DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1-15 are objected to because of the following informalities:
Line 1 of claims 2-15 should recite “the SSVEP electroencephalogram signal”.
Claim 1 line 1 “SSVEP” should recite “steady-state visually evoked potential (SSVEP)”.
Claim 1 line 2 should recite “an interactive terminal”.
Claim 1 line 3 should recite “a patient”.
Claim 1 line 3 should recite “an acquisition terminal”.
Claim 1 line 26 should recite “each channel of the multichannel signal”.
Claim 3 lines 2-3 should recite “wherein the defining the reference signal according to the visual stimulus frequency for evoking the first electroencephalogram signal in the step S1”.
Claim 4 line 17 should recite “the one band-pass filter”.
Claim 5 lines 2-4 should recite “wherein the step S2 of calculating differences of the sub-band components, the difference of the reference signal and differences between the sub-band components and the reference signal, and acquiring the total spatial filter of the sub-band components”.
Claim 7 lines 2-3 should recite “herein the obtaining the total spatial filter of the sub-bands”.
Claim 8 lines 3-5 should recite “rearranging the sub-band components into the new multi-channel signal, and recording the new multi-channel signal as the second electroencephalogram signal”.
Claim 9 lines 2-6 should recite “acquiring the plurality of variation modal components of the second electroencephalogram signal by adopting variation modal decomposition, and optimizing weights of the variation modal components under each channel to reconstruct the electroencephalogram signal to obtain the third electroencephalogram signal”.
Claim 10 lines 2-3 should recite “determining the number P of the variation modal components based on the variation trend”.
Claim 11 lines 2-3 should recite “constructing the variation model based on the number P of the variation modal components, and solving the variation model by adopting the alternating direction multiplier algorithm”.
Claim 12 lines 2-5 should recite “S44 of weighting the variation modal components of each frequency band by adopting the sparrow search algorithm, weights of the variation modal components being determined according to the fitness function value of the sparrow search algorithm”.
Claim 14 lines 2-5 should recite “calculating the Pearson coefficient of the third electroencephalogram signal and the average signal of the third electroencephalogram signal and the Pearson coefficient of the third electroencephalogram signal and the reference signal by adopting the typical correlation”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 line 6 recites the limitation "the patient’s expression intention .... expression intentions". It is unclear if the patient’s expression intention/expression intentions is/are related to or different from the plurality of the candidate intention options. For examination purposes the patient’s expression intention/expression intentions will be treated as being related to the plurality of the candidate intention options. The limitation is suggested to recite “determining an expression intention of the patient from the plurality of candidate intention options, the plurality of candidate intention options comprising”.
Claim 1 line 11 recites the limitation "a patient". It is unclear if this patient is different or the same patient as recited in line 3. The limitation is suggested to recite “the patient”.
Claim 1 line 29 recites “own features”. It is unclear what the own features of the third electroencephalogram signal are and further it is unclear how a correlation coefficient based on own features is obtained when the own features of the third electroencephalogram signal are not previously extracted. Clarification is requested.
Claim 1 lines 32-33 recite “an intelligent ward round”. It is unclear if this is the same or different intelligent ward round than recited in line 9. The limitation is suggested to recite “the intelligent ward round”.
Claims 2-15 are rejected by virtue of depending from claim 1.
Claim 5 line 10 recites the limitation "acquiring the spatial filter of the sub-band component". There is insufficient antecedent basis for this limitation in the claim. The limitation is suggested to recite “acquiring a spatial filter of each sub-band component”.
Claim 7 line 10 recites the limitation "the final spatial filter". There is insufficient antecedent basis for this limitation in the claim. The limitation is suggested to recite “a final spatial filter” or “the total spatial filter”.
Claim 8 line 7 recites the limitation "the y axis". There is insufficient antecedent basis for this limitation in the claim. The limitation is suggested to recite “a y-axis”.
Claim 8 line 8 recites the limitation "the Nf spatial filters". There is insufficient antecedent basis for this limitation in the claim. The limitation is suggested to recite “a sequence of Nf spatial filters”.
Claim 8 line 9 recites the limitation "the K sub-band components along the z axis". There is insufficient antecedent basis for this limitation in the claim. The limitation is suggested to recite “recombining K sub-band components along a z axis”.
Claim 8 line 10 recites “a new signal”. It is unclear if this is the same or different signal than the multi-channel signal. The limitation is suggested to recite “the new multi-channel signal”.
Claim 10 line 6 recites the limitation "the channel number". There is insufficient antecedent basis for this limitation in the claim. The limitation is suggested to recite “a channel number”.
Claim 12 line 7 recites “a fitness function”. It is unclear if this is the same or different fitness function than recited in claim 9. For examination purposes it will be treated as the same fitness function and the limitation is suggested to recite “the fitness function”.
Claim Rejections - 35 USC § 101
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
STEP 1
Regarding claim 1, the claim recites a series of steps or acts. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of:
S2: decomposing the first electroencephalogram signal into sub-band components distributed in different frequency ranges, calculating differences of the sub-band components, a difference of the reference signal and differences between the sub-band components and the reference signal, and acquiring a total spatial filter of the sub-band components;
S3: processing each sub-band component by the total spatial filter, rearranging the sub-band components into a new multi-channel signal, and recording the new multi-channel signal as a second electroencephalogram signal;
S4: acquiring a plurality of variation modal components of the second electroencephalogram signal by adopting variation modal decomposition, and optimizing weights of the variation modal components under each channel to reconstruct an electroencephalogram signal to obtain a third electroencephalogram signal; and
S5: determining a correlation coefficient based on own features of the third electroencephalogram signal and the reference signal to obtain an evoking stimulus frequency of the third electroencephalogram signal, and determining an intention of the patient based on the evoking stimulus frequency to complete an intelligent ward round.
set forth a judicial exception. These steps describe a concept performed in the human mind (including an observation, evaluation, judgment, opinion).
