to be an effective application for characterization of the same or multiple events at detection.

[0070] (Table 1) p-values of KS tests for imaginary tasks against rest TIFF2025100710000002.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 entirety, and to the extent that each individual publication, patent, and / or patent application is specifically and individually indicated to be incorporated by reference as if fully set forth herein, for the subject matter specifically referenced in the same or preceding sentences.

[0072] While only some embodiments have been disclosed in detail above, other embodiments are possible and the inventors intend for these to be included within this specification. This specification describes specific examples for achieving more general goals that could be achieved in another way. 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 has described characterizing the frequency in terms of its "preferred frequency" over a broad range, it should be understood that a more precise characterization of the information may also be possible. Also, while the above only mentions determining an intended state from EEG data and only mentions determining a few different types of intended states, it should be understood that other applications are conceivable.

[0073] Although the principles of the invention have been shown and described in exemplary embodiments, the described embodiments are illustrative and it will be apparent to one of ordinary skill in the art that the arrangements and details may be changed without departing from such principles. The techniques from any example may be incorporated into one or more of any other examples. The specification and examples are merely illustrative, and the true scope and spirit of the invention are intended to be indicated by the following claims.

Claims

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

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

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

5. The system according to claim 2, 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.

6. The analyzing further includes: performing principal component analysis and / or independent component analysis of the normalized spectrogram, according to claim 1.

7. The system according to claim 1, wherein the analysis of fragmentation includes an analysis of transient fragmentation.

8. The system according to claim 1, wherein the analyzing includes performing an analysis of a preferred frequency.

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

10. The system according to claim 1, wherein the analysis of fragmentation includes an analysis of spectral fragmentation.

11. The system according to claim 1, wherein the command further includes converting the data analyzed to perform a task associated with the higher cognitive function after the correlating, the task being selected from the group consisting of simulating, uttering on a display, simulating an utterance by a speech synthesizer, and moving a prosthesis.

12. The system according to claim 1, wherein the higher cognitive function is selected from the group consisting of intention, utterance, recollection, planned movement, thought, and imagination.

13. A method for detecting intentional brain signals from a subject, comprising: detecting data indicating brain activity of the subject; acquiring the data indicating brain activity from a sensor; analyzing the acquired data indicating brain activity, including calculating a spectrogram of the data, normalizing the spectrogram of the data at least once, and performing fragmentation analysis; correlating the analyzed data with an intentional higher cognitive function from the subject; and a method.

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

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

16. The method according to claim 15, wherein the data is acquired from at least a single channel of EEG.

17. The method according to 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 includes: performing principal component analysis and / or independent component analysis of the normalized spectrogram, the method according to claim 13.

19. The method according to claim 13, wherein the fragmentation analysis includes analysis of temporary fragmentation.

20. The method according to claim 13, wherein the analyzing includes analyzing a preferred frequency.

21. The method according to claim 20, wherein the analysis of the preferred frequency is performed on the spectrogram. **Claim 22** The method according to claim 13, wherein the analysis of the fragmentation includes analysis of spectral fragmentation. **Claim 23** After the correlating, further comprising converting the data analyzed for performing a task associated with the higher cognitive function, the task being selected from the group consisting of simulating, uttering on a display, simulating an utterance by a speech synthesizer, and moving a prosthesis, the method according to claim 13. **Claim 24** The method according to claim 13, wherein the higher cognitive function is selected from the group consisting of intention, utterance, recollection, planned movement, thought, and imagination. **Claim 25** A computer program product embodied in a non-transitory machine-readable storage medium including instructions, the instructions being to detect data indicative of brain activity of a subject; to obtain the data indicative of brain activity from a sensor; to analyze the obtained data indicative of brain activity, including calculating a spectrogram of the data, normalizing the spectrogram of the data at least once, and performing an analysis of fragmentation; to correlate the analyzed data with an intentional higher cognitive function from the subject; and configured to cause one or more data processors to perform a set of actions including. **Claim 26** The computer program product according to claim 25, wherein the data is obtained non-invasively by attaching the sensor to the subject. **Claim 27** The computer program product according to 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. **Claim 28** The computer program product according to claim 27, wherein the data is obtained from at least a single channel of EEG. **Claim 29** The computer program product according to 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 further analyzing includes performing principal component analysis and / or independent component analysis of the normalized spectrogram, for the computer program product according to claim 25.

31. The computer program product according to claim 25, wherein the analysis of the fragmentation includes analysis of transient fragmentation.

32. The computer program product according to claim 25, wherein the analyzing includes performing analysis of a preferred frequency.

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

34. The computer program product according to claim 25, wherein the analysis of the fragmentation includes analysis of spectral fragmentation.

35. After the correlating, further includes converting the data analyzed to execute a task associated with the higher cognitive function, the task being selected from the group consisting of simulating, uttering on a display, simulating an utterance by a speech synthesizer, and moving a prosthesis, for the computer program product according to claim 25.

36. The computer program product according to claim 25, wherein the higher cognitive function is selected from the group consisting of intention, utterance, recollection, planned movement, thought, and imagination.

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