TECHNICAL FIELD

[0001] The present disclosure generally relates to medical treatment, and particularly, to EEG guided treatment.

BACKGROUND ART

[0002] Cranial electrotherapy stimulation (CES) is a neuromodulation tool used for treating several clinical disorders, including insomnia, anxiety, and depression. CES involves delivering low-intensity (50 pA to 4 mA) electrical current via a pair of electrodes attached to bilateral anatomical positions around the head (for example, eyelids, earlobes, mastoids, temples, etc.) with an intent of acutely modulating central and/or peripheral nervous system activity [US Patents no. 9,079,030 B2, 9,433,773 B2, and 10,857,360 B2], However, almost all of CES systems generate electrical stimulation signals in only one shape (i.e., square waves) and with limited frequencies. There is, therefore, a need for a method that may provide customized stimulation signals in terms of frequencies, pulse shapes, and patterns according to different brain disorders.

SUMMARY OF THE DISCLOSURE

[0003] This summary is intended to provide an overview of the subject matter of this patent, and is not intended to identify essential elements or key elements of the subject matter, nor is it intended to be used to determine the scope of the claimed implementations. The proper scope of this patent may be ascertained from the claims set forth below in view of the detailed description below and the drawings.

[0004] In one general aspect, the present disclosure describes an exemplary method for electroencephalography (EEG) guided treatment based on cranial electrotherapy stimulation (CES). An exemplary method may include acquiring a pre-treatment EEG signal from a subject, extracting a pre-treatment feature vector from the pre-treatment EEG signal, generating a stimulation signal based on the pre-treatment feature vector, applying the stimulation signal to the subject, acquiring a post-treatment EEG signal from the subject after applying the stimulation signal to the subject, extracting a post-treatment feature vector from the post- treatment EEG signal, and determining a recovery progress of the subject based on the pretreatment feature vector and the post-treatment feature vector. In an exemplary embodiment, applying the stimulation signal to the subject may include placing each of at least two transcutaneous electrodes on a skin of a respective earlobe of the subject.

[0005] An exemplary method may further include applying an independent component analysis (ICA) to each of the pre-treatment EEG signal and the post-treatment EEG signal. An exemplary method may further include applying a current source density (CSD) analysis to each of the pre-treatment EEG signal and the post-treatment EEG signal.

[0006] In an exemplary embodiment, generating the stimulation signal based on the pretreatment feature vector may include finding K most similar embedded feature vectors among the plurality of embedded feature vectors to the pre-treatment feature vector by applying a K- nearest neighbors (K-NN) method to the pre-treatment feature vector and the plurality of embedded feature vectors where K is a constant integer, determining a plurality of signal features based on K embedded stimulation signals, and generating the stimulation signal according to the plurality of signal features. In an exemplary embodiment, each of the K embedded stimulation signals may be associated with a respective embedded feature vector of the K most similar embedded feature vectors.

[0007] An exemplary method may further include obtaining a plurality of embedded EEG signals from a patient EEG database. In an exemplary embodiment, each of the plurality of embedded EEG signals may be acquired from a respective patient of a plurality of patients. An exemplary method may further include extracting a respective embedded feature vector of a plurality of embedded feature vectors from each respective embedded EEG signal of the plurality of embedded EEG signals.

[0008] An exemplary method may further include obtaining a plurality of normal EEG signals from a normal EEG database. In an exemplary embodiment, each of the plurality of normal EEG signals may be acquired from a respective normal subject of a plurality of normal subjects. An exemplary method may further include extracting a respective normal feature vector of a plurality of normal feature vectors from each respective normal EEG signal of the plurality of normal EEG signals.

[0009] In an exemplary embodiment, determining the plurality of signal features based on the K embedded stimulation signals may include setting a signal type to a type of a majority of the K embedded stimulation signals, setting a signal orientation to an orientation of the majority of the K embedded stimulation signals, setting a signal frequency to a median of frequencies of the K embedded stimulation signals, setting signal extremum points to medians of respective extremum points of the K embedded stimulation signals, setting a signal segment length to a mean of segment lengths of the K embedded stimulation signals, and setting a signal duration to a mean of durations of the K embedded stimulation signals.