These steps describe a concept performed in the human mind (including an observation, evaluation, judgment, opinion) (decomposing, calculating, determining) and/or mathematical concepts (including mathematical relationships, mathematical formulas or equations, and mathematical calculations) (decomposing, calculating differences, processing, total spatial filter, variation modal components, variation modal decomposition, determining a correlation coefficient). Thus, the claim is drawn to a Mental Process and/or Mathematical concepts, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites the interactive terminal used to present a visual stimulus source, the acquisition terminal used to collect the patient’s electroencephalogram signal, the electroencephalogram signal transmitted to the interactive terminal, and S1: acquiring a preprocessed electroencephalogram signal of a patient under a stimulus of a current visual stimulus source, recording the preprocessed electroencephalogram signal as a first electroencephalogram signal, and defining a reference signal according to a visual stimulus frequency for evoking the first electroencephalogram signal, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The presenting of a visual stimulus source, acquisition of the electroencephalogram signal, transmitting, acquisition of the preprocessed electroencephalogram signal, and defining a reference signal does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the presenting of a visual stimulus source, acquisition of the electroencephalogram signal, transmitting, acquisition of the preprocessed electroencephalogram signal, and defining a reference signal, nor does the method use a particular machine to perform the Abstract Idea.
Regarding claim 1, the interactive terminal and acquisition terminal recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited acquisition terminal is configured to perform pre-solutional data gathering activity, and the interactive terminal is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of:
interactive terminal;
acquisition terminal
the electroencephalogram signal obtained by the acquisition terminal is transmitted to the interactive terminal for electroencephalogram signal recognition
S1: acquiring a preprocessed electroencephalogram signal of a patient under a stimulus of a current visual stimulus source, recording the preprocessed electroencephalogram signal as a first electroencephalogram signal, and defining a reference signal according to a visual stimulus frequency for evoking the first electroencephalogram signal
The acquisition, transmitting, acquiring, and defining steps are well-understood, routine and conventional activities for those in the field of medical diagnostics. Further, the acquisition, transmitting, acquiring, and defining steps are each recited at a high level of generality such that it amounts to insignificant pre-solution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 1, the interactive terminal and acquisition terminal recited in the claim are well-understood, routine, and conventional terminals comprising generic components configured to perform the abstract idea – as evidenced by the non-patent literature of record; US 20230359275 A1 teaches conventional SSVEP where stimuli oscillate at a specific frequency in para. [0062]; US 20190307356 A1 teaches in para. [0002] that in conventional implementations, these BCIs use visual stimuli rendered in computer screens or with light sources modulated at a specified frequency to elicit response signals at the visual cortex and that response signals can be captured by electroencephalogram (“EEG”) equipment; US 20160144173 A1 teaches conventional EEG electrodes and a conventional computer in para. [0111, 0116]; US 6066163 A teaches a conventional monitoring system incorporated into a circuit board that is removably located on the motherboard of personal computer 22 and a conventional scalp EEG electrode in col. 4 lines 25-51 & claim 10. The recited acquisition terminal is a generic sensor configured to perform pre-solutional data gathering activity, and the interactive terminal is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims. Claims 2 is directed to more insignificant pre-solution activity and claims 3-15 are directed to more mental processes and/or mathematical concepts, which does not add anything significantly more. The steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1, 3 and 8-9, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Dehzangi (Dehzangi et al., Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification, BioMed Research International, 2018, 9796238, 14 pages, 2018. https://doi.org/10.1155/2018/9796238) in view of Deng (Yang Deng et al 2023 J. Neural Eng. 20 046005DOI 10.1088/1741-2552/ace380), and further in view of Chang (Liang Chang, Raofen Wang, Yu Zhang, Decoding SSVEP patterns from EEG via multivariate variational mode decomposition-informed canonical correlation analysis, Biomedical Signal Processing and Control, Volume 71, Part B, 2022, 103209, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2021.103209).
Regarding claim 1, Dehzangi discloses an intelligent ward round method based on an SSVEP electroencephalogram signal (Figs. 1 & 3-4, pages 4-5), the interactive terminal (as seen in Fig. 3, page 5 & page 2, Introduction, “portable BCI device of interest ... an Android tablet screen”) is used to present a visual stimulus source that includes a plurality of candidate intention options to the patient (as seen in Figs. 1 & 3-4, page 4-5, 4.2. Task and SSVEP Paradigm Generation, “icon was 600 pixels in size and flickered with a specific target frequency. Target frequencies were 10Hz, 12 Hz, 15Hz, and 8.5Hz, respectively” & page 2, Introduction, “icon, which depicts a common need of a patient in the intensive care”), the acquisition terminal is used to collect the patient's electroencephalogram signal (page 2, Introduction, “BCI4ICU system utilizes a wireless EEG cap”), the electroencephalogram signal obtained by the acquisition terminal is transmitted to the interactive terminal for electroencephalogram signal recognition (Abstract, “wearable EEG cap coupled with an Android app” & page 2, Introduction, “Android based wireless data acquisition and processing platform app” & page 3, 3. BCI4ICU System Architecture, “EEG data acquisition, signal processing” & Fig. 2), the patient's expression intention is determined (as seen in Figs. 1 & 4 & 9, pages 11-12, 5.3. GMM-Based SSVEP Identification Results), and the expression intentions comprise disease intentions, privacy intentions, psychological intentions, physiological intentions, environmental intentions, and safety intentions (as seen in Figs. 1 & 4, page 1, Introduction), and the intelligent ward round is completed (as seen in Figs. 1 & 4 & 9); S1: acquiring a preprocessed electroencephalogram signal of a patient under a stimulus of a current visual stimulus source, recording the preprocessed electroencephalogram signal as a first electroencephalogram signal (pages 4-5, 4. Proposed Signal Processing Methodology, “data is imported into MATLAB, we apply a 60Hz notch filter and a 5th-order Butterworth bandpass filter ... filtered data”), and defining a reference signal according to a visual stimulus frequency for evoking the first electroencephalogram signal (page 5, 4.2. Task and SSVEP Paradigm Generation, “Target frequencies were 10Hz, 12 Hz, 15Hz, and 8.5Hz, respectively” & Fig. 5); and determining a correlation coefficient of the electroencephalogram signal and the reference signal to obtain an evoking stimulus frequency of the electroencephalogram signal (Page 5, 4. Proposed Signal Processing Methodology, “calculate the CCA coefficients and to PSDA to generate the signal’s power scores” & Figs. 5-6), and determining an intention of the patient based on the evoking stimulus frequency to complete an intelligent ward round (as seen in Figs. 1 & 4 & 9, pages 11-12, 5.3. GMM-Based SSVEP Identification Results).
Dehzangi does not expressly disclose S2: decomposing the first electroencephalogram signal into sub-band components distributed in different frequency ranges, calculating differences of the sub-band components, a difference of the reference signal and differences between the sub-band components and the reference signal, and acquiring a total spatial filter of the sub-band components; S3: processing each sub-band component by the total spatial filter, rearranging the sub-band components into a new multi-channel signal, and recording the new multi-channel signal as a second electroencephalogram signal.
However Deng directed to a novel algorithm named TRCA-Net (TRCA-Net) to enhance SSVEP signal classification, which enjoys the advantages of both the knowledge-based method and the deep model discloses S2: decomposing the first electroencephalogram signal into sub-band components distributed in different frequency ranges (page 4, 3.1.TRCA, “filter bank to decompose it into NK sub-bands” & 3.2.TRCA-Net), calculating differences of the sub-band components, a difference of the reference signal and differences between the sub-band components and the reference signal (Abstract, “find the spatial filters by maximizing the inter-trial covariance” & page 4, 3.1.TRCA, “sum of cross-covariance matrices between all pairs of trials of stimulus n in sub-band k”), and acquiring a total spatial filter of the sub-band components (page 4, 3.1.TRCA, “final spatial filters for each sub band ... are obtained” & 3.2.TRCA-Net); S3: processing each sub-band component by the total spatial filter (page 4, 3.1.TRCA, “applied to filter both the testing signal and the individual signal template” & 3.2.TRCA-Net, “applies the spatial filters to extract task-related components of each trial”), rearranging the sub-band components into a new multi-channel signal (page 4, 3.2.TRCA-Net, “rearranged to form the new multi-channel signals”), and recording the new multi-channel signal as a second electroencephalogram signal (Abstract, “new multi-channel signals” page 4, 3.2.TRCA-Net, “new multi-channel signals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi such that the method comprises S2: decomposing the first electroencephalogram signal into sub-band components distributed in different frequency ranges, calculating differences of the sub-band components, a difference of the reference signal and differences between the sub-band components and the reference signal, and acquiring a total spatial filter of the sub-band components; S3: processing each sub-band component by the total spatial filter, rearranging the sub-band components into a new multi-channel signal, and recording the new multi-channel signal as a second electroencephalogram signal, in view of the teachings of Yang, as this would aid in improving the SNR of data by maximizing the inter-trial covariance, extracting task-related components of data, and obtaining a multi-channel signal.
Dehzangi, as modified by Deng hereinabove, does not disclose S4: acquiring a plurality of variation modal components of the second electroencephalogram signal by adopting variation modal decomposition, and optimizing weights of the variation modal components under each channel to reconstruct an electroencephalogram signal to obtain a third electroencephalogram signal; and S5: determining a correlation coefficient based on own features of the third electroencephalogram signal and the reference signal to obtain an evoking stimulus frequency of the third electroencephalogram signal.