[0010] In an exemplary embodiment, determining the recovery progress may include calculating an average feature vector by averaging the plurality of normal feature vectors, obtaining a first difference between the pre-treatment feature vector and the average feature vector, obtaining a second difference between the post-treatment feature vector and the average feature vector, and determining the recovery progress responsive to the second difference being smaller than the first difference. An exemplary method may further include adding the pretreatment EEG signal to the plurality of embedded EEG signals responsive to the recovery progress being determined.

[0011] Other exemplary systems, methods, features and advantages of the implementations will be, or will become, apparent to one of ordinary skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description and this summary, be within the scope of the implementations, and be protected by the claims herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawing figures depict one or more implementations in accord with the present teachings, by way of example only, not by way of limitation. In the figures, like reference numerals refer to the same or similar elements.

[0013] FIG. 1A shows a flowchart of a method for electroencephalography (EEG) guided treatment based on cranial electrotherapy stimulation (CES), consistent with one or more exemplary embodiments of the present disclosure.

[0014] FIG. IB shows a flowchart for generating a stimulation signal based on a pre-treatment feature vector, consistent with one or more exemplary embodiments of the present disclosure. [0015] FIG. 1C shows a flowchart for determining a recovery progress of a subject, consistent with one or more exemplary embodiments of the present disclosure.

[0016] FIG. 2 shows a schematic of a system for EEG guided treatment based on CES, consistent with one or more exemplary embodiments of the present disclosure. [0017] FIG. 3 shows a stimulation signal, consistent with one or more exemplary embodiments of the present disclosure.

[0018] FIG. 4 shows a high-level functional block diagram of a computer system, consistent with one or more exemplary embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

[0019] In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. However, it should be apparent that the present teachings may be practiced without such details. In other instances, well known methods, procedures, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present teachings.

[0020] The following detailed description is presented to enable a person skilled in the art to make and use the methods and devices disclosed in exemplary embodiments of the present disclosure. For purposes of explanation, specific nomenclature is set forth to provide a thorough understanding of the present disclosure. However, it will be apparent to one skilled in the art that these specific details are not required to practice the disclosed exemplary embodiments. Descriptions of specific exemplary embodiments are provided only as representative examples. Various modifications to the exemplary implementations will be readily apparent to one skilled in the art, and the general principles defined herein may be applied to other implementations and applications without departing from the scope of the present disclosure. The present disclosure is not intended to be limited to the implementations shown, but is to be accorded the widest possible scope consistent with the principles and features disclosed herein.

[0021] Herein are disclosed an exemplary method for encephalography (EEG) guided treatment based on cranial electrotherapy stimulation (CES). An exemplary method may record an EEG signal of a subject under treatment. Exemplary features may be extracted from the subject to be compared with previously obtained features that have been extracted from EEG signals from several patients with different brain disorders that have undergone successful treatments by brain stimulation. Based on comparison results, a number of stimulation signals may be selected that have been applied to patients with more similar features to the features obtained from the subject under treatment. An exemplary stimulation signal may then be constructed according to features of the selected stimulation signals to be applied to the subject. The treatment result may then be evaluated by studying an EEG signal of the subject that is recorded after the exemplary stimulation signal is applied to the subject. If a post-treatment EEG signal shows a recovery progress of an exemplary subject, the EEG signal of the subject that is recorded prior to the treatment procedure may be stored among the previously gathered EEG signals to be utilized in future treatments of patients with similar brain disorders.

[0022] FIG. 1A shows a flowchart of a method for electroencephalography (EEG) guided treatment based on cranial electrotherapy stimulation (CES), consistent with one or more exemplary embodiments of the present disclosure. An exemplary method 100 may include acquiring a pre-treatment EEG signal from a subject (step 102), extracting a pre-treatment feature vector from the pre-treatment EEG signal (step 104), generating a stimulation signal based on the pre-treatment feature vector (step 106), applying the stimulation signal to the subject (step 108), acquiring a post-treatment EEG signal from the subject after applying the stimulation signal to the subject (step 110), extracting a post-treatment feature vector from the post-treatment EEG signal (step 112), and determining a recovery progress of the subj ect based on the pre-treatment feature vector and the post-treatment feature vector (step 114).