However, Chang directed to a method of canonical correlation analysis based on multivariate variational mode decomposition and processing multi-channel signal (page 3, 2.2. Multivariate variational mode decomposition, “process multi-channel data”) discloses a multi-channel signal (page 3, 2.2. Multivariate variational mode decomposition, “EEG signal with n channels”) and S4: acquiring a plurality of variation modal components of the second electroencephalogram signal by adopting variation modal decomposition, and optimizing weights of the variation modal components under each channel to reconstruct an electroencephalogram signal to obtain a third electroencephalogram signal (pages 3-4 & 6, 2.2 Multivariate variational mode decomposition, “” & 2.3. Sparrow search algorithm, & 2.5. Canonical correlation analysis based on multivariate variational mode decomposition, “optimize the weight of IMF components in each frequency band ... weighted reconstructed EEG and sine–cosine reference signals”); and S5: determining a correlation coefficient based on own features of the third electroencephalogram signal and the reference signal to obtain an evoking stimulus frequency of the third electroencephalogram signal (pages 4-6, 2.4. Canonical correlation analysis & 2.5. Canonical correlation analysis based on multivariate variational mode decomposition, “canonical correlation analysis based on multivariate variational mode decomposition ... highest correlation coefficient is chosen as the final recognition target).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by ... hereinabove, such that the method comprises S4: acquiring a plurality of variation modal components of the second electroencephalogram signal by adopting variation modal decomposition, and optimizing weights of the variation modal components under each channel to reconstruct an electroencephalogram signal to obtain a third electroencephalogram signal; and S5: determining a correlation coefficient based on own features of the third electroencephalogram signal and the reference signal to obtain an evoking stimulus frequency of the third electroencephalogram signal, in view of the teachings of Chang, as this would aid in improving the detection of SSVEP performance due to the EEG signal’s susceptibility to noise artifacts and differences in SSVEP components between different channels and frequency bands (Chang, page 2, Introduction).
Regarding claim 3, Dehzangi, as modified by Deng and Chang hereinabove, discloses the intelligent ward round method based on an SSVEP electroencephalogram signal according to claim 1. Dehzangi, as modified by Deng and Chang hereinabove, does not expressly disclose wherein the defining a reference signal according to a visual stimulus frequency for evoking the first electroencephalogram signal in the step S1 comprises: constructing sine and cosine periodic signals as the reference signal based on the visual stimulus frequency for evoking the first electroencephalogram signal, a frequency of the reference signal being the same as the visual stimulus frequency for evoking the first electroencephalogram signal or being a multiple of the visual stimulus frequency for evoking the first electroencephalogram signal.
However, Chang discloses defining a reference signal according to a visual stimulus frequency for evoking the first electroencephalogram signal comprising constructing sine and cosine periodic signals as the reference signal based on the visual stimulus frequency for evoking the first electroencephalogram signal (pages 4-5, 2.4. Canonical correlation analysis, “reference signal Yf. The formula of Yf is shown” & 2.5. Canonical correlation analysis based on multivariate variational mode decomposition, “sine–cosine reference signals”), a frequency of the reference signal being the same as the visual stimulus frequency for evoking the first electroencephalogram signal or being a multiple of the visual stimulus frequency for evoking the first electroencephalogram signal (pages 4-6, 2.4. Canonical correlation analysis, “reference signal Yf. The formula of Yf is shown ... f is the stimulation frequency required by the target” & 3.1. Interface design for visual stimulation, “sinusoidal encoding method ... visual stimulus frequency”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by Deng and Chang hereinabove, such that defining a reference signal according to a visual stimulus frequency for evoking the first electroencephalogram signal in the step S1 comprises: constructing sine and cosine periodic signals as the reference signal based on the visual stimulus frequency for evoking the first electroencephalogram signal, a frequency of the reference signal being the same as the visual stimulus frequency for evoking the first electroencephalogram signal or being a multiple of the visual stimulus frequency for evoking the first electroencephalogram signal, in view of the teachings of Chang, as this would aid in ensuring the stability of the SSVEP source and defining reference signals for canonical correlation.
Regarding claim 8, Dehzangi, as modified by Deng and Chang hereinabove, discloses the intelligent ward round method based on an SSVEP electroencephalogram signal according to claim 1. Dehzangi, as modified by Deng and Chang hereinabove, does not expressly disclose wherein the S3 of processing each sub-band component by the total spatial filter, rearranging the sub-band components into a new multi-channel signal, and recording the new multi-channel signal as a second electroencephalogram signal specifically comprises: vertically arranging and organizing the first electroencephalogram signal subjected to TRCA space domain filtering into new channels along the y axis according to a sequence of the Nf spatial filters; and recombining the K sub-band components along the z axis in each new channel to obtain a new signal formed by rearrangement, and recording the new signal as the second electroencephalogram signal.
However, Deng discloses vertically arranging and organizing the first electroencephalogram signal subjected to TRCA space domain filtering into new channels along the y axis according to a sequence of the Nf spatial filters (page 4, 3.2.TRCA-Net, “TRCA-filtered signals from NF spatial filters are rearranged vertically ... reorganized as new channels along the y-axis”); and recombining the K sub-band components along the z axis in each new channel to obtain a new signal formed by rearrangement (page 5, 3.2.TRCA-Net, “new multi-channel signals from NK sub bands are reorganized along the z-axis to lever age the contributions of the harmonics”), and recording the new signal as the second electroencephalogram signal (pages 4-5, 3.2.TRCA-Net, “form the new multi-channel signals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by Deng and Chang hereinabove, such that the S3 of processing each sub-band component by the total spatial filter, rearranging the sub-band components into a new multi-channel signal, and recording the new multi-channel signal as a second electroencephalogram signal specifically comprises: vertically arranging and organizing the first electroencephalogram signal subjected to TRCA space domain filtering into new channels along the y axis according to a sequence of the Nf spatial filters; and recombining the K sub-band components along the z axis in each new channel to obtain a new signal formed by rearrangement, and recording the new signal as the second electroencephalogram signal, in view of the teachings of Deng, as this would aid in improving the SNR of data by maximizing the inter-trial covariance, extracting task-related components of data, and obtaining a multi-channel signal.