[0023] FIG. 2 shows a schematic of a system for EEG guided treatment based on CES, consistent with one or more exemplary embodiments of the present disclosure. An exemplary system 200 may include a plurality of EEG electrodes 202, a signal generator 204, at least two transcutaneous electrodes 206, and a processor 208. Each exemplary transcutaneous electrode (for example, a transcutaneous electrode 206A) may be placed on a skin of a respective earlobe (for example, an earlobe 210) of a subject 212. In an exemplary embodiment, different steps of method 100 may be implemented utilizing system 200.

[0024] In an exemplary embodiment, step 102 may include acquiring a pre-treatment EEG signal from subject 212 utilizing plurality of EEG electrodes 202. In an exemplary embodiment, plurality of EEG electrodes 202 may include 19 electrodes (i.e., a 19-channel EEG setup) that may be placed on the head of subject 212 to acquire the pre-treatment EEG signal.

[0025] In an exemplary embodiment, method 100 may further include applying an independent component analysis (ICA) to the pre-treatment EEG signal after the pre-treatment EEG signal is acquired from subject 212 in step 102. An exemplary ICA may include decomposing the pre-treatment EEG signal to a plurality of components. As a result, a finite number of lower complexity series compared to the original EEG signal may be generated. An exemplary score may then be obtained for each component according to a set of operations defined by the following: Equation (la)

Sj = \Mj \ Equation (lb) where M) is a 7 ^th set of a plurality of sets where 1 < j < J and J is a number of the plurality of components, x _t is an I ^th sample of a 7 ^th component Xj, x is an arbitrary sample of 7 ^th component Xj, p is an upper threshold, and Sj is a 7 ^th score of a plurality of scores. In an exemplary embodiment, upper threshold p may be set to a median of the pre-treatment EEG signal.

[0026] According to Equation (la), a separate set may be produced for each exemplary component of the plurality of components. According to Equation (lb), score Sj of 7 ^th component ry may be the size of 7 ^th setM) that is produced for 7 ^th component Xj. Therefore, in an exemplary embodiment, each score of the plurality of scores may be obtained for a different component of the plurality of components. In an exemplary embodiment, number / of the plurality of components may be set to 10. As a result, an exemplary ICA may decompose the pre-treatment EEG signal to 10 components.

[0027] In an exemplary embodiment, 7 ^th component Xj may be removed from the plurality of components responsive to 7 ^th score Sj being among m largest scores of the plurality of scores where 1 < m < J. To do so, an exemplary plurality of components may be sorted in a list according to the plurality of scores in a descending order and top m components in the list (corresponding to the m largest scores) may be removed. As a result, in an exemplary embodiment, 7 ^th component Xj may be removed from the plurality of components if 7 ^th score Sj is among the m largest scores of the plurality of scores. In an exemplary embodiment, number m may be set to 2. As a result, two exemplary components of the plurality of components may be considered noise and may be removed from the plurality of components. An exemplary pre-treatment EEG signal may be then restored by recombining the plurality of components that remain in the list after removing the noise components.

[0028] In an exemplary embodiment, method 100 may further include applying a current source density (CSD) analysis to the pre-treatment EEG signal. In many EEG records there may be sharp picks and multi-channel local extremums. In an exemplary embodiment, CSD analysis may take the spatial Laplacian of recorded EEG signals. Computing these spatial derivatives may reduce point spread, resulting in sharper or more distinct topographically transformed data, and providing a way to reduce impact of volume conduction.

[0029] Referring again to FIG. 1A, in an exemplary embodiment, step 104 may include extracting the pre-treatment feature vector from the pre-treatment EEG signal. An exemplary pre-treatment feature vector may include a plurality of features. An exemplary a plurality of features may include a z-score of the pre-treatment EEG signal. An exemplary z-score may be calculated according to an operation defined by the following:

Xi — u.

Z = AGG(— — - Equation (2) where Z is the z-score, AGG() is an aggregation function, is an I ^th sample of the pretreatment EEG signal where 1 < i < N and N is a number of samples of the pre-treatment EEG signal, /J. is a mean value of the pre-treatment EEG signal, and a is a standard deviation of the pre-treatment EEG signal. An exemplary aggregation function may output a median, a mean, or a concatenation of input arguments of the aggregation function.

[0030] In an exemplary embodiment, the plurality of features may further include a harmonic mean of the pre-treatment EEG signal. An exemplary harmonic mean may be calculated according to an operation defined by the following:

Equation (3) where H is the harmonic mean.

[0031] In an exemplary embodiment, the plurality of features may further include a kurtosis of the pre-treatment EEG signal. An exemplary kurtosis may be calculated according to an operation defined by the following:

Equation (4) where Kurt is the kurtosis, IE is an expectation operator, and X is the pre-treatment EEG signal.

[0032] In an exemplary embodiment, the plurality of features may further include a skewness of the pre-treatment EEG signal. An exemplary skewness may be calculated according to an operation defined by the following: Equation (5) where p. is the skewness.

[0033] In an exemplary embodiment, the plurality of features may further include a root mean square of the pre-treatment EEG signal. An exemplary root mean square may be calculated according to an operation defined by the following:

Equation (6) where RMS is the root mean square.

[0034] In an exemplary embodiment, the plurality of features may further include a margin of the pre-treatment EEG signal. An exemplary margin may be calculated according to an operation defined by the following:

Equation (7) where Mis the margin.

[0035] In an exemplary embodiment, the plurality of features may further include an entropy of the pre-treatment EEG signal. An exemplary entropy may be calculated according to an operation defined by the following:

Equation (8) where S is the entropy.

[0036] In an exemplary embodiment, the plurality of features may further include a median absolute deviation of the pre-treatment EEG signal. An exemplary median absolute deviation may be calculated according to an operation defined by the following:

MAD = med{|%i — | | 1 < i < N } Equation (9) where MAD is the median absolute deviation and med{] is a median operator.

[0037] In an exemplary embodiment, the plurality of features may further include a Spearman correlation of the pre-treatment EEG signal. An exemplary Spearman correlation may be calculated according to an operation defined by the following:

Equation (10) where r _s is the Spearman correlation, R() is a rank variable, and is a projection of the I ^th sample of the pre-treatment EEG signal.

[0038] In an exemplary embodiment, the plurality of features may further include a Lomb- Scargle periodogram of the pre-treatment EEG signal. An exemplary Lomb-Scargle periodogram may be calculated by solving an equation defined by the following: where a> is an angular frequency, t _£ is an I ^th time instance, T is a time offset, and P/v(<^) is the Lomb-Scargle periodogram given by: Equation (12) where a> _n is an n ^th sample of angular frequency a> and k is a constant integer. In an exemplary embodiment, Equation (11) may be solved for angular frequency a> to obtain the Lomb- Scargle periodogram. An exemplary Lomb-Scargle periodogram may provide frequency domain features that may be more easily reproduced than temporal features that may have to be extracted from large scale time series of EEG recordings.

[0039] In an exemplary embodiment, method 100 may further include obtaining a plurality of embedded EEG signals from a patient EEG database. In an exemplary embodiment, each of the plurality of embedded EEG signals may be acquired from a respective patient of a plurality of patients. In an exemplary embodiment, a different EEG signal may be acquired from each different patient to be stored in the patient EEG database. An exemplary patient EEG database may be provided prior to step 102 described above. In an exemplary embodiment, processes similar to ICA and CSD (described above for the pre-treatment EEG signal) may be applied to each embedded EEG signal to enhance the quality of the plurality of embedded EEG signals.

[0040] In an exemplary embodiment, method 100 may further include extracting a respective embedded feature vector of a plurality of embedded feature vectors from each respective embedded EEG signal of the plurality of embedded EEG signals. In an exemplary embodiment, a different embedded feature vector may be extracted from each different embedded EEG signal. As a result, in an exemplary embodiment, a separate feature vector may be provided for each different patient of the plurality of patients. An exemplary embedded feature vector may include features similar to the plurality of features described above in Equations (la)-(12).

[0041] In further detail regarding step 106, FIG. IB shows a flowchart for generating a stimulation signal based on a pre-treatment feature vector, consistent with one or more exemplary embodiments of the present disclosure. In an exemplary embodiment, generating the stimulation signal in step 106 may include finding K most similar embedded feature vectors among the plurality of embedded feature vectors to the pre-treatment feature vector where K is a constant integer (step 116), determining a plurality of signal features based on the K most similar embedded feature vectors (step 118), and generating the stimulation signal according to the plurality of signal features (step 120). In an exemplary embodiment, each of the K embedded stimulation signals may be associated with a respective embedded feature vector of the K most similar feature vectors.