Regarding claim 9, Dehzangi, as modified by Deng and Chang hereinabove, discloses the intelligent ward round method based on an SSVEP electroencephalogram signal according to claim 1. Dehzangi, as modified by Deng and Chang hereinabove, does not expressly disclose wherein the S4 of acquiring a plurality of variation modal components of the second electroencephalogram signal by adopting variation modal decomposition, and optimizing weights of the variation modal components under each channel to reconstruct an electroencephalogram signal to obtain a third electroencephalogram signal comprises: S41: representing the second electroencephalogram signal as a sum of the plurality of variation modal components to carry out singular value decomposition on the second electroencephalogram signal; S42: determining a number P of the variation modal components based on a variation trend of singular values; S43: constructing a variation model based on the number P of the variation modal components, and solving the variation model by adopting an alternating direction multiplier algorithm to acquire the plurality of variation modal components of the second electroencephalogram signal; S44: weighting the variation modal components of each frequency band by adopting a sparrow search algorithm, weights of the variation modal components being determined according to a fitness value of the sparrow search algorithm; and S45: reconstructing the electroencephalogram signal under each channel based on the weights of the variation modal components to obtain the third electroencephalogram signal.
However, Chang discloses S41: representing the second electroencephalogram signal as a sum of the plurality of variation modal components to carry out singular value decomposition on the second electroencephalogram signal (pages 2-3, 2.1. Variational mode decomposition, “IMF component uk(t)is defined as Eq. (1)” & EEG signal with n channels X(t) ... Eq (9)”); S42: determining a number P of the variation modal components based on a variation trend of singular values (pages 3-4 & 7, 2.2. Multivariate variational mode decomposition, “obtaining k narrowband IMF components ... narrowband IMF components are obtained” & 4.1. The related parameters of multivariate variational mode decomposition “main parameters of multivariate variational mode decomposition are the number of modes k”); S43: constructing a variation model based on the number P of the variation modal components, and solving the variation model by adopting an alternating direction multiplier algorithm to acquire the plurality of variation modal components of the second electroencephalogram signal (“find the common frequency component ... sum of the IMF components decomposed by each channel reconstruct the input signal and minimize the sum of the IMF bandwidth, the constrained related optimization problem is shown in Eq. (10) ... Lagrangian representation for the variational problem”); S44: weighting the variation modal components of each frequency band by adopting a sparrow search algorithm, weights of the variation modal components being determined according to a fitness value of the sparrow search algorithm (pages 4-5, 2.3. Sparrow search algorithm, “Sparrow search algorithm is used ... Fx vector ... individual sparrow’s fitness” & 2.5. Canonical correlation analysis based on multivariate variational mode decomposition, “IMF components of various channels and frequency bands are weighted ... optimize the weight”); and S45: reconstructing the electroencephalogram signal under each channel based on the weights of the variation modal components to obtain the third electroencephalogram signal (page 6, 2.5. Canonical correlation analysis based on multivariate variational mode decomposition, “x i represents the EEG signal after weighted reconstruction of the i- th channel, w i,j different weights, i the i-th channel, and j the j-th layer IMF”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by Deng and Chang hereinabove, such that the S4 of acquiring a plurality of variation modal components of the second electroencephalogram signal by adopting variation modal decomposition, and optimizing weights of the variation modal components under each channel to reconstruct an electroencephalogram signal to obtain a third electroencephalogram signal comprises: S41: representing the second electroencephalogram signal as a sum of the plurality of variation modal components to carry out singular value decomposition on the second electroencephalogram signal; S42: determining a number P of the variation modal components based on a variation trend of singular values; S43: constructing a variation model based on the number P of the variation modal components, and solving the variation model by adopting an alternating direction multiplier algorithm to acquire the plurality of variation modal components of the second electroencephalogram signal; S44: weighting the variation modal components of each frequency band by adopting a sparrow search algorithm, weights of the variation modal components being determined according to a fitness value of the sparrow search algorithm; and S45: reconstructing the electroencephalogram signal under each channel based on the weights of the variation modal components to obtain the third electroencephalogram signal, in view of the teachings of Chang, as this would aid in improving the detection of SSVEP performance due to the EEG signal’s susceptibility to noise artifacts and differences in SSVEP components between different channels and frequency bands (Chang, page 2, Introduction).
Regarding claim 12, Dehzangi, as modified by Deng and Chang hereinabove, discloses the intelligent ward round method based on an SSVEP electroencephalogram signal according to claim 9. Dehzangi, as modified by Deng and Chang hereinabove, does not expressly disclose wherein the S44 of weighting the variation modal components of each frequency band by adopting a sparrow search algorithm, weights of the variation modal components being determined according to a fitness function value of the sparrow search algorithm comprises: adopting relative entropy of the variation modal component and the second electroencephalogram signal as a fitness function of the sparrow search algorithm; the larger the value of the fitness function of the variation modal component, the larger the weight.