[0042] In an exemplary embodiment, step 116 may include finding K most similar embedded feature vectors to the pre-treatment feature vector. To obtain exemplary K most similar embedded feature vectors, a K-nearest neighbors (K-NN) method - which is a well-known machine learning method - may be applied to the pre-treatment feature vector and the plurality of embedded feature vectors. An exemplary value of K may be set to 3. Therefore, in an exemplary embodiment, three vectors from the plurality of embedded feature vectors that are more similar to the pre-treatment feature vector than other vectors in the plurality of embedded feature vectors may be obtained in step 116.

[0043] In further detail regarding step 118, FIG. 3 shows a stimulation signal, consistent with one or more exemplary embodiments of the present disclosure. An exemplary stimulation signal 300 may have a plurality of signal features. An exemplary plurality of features may include signal type, signal orientation, signal frequency, signal extremum points, signal segment length, and signal duration. In an exemplary embodiment, signal type may include a shape of stimulation signal 300 (for example, a sine wave, a biphasic square pattern, a triangular waveform, a saw tooth waveform, white noise, etc.). An exemplary signal orientation may include a movement direction of a lower edge of stimulation signal 300. In an exemplary embodiment, signal frequency may be defined as an inverse of a signal period 302. Exemplary signal extremum points may include points of stimulation signal 300 that have largest (such as a point 304) or smallest (such as a point 306) amplitudes. In an exemplary embodiment, a signal segment length 308 may refer to a length of each segment of stimulation signal 300. An exemplary signal duration may refer to a duration of stimulation signal 300 that may be applied to a subject in a treatment session.

[0044] In an exemplary embodiment, step 118 may include determining the plurality of signal features based on the K most similar embedded feature vectors. In an exemplary embodiment, each of the K most similar feature vectors may be extracted from a separate embedded EEG signal that has been recorded from a separate patient and has been stored in the patient EEG database. Each exemplary patient with a recorded EEG signal in the EEG database may have undergone one or more treatment sessions. Therefore, in an exemplary embodiment, a different stimulation signal may have been applied to each different patient of the plurality of patients. Each exemplary stimulation signal may have been embedded in the patient EEG database and may be referred to as an embedded stimulation signal. As a result, each exemplary embedded stimulation signal may correspond to a different embedded feature vector of the plurality of embedded feature vectors. In an exemplary embodiment, each of the K most similar embedded feature vectors may correspond to a different embedded stimulation signal of a number of K embedded stimulation signals. In other words, in an exemplary embodiment, each of the K embedded stimulation signals may have been applied to a patient with a recorded EEG signal from which a corresponding embedded feature vector of the K most similar embedded feature vectors has been extracted. Therefore, in an exemplary embodiment, the K embedded stimulation signals may correspond to the K most similar embedded feature vectors.

[0045] In an exemplary embodiment, determining the plurality of signal features based on the K most similar embedded feature vectors in step 118 may include determining the plurality of signal features based on a plurality of embedded features that may be obtained from the K embedded stimulation signals. For this purpose, an exemplary signal type of stimulation signal 300 may be set to a type of a majority of the K embedded stimulation signals. In addition, an exemplary signal orientation of stimulation signal 300 may be set to an orientation of the majority of the K embedded stimulation signals. Furthermore, an exemplary signal frequency of stimulation signal 300 may be set to a median of frequencies of the K embedded stimulation signals. Also, exemplary signal extremum points of stimulation signal 300 may be set to medians of respective extremum points of the K embedded stimulation signals. For example, position and amplitude of point 304 may be set to a median of corresponding extremum points of the K embedded stimulation signals. In an exemplary embodiment, signal segment length 308 may be set to a mean of segment lengths of the K embedded stimulation signals. Moreover, an exemplary signal duration of stimulation signal 300 may be set to a mean of durations of the K embedded stimulation signals.

[0046] Referring to FIGs. IB, 2, and 3, in an exemplary embodiment, step 120 may include generating stimulation signal 300 according to the plurality of signal features. For this purpose, in an exemplary embodiment, processor 208 may determine a waveform of stimulation signal 300 according to the plurality of signal features. An exemplary waveform may then be sent from processor 208 to signal generator 204. In an exemplary embodiment, signal generator 204 may generate an electric signal that may have a shape of the waveform.