However, Chang discloses wherein the S44 of weighting the variation modal components of each frequency band by adopting a sparrow search algorithm, weights of the variation modal components being determined according to a fitness function value of the sparrow search algorithm (pages 4-5, 2.3. Sparrow search algorithm, “Sparrow search algorithm is used ... Fx vector ... individual sparrow’s fitness” & 2.5. Canonical correlation analysis based on multivariate variational mode decomposition, “IMF components of various channels and frequency bands are weighted ... optimize the weight”) comprises: adopting relative entropy of the variation modal component and the second electroencephalogram signal as a fitness function of the sparrow search algorithm (page 4, 2.3. Sparrow search algorithm, “represent the current fitness, best fitness value, and worst fitness, respectively” & pages 9-10, 4.3. Parameters related to sparrow search algorithm, “fitness function: ..., the fitness function value is the error rate of CCA algorithm classification” & 4.4. The weight of the inherent mode functions, “optimized weight result is shown in Fig. 8 after combining it with the sparrow algorithm to optimize ... weights of different rows”); the larger the value of the fitness function of the variation modal component, the larger the weight (pages 9-10, 4.4. The weight of the inherent mode functions, “channels with higher weights for different subjects differ, the POz, Oz, O1, and O2 channels have more bright color blocks than other channels, indicating that the weighting degree of these channels is higher”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by Deng and Chang hereinabove, such that the S44 of weighting the variation modal components of each frequency band by adopting a sparrow search algorithm, weights of the variation modal components being determined according to a fitness function value of the sparrow search algorithm comprises: adopting relative entropy of the variation modal component and the second electroencephalogram signal as a fitness function of the sparrow search algorithm; the larger the value of the fitness function of the variation modal component, the larger the weight, in view of the teachings of Chang, as this would aid in improving the detection of SSVEP performance due to the EEG signal’s susceptibility to noise artifacts and differences in SSVEP components between different channels and frequency bands (Chang, page 2, Introduction).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Dehzangi in view of Deng, further in view of Chang, as applied to claim 1 above, and further in view of Kidmose (US 20220031217 A1).
Regarding claim 2, Dehzangi, as modified by Deng and Chang hereinabove, discloses the intelligent ward round method based on an SSVEP electroencephalogram signal according to claim 1, wherein the preprocessing comprises passing the electroencephalogram signal through a notch filter and a Butterworth filter in sequence to obtain the first electroencephalogram signal (page 5, 4. Proposed Signal Processing Methodology, “60Hz notch filter and a 5th-order Butterworth bandpass filter”).
Dehzangi, as modified by Deng and Chang hereinabove, does not expressly disclose performing signal amplification, filtering and analog-to-digital conversion on the acquired electroencephalogram signal of the patient under a rhythmic visual stimulus in an SSVEP paradigm; the filtering comprises passing the electroencephalogram signal through a 50Hz notch filter and a 3-40Hz Butterworth filter in sequence to obtain the first electroencephalogram signal.
However, Kidmose directed to an electrode for detecting a bioelectrical signal, for example EEG, discloses performing signal amplification, filtering and analog-to-digital conversion on the acquired electroencephalogram signal (“module 125 operating as electrode frontend ... and Analog to Digital Converter (ADC) ... amplified”; “all recordings were bandpass filtered”, para. [0098, 0149, 0154]) of the patient under a rhythmic visual stimulus in an SSVEP paradigm (“stimulus ... SSVEP”; “stimuli ... sinusoid”, para. [0147, 0153, 0158]); the filtering comprises passing the electroencephalogram signal through a 50Hz notch filter (“50 Hz notch-filter”, para. [0154]) and a 3-40Hz filter in sequence to obtain the first electroencephalogram signal (“bandpass filtered ... retain frequencies between 2 and 100 Hz”; “bandpass filtered from 2 to 45 Hz”, para. [0149, 0154, 0162]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by Deng and Chang hereinabove, such that the preprocessing in the step S1 comprises performing signal amplification, filtering and analog-to-digital conversion on the acquired electroencephalogram signal of the patient under a rhythmic visual stimulus in an SSVEP paradigm; the filtering comprises passing the electroencephalogram signal through a 50Hz notch filter and a 3-40Hz Butterworth filter in sequence to obtain the first electroencephalogram signal, in view of the teachings of Kidmose, as this would aid in digitizing the signal, reducing power line interference, and retaining frequences from 3-40 Hz.
Claims 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Dehzangi in view of Deng, further in view of Chang, as applied to claim 1 above, and further in view of Wong (C. M. Wong et al., "Inter- and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-Based BCIs," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 10, pp. 2123-2135, Oct. 2020, doi: 10.1109/TNSRE.2020.3019276)
Regarding claim 13, Dehzangi, as modified by Deng and Chang hereinabove, discloses the intelligent ward round method based on an SSVEP electroencephalogram signal according to claim 1. Dehzangi, as modified by Deng and Chang hereinabove, does not expressly disclose wherein the S5 comprises: calculating a Pearson coefficient of the third electroencephalogram signal and an average signal of the third electroencephalogram signal and a Pearson coefficient of the third electroencephalogram signal and the reference signal by adopting a typical correlation analysis method as correlation coefficients, and taking a frequency corresponding to the maximum correlation coefficient as the evoking stimulus frequency of the third electroencephalogram signal.