[0047] Referring to FIGs. 1A and 2, and 3, in an exemplary embodiment, step 108 may include applying stimulation signal 300 to subject 212. In an exemplary embodiment, stimulation signal 300 may be applied to subject 212 through transcutaneous electrodes 206. Each exemplary transcutaneous electrode (for example, transcutaneous electrode 206A) may deliver a small, pulsed, alternating current to subject 212 via subject 212 earlobes, similar to conventional CES.

[0048] In an exemplary embodiment, step 110 may include acquiring the post-treatment EEG signal from subject 212 after applying stimulation signal 300 to subject 212. Similar to acquiring an exemplary pre-treatment EEG signal in step 102, plurality of EEG electrodes 202 may be utilized to record the post-treatment EEG signal. An exemplary post-treatment EEG signal may be recorded from subject 212 to evaluate the impact of stimulation signal 300 on subject 212, thereby determining whether the treatment of subject 212 by method 100 may have been effective. In an exemplary embodiment, processes similar to ICA and CSD (described above for the pre-treatment EEG signal) may be applied to the post-treatment EEG signal to enhance the quality of the post-treatment EEG signal.

[0049] In an exemplary embodiment, step 112 may include extracting the post-treatment feature vector from the post-treatment EEG signal. In an exemplary embodiment, the posttreatment feature vector may be extracted from the post-treatment EEG signal similar to extracting pre-treatment feature vector from the pre-treatment EEG signal in step 104. An exemplary post-treatment feature vector may include features similar to the plurality of features described above in Equations (la)-(12).

[0050] In an exemplary embodiment, method 100 may further include obtaining a plurality of normal EEG signals from a normal EEG database. In an exemplary embodiment, each of the plurality of normal EEG signals may be acquired from a respective normal subject of a plurality of normal subjects. In an exemplary embodiment, a different EEG signal may be acquired from each different normal subject. In an exemplary embodiment, a “normal subject” may refer to a subject without any known brain disorder or a related symptom in an EEG signal recorded from the subject. An exemplary EEG signal that is acquired from a normal subject may be referred to as a “normal EEG signal.” Each exemplary normal EEG signal may be stored in a “normal EEG database.” An exemplary normal EEG database may be provided prior to step 102 described above and may include more than about 150,000 EEG data. In an exemplary embodiment, processes similar to ICA and CSD (described above for the pre-treatment EEG signal) may be applied to each normal EEG signal to enhance the quality of the plurality of normal EEG signals.

[0051] In an exemplary embodiment, method 100 may further include extracting a respective normal feature vector of a plurality of normal feature vectors from each respective normal EEG signal of the plurality of normal EEG signals. In an exemplary embodiment, a different normal feature vector may be extracted from each different normal EEG signal. As a result, in an exemplary embodiment, a separate normal feature vector may be provided for each different normal subject of the plurality of normal subjects. An exemplary normal feature vector may include features similar to the plurality of features described above in Equations (la)-(12).

[0052] In further detail with respect to step 114, FIG. 1C shows a flowchart for determining a recovery progress of a subject, consistent with one or more exemplary embodiments of the present disclosure. In an exemplary embodiment, determining the recovery progress may include calculating an average feature vector (step 122), obtaining a first difference between the pre-treatment feature vector and the average feature vector (step 124), obtaining a second difference between the post-treatment feature vector and the average feature vector (step 126), and determining the recovery progress responsive to the second difference being smaller than the first difference (step 128).

[0053] In an exemplary embodiment, step 122 may include calculating the average feature vector. An exemplary average feature vector may be obtained by averaging the plurality of normal feature vectors. For this purpose, an exemplary average of corresponding elements of each of the plurality of normal feature vectors may be calculated. In an exemplary embodiment, the average feature vector may be utilized as a measure of a normal feature vector, i.e., a feature vector that may be extracted from a normal EEG signal as defined above. [0054] In an exemplary embodiment, step 124 may include obtaining a first difference between the pre-treatment feature vector and the average feature vector. An exemplary first difference may be obtained by calculating a Euclidean distance between the pre-treatment feature vector and the average feature vector. In an exemplary embodiment, the first difference may show a deviation of the pre-treatment EEG signal (i.e., brain functionality of subject 212 before undergoing a treatment by method 100) from a normal EEG signal (i.e., brain functionality of a healthy subject).