However, Wong directed to a subject transfer based canonical correlation analysis (stCCA) method discloses calculating a Pearson coefficient of the third electroencephalogram signal and an average signal of the third electroencephalogram signal and a Pearson coefficient of the third electroencephalogram signal and the reference signal by adopting a typical correlation analysis method as correlation coefficients (page 4, A. Transferred Spatial Filter and Transferred SSVEP Template, “CCA-based SSVEP recognition algorithms ... corresponding Nf features for target recognition can be computed by ... Pearson’s correlation between two vectors: x and y”, see equation 9), and taking a frequency corresponding to the maximum correlation coefficient as the evoking stimulus frequency of the third electroencephalogram signal (page 4, A. Transferred Spatial Filter and Transferred SSVEP Template, “stimulus frequency fc corresponding to the largest feature ρc is recognized”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by Deng and Chang hereinabove, such that the S5 comprises: calculating a Pearson coefficient of the third electroencephalogram signal and an average signal of the third electroencephalogram signal and a Pearson coefficient of the third electroencephalogram signal and the reference signal by adopting a typical correlation analysis method as correlation coefficients, and taking a frequency corresponding to the maximum correlation coefficient as the evoking stimulus frequency of the third electroencephalogram signal, in view of the teachings of Wong, as this would aid in recognizing the stimulus frequency of the SSVEP.
Regarding claim 14, Dehzangi, as modified by Deng, Chang, and Wong hereinabove, discloses the intelligent ward round method based on an SSVEP electroencephalogram signal according to claim 13. Dehzangi, as modified by Deng and Chang hereinabove, does not expressly disclose wherein the calculating a Pearson coefficient of the third electroencephalogram signal and an average signal of the third electroencephalogram signal and a Pearson coefficient of the third electroencephalogram signal and the reference signal by adopting a typical correlation analysis method specifically comprises: calculating a spatial filter of the third electroencephalogram signal and the reference signal by adopting the typical correlation analysis method to obtain a first weight vector ωX1, and solving a first Pearson coefficient ρ1 of the third electroencephalogram signal X and the reference signal Yf based on the first weight vector; and calculating a spatial filter of the third electroencephalogram signal and the average signal of the third electroencephalogram signal by adopting the typical correlation analysis method to obtain a second weight vector ωX2, and solving a second Pearson coefficient of the third electroencephalogram signal X and the average signal X- of the third electroencephalogram signal based on the second weight vector.
However, Wong discloses wherein the calculating a Pearson coefficient of the third electroencephalogram signal and an average signal of the third electroencephalogram signal and a Pearson coefficient of the third electroencephalogram signal and the reference signal by adopting a typical correlation analysis method specifically (page 4, A. Transferred Spatial Filter and Transferred SSVEP Template, “CCA-based SSVEP recognition algorithms ... corresponding Nf features for target recognition can be computed by ... Pearson’s correlation between two vectors: x and y”, see equation 9) comprises: calculating a spatial filter of the third electroencephalogram signal and the reference signal by adopting the typical correlation analysis method to obtain a first weight vector ωX1 (pages 3-4, A. Transferred Spatial Filter and Transferred SSVEP Template, “spatially filtered ... weight vector ... determine the weight wn ... transferable within subject and across classes”), and solving a first Pearson coefficient ρ1 of the third electroencephalogram signal X and the reference signal Yf based on the first weight vector (pages 3-4, “computes the Pearson’s correlation between two vectors: x and y ... rk,1”, see equation 9); and calculating a spatial filter of the third electroencephalogram signal and the average signal of the third electroencephalogram signal by adopting the typical correlation analysis method to obtain a second weight vector ωX2 (pages 3-4, A. Transferred Spatial Filter and Transferred SSVEP Template, “spatially filtered ... weight vector ... determine the weight wn”), and solving a second Pearson coefficient of the third electroencephalogram signal X and the average signal X- of the third electroencephalogram signal based on the second weight vector (pages 3-4, “computes the Pearson’s correlation between two vectors ... rk,2”, see equation 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by Deng and Chang hereinabove, such that wherein the calculating a Pearson coefficient of the third electroencephalogram signal and an average signal of the third electroencephalogram signal and a Pearson coefficient of the third electroencephalogram signal and the reference signal by adopting a typical correlation analysis method specifically comprises: calculating a spatial filter of the third electroencephalogram signal and the reference signal by adopting the typical correlation analysis method to obtain a first weight vector ωX1, and solving a first Pearson coefficient ρ1 of the third electroencephalogram signal X and the reference signal Yf based on the first weight vector; and calculating a spatial filter of the third electroencephalogram signal and the average signal of the third electroencephalogram signal by adopting the typical correlation analysis method to obtain a second weight vector ωX2, and solving a second Pearson coefficient of the third electroencephalogram signal X and the average signal X- of the third electroencephalogram signal based on the second weight vector, in view of the teachings of Wong, as this would aid in recognizing the stimulus frequency of the SSVEP.
Regarding claim 15, Dehzangi, as modified by Deng, Chang, and Wong hereinabove, discloses the intelligent ward round method based on an SSVEP electroencephalogram signal according to claim 14. Dehzangi, as modified by Deng and Chang hereinabove, does not expressly disclose wherein the correlation coefficient specifically is:
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38
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wherein Ri represents a correlation coefficient of the ith stimulus and the reference signal, ρ1i represents the first Pearson coefficient of the third electroencephalogram signal under the ith stimulus, ρ2i represents the second Pearson coefficient of the third electroencephalogram signal under the ith stimulus, and sign() is a sign function and used for representing positive or negative correlation, the evoking stimulus frequency of the third electroencephalogram signal is
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46
136
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, wherein I =1, 2, ..., Nf represents a stimulus target number.