[0055] In an exemplary embodiment, step 126 may include obtaining a second difference between the post-treatment feature vector and the average feature vector. An exemplary second difference may be obtained by calculating a Euclidean distance between the post-treatment feature vector and the average feature vector. In an exemplary embodiment, the second difference may show a deviation of the post-treatment EEG signal (i.e., brain functionality of subject 212 after undergoing a treatment by method 100) from a normal EEG signal (i.e., brain functionality of a healthy subject).

[0056] In an exemplary embodiment, step 128 may include determining the recovery progress responsive to the second difference being smaller than the first difference. If an exemplary second difference is smaller than the first difference, it may be deduced that the post-treatment EEG signal is closer to a normal EEG signal than the pre-treatment EEG signal. As a result, in an exemplary embodiment, a smaller second difference than the first difference may be a sign of a recovery progress as a result of applying method 100 to subject 212. Therefore, in an exemplary embodiment, the first difference and the second difference may be compared in step 128. If an exemplary second difference is smaller than the first difference, a recovery progress may be determined in a treatment of subject 212 by method 100. In an exemplary embodiment, the pre-treatment EEG signal may be added to the plurality of embedded EEG signals responsive to the recovery progress being determined. In other words, an exemplary pretreatment EEG signal may be stored in the patient EEG database to be used in future applications of method 100 if the post-treatment EEG signal shows a progress in the recovery of subject 212 so that pre-treatment EEG signal may be utilized for treatment of other exemplary patients with brain disorders.

[0057] FIG. 4 shows an example computer system 400 in which an embodiment of the present invention, or portions thereof, may be implemented as computer-readable code, consistent with exemplary embodiments of the present disclosure. For example, different steps of method 100 may be implemented in computer system 400 using hardware, software, firmware, tangible computer readable media having instructions stored thereon, or a combination thereof and may be implemented in one or more computer systems or other processing systems. Hardware, software, or any combination of such may embody any of the modules and components in FIGs. 1A-2, for example, processor 208 in FIG. 2.

[0058] If programmable logic is used, such logic may execute on a commercially available processing platform or a special purpose device. One ordinary skill in the art may appreciate that an embodiment of the disclosed subject matter can be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device.

[0059] For instance, a computing device having at least one processor device and a memory may be used to implement the above-described embodiments. A processor device may be a single processor, a plurality of processors, or combinations thereof. Processor devices may have one or more processor “cores.”

[0060] An embodiment of the invention is described in terms of this example computer system 400. After reading this description, it will become apparent to a person skilled in the relevant art how to implement the invention using other computer systems and/or computer architectures. Although operations may be described as a sequential process, some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multiprocessor machines. In addition, in some embodiments the order of operations may be rearranged without departing from the spirit of the disclosed subject matter.

[0061] Processor device 404 may be a special purpose (e.g., a graphical processing unit) or a general -purpose processor device. As will be appreciated by persons skilled in the relevant art, processor device 404 may also be a single processor in a multi-core/multiprocessor system, such system operating alone, or in a cluster of computing devices operating in a cluster or server farm. Processor device 404 may be connected to a communication infrastructure 406, for example, a bus, message queue, network, or multi-core message-passing scheme.

[0062] In an exemplary embodiment, computer system 400 may include a display interface 402, for example a video connector, to transfer data to a display unit 430, for example, a monitor. Computer system 700 may also include a main memory 408, for example, random access memory (RAM), and may also include a secondary memory 410. Secondary memory 410 may include, for example, a hard disk drive 412, and a removable storage drive 414. Removable storage drive 414 may include a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, or the like. Removable storage drive 414 may read from and/or write to a removable storage unit 418 in a well-known manner. Removable storage unit 418 may include a floppy disk, a magnetic tape, an optical disk, etc., which may be read by and written to by removable storage drive 414. As will be appreciated by persons skilled in the relevant art, removable storage unit 418 may include a computer usable storage medium having stored therein computer software and/or data.

[0063] In alternative implementations, secondary memory 410 may include other similar means for allowing computer programs or other instructions to be loaded into computer system 700. Such means may include, for example, a removable storage unit 422 and an interface 420. Examples of such means may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 422 and interfaces 420 which allow software and data to be transferred from removable storage unit 422 to computer system 400. [0064] Computer system 400 may also include a communications interface 424. Communications interface 424 allows software and data to be transferred between computer system 400 and external devices. Communications interface 424 may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, or the like. Software and data transferred via communications interface 424 may be in the form of signals, which may be electronic, electromagnetic, optical, or other signals capable of being received by communications interface 424. These signals may be provided to communications interface 424 via a communications path 426. Communications path 426 carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link or other communications channels.

[0065] In this document, the terms “computer program medium” and “computer usable medium” are used to generally refer to media such as removable storage unit 418, removable storage unit 422, and a hard disk installed in hard disk drive 412. Computer program medium and computer usable medium may also refer to memories, such as main memory 408 and secondary memory 410, which may be memory semiconductors (e.g. DRAMs, etc.). [0066] Computer programs (also called computer control logic) are stored in main memory 708 and/or secondary memory 410. Computer programs may also be received via communications interface 424. Such computer programs, when executed, enable computer system 400 to implement different embodiments of the present disclosure as discussed herein. In particular, the computer programs, when executed, enable processor device 404 to implement the processes of the present disclosure, such as the operations in method 100 illustrated by flowcharts of FIGs. 1A-1C discussed above. Accordingly, such computer programs represent controllers of computer system 400. Where an exemplary embodiment of method 100 is implemented using software, the software may be stored in a computer program product and loaded into computer system 400 using removable storage drive 414, interface 420, and hard disk drive 412, or communications interface 424.

[0067] Embodiments of the present disclosure also may be directed to computer program products including software stored on any computer useable medium. Such software, when executed in one or more data processing device, causes a data processing device to operate as described herein. An embodiment of the present disclosure may employ any computer useable or readable medium. Examples of computer useable mediums include, but are not limited to, primary storage devices (e.g., any type of random access memory), secondary storage devices (e.g., hard drives, floppy disks, CD ROMS, ZIP disks, tapes, magnetic storage devices, and optical storage devices, MEMS, nanotechnological storage device, etc.).

[0068] The embodiments have been described above with the aid of functional building blocks illustrating the implementation of specified functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed.

[0069] While the foregoing has described what are considered to be the best mode and/or other examples, it is understood that various modifications may be made therein and that the subject matter disclosed herein may be implemented in various forms and examples, and that the teachings may be applied in numerous applications, only some of which have been described herein. It is intended by the following claims to claim any and all applications, modifications and variations that fall within the true scope of the present teachings.

[0070] Unless otherwise stated, all measurements, values, ratings, positions, magnitudes, sizes, and other specifications that are set forth in this specification, including in the claims that follow, are approximate, not exact. They are intended to have a reasonable range that is consistent with the functions to which they relate and with what is customary in the art to which they pertain.

[0071] The scope of protection is limited solely by the claims that now follow. That scope is intended and should be interpreted to be as broad as is consistent with the ordinary meaning of the language that is used in the claims when interpreted in light of this specification and the prosecution history that follows and to encompass all structural and functional equivalents.

[0072] Except as stated immediately above, nothing that has been stated or illustrated is intended or should be interpreted to cause a dedication of any component, step, feature, object, benefit, advantage, or equivalent to the public, regardless of whether it is or is not recited in the claims.

[0073] It will be understood that the terms and expressions used herein have the ordinary meaning as is accorded to such terms and expressions with respect to their corresponding respective areas of inquiry and study except where specific meanings have otherwise been set forth herein. Relational terms such as first and second and the like may be used solely to distinguish one entity or action from another without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “a” or “an” does not, without further constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0074] The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various implementations. This is for purposes of streamlining the disclosure, and is not to be interpreted as reflecting an intention that the claimed implementations require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed implementation. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.

[0075] While various implementations have been described, the description is intended to be exemplary, rather than limiting and it will be apparent to those of ordinary skill in the art that many more implementations and implementations are possible that are within the scope of the implementations. Although many possible combinations of features are shown in the accompanying figures and discussed in this detailed description, many other combinations of the disclosed features are possible. Any feature of any implementation may be used in combination with or substituted for any other feature or element in any other implementation unless specifically restricted. Therefore, it will be understood that any of the features shown and/or discussed in the present disclosure may be implemented together in any suitable combination. Accordingly, the implementations are not to be restricted except in light of the attached claims and their equivalents. Also, various modifications and changes may be made within the scope of the attached claims