However, Wong discloses that the correlation coefficient specifically is:
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38
294
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(page 4, B. SSVEP Recognition Algorithms with Subject Transfer, “computed by ... rk,1 ... rk,2” see equation 9) wherein Ri represents a correlation coefficient of the ith stimulus and the reference signal (page 4, B. SSVEP Recognition Algorithms with Subject Transfer, “computes the Pearson’s correlation”), ρ1i represents the first Pearson coefficient of the third electroencephalogram signal under the ith stimulus (page 4, B. SSVEP Recognition Algorithms with Subject Transfer, “rk,1”), ρ2i represents the second Pearson coefficient of the third electroencephalogram signal under the ith stimulus (page 4, B. SSVEP Recognition Algorithms with Subject Transfer, “rk,2”), and sign() is a sign function and used for representing positive or negative correlation (page 4, B. SSVEP Recognition Algorithms with Subject Transfer, see equation 9), the evoking stimulus frequency of the third electroencephalogram signal is
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46
136
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, wherein I =1, 2, ..., Nf represents a stimulus target number (page 2, Table 1 & page 4, B. SSVEP Recognition Algorithms with Subject Transfer, “Nf features”, see equation 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Dehzangi, as modified by Deng and Chang hereinabove, such that the correlation coefficient specifically is:
PNG
media_image1.png
38
294
media_image1.png
Greyscale
wherein Ri represents a correlation coefficient of the ith stimulus and the reference signal, ρ1i represents the first Pearson coefficient of the third electroencephalogram signal under the ith stimulus, ρ2i represents the second Pearson coefficient of the third electroencephalogram signal under the ith stimulus, and sign() is a sign function and used for representing positive or negative correlation, the evoking stimulus frequency of the third electroencephalogram signal is
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46
136
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Greyscale
, wherein I =1, 2, ..., Nf represents a stimulus target number, in view of the teachings of Wong, as this would aid in recognizing the stimulus frequency of the SSVEP.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chen (Xiaogang Chen et al 2015 J. Neural Eng. 12 046008 DOI 10.1088/1741-2560/12/4/046008): Regarding claim 4 (claim 5-7 by virtue of dependency), Chen directed to a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs discloses constructing a decomposition filter bank (page 5, 2.2.3. Filter bank design, “selected for the filter bank. In practice, the frequency range of the filter bank can be further optimized towards the highest BCI performance” & page 12, 4.2. Design of a filter bank), and decomposing the first electroencephalogram signal into a plurality of frequency bands by using the decomposition filter bank to obtain the sub-band components of the first electroencephalogram signal (pag2, Introduction, “array of band-pass filters that separates an input signal into multiple sub-band components” & page 5, 2.2.3. Filter bank design, “decompose SSVEPs into sub-band components” & page 12, 44.3. Extended CCA methods using SSVEP training data, “decompose the SSVEP signals into sub-band components”), specifically comprising: S21: determining a number K of the sub-band components based on a frequency range of the first electroencephalogram signal (as seen in Fig. 3 & page 4, 2.2.2. Filter bank CCA, “multiple filters that have different pass-bands” & page 5, 2.2.3. Filter bank design, “10 sub-bands, each of which has a bandwidth of 8Hz”), the decomposition filter bank comprising a plurality of band-pass filters (as seen in Fig. 3 & page 4, 2.2.2. Filter bank CCA, “multiple filters that have different pass-bands”), and each band-pass filter being configured to extract information of one specific frequency range of the first electroencephalogram signal to obtain one sub-band component (as seen in Fig. 3 & page 4, 2.2.2. Filter bank CCA, “multiple filters that have different pass-bands” & page 5, 2.2.3. Filter bank design, “10 sub-bands, each of which has a bandwidth of 8Hz”); S22: calculating a center frequency of each band-pass filter, the center frequency being selected based on an equidistant frequency between the frequency ranges of the first electroencephalogram signal (page 5, 2.2.3. Filter bank design, “first method (M1) divided the full frequency band of SSVEP components into sub-bands with equally spaced bandwidths (i.e. the bandwidth of the stimulation signals)” & Fig. 3); S23: establishing one band-pass filter for each center frequency (page 4, 2.2.2. Filter bank CCA, “multiple filters that have different pass-bands” & page 5, 2.2.3. Filter bank design, “10 sub-bands, each of which has a bandwidth of 8Hz”, Fig. 3), S24: decomposing the first electroencephalogram signal into K sections in a frequency domain through the decomposition filter bank, each section being one sub-band component to obtain K sub-band components of the first electroencephalogram signal, and the sub-band components being distributed in different frequency ranges (as seen in Fig. 3 & page 4, 2.2.2. Filter bank CCA, “multiple filters that have different pass-bands” & page 5, 2.2.3. Filter bank design, “10 sub-bands, each of which has a bandwidth of 8Hz”). Dehzangi, Deng, Chang, and Chen do not disclose the transfer function of the band-pass filter being:
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49
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wherein fc denotes the center frequency, and z denotes a complex variable.
Seyed Mohammad Mehdi Safi, Mohammad Pooyan, Ali Motie Nasrabadi, Improving the performance of the SSVEP-based BCI system using optimized singular spectrum analysis (OSSA), Biomedical Signal Processing and Control, Volume 46, 2018, Pages 46-58, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2018.06.010 teaches a Hankel matrix on page 49. Safi does not teach “constructing an m×n order Hankel matrix based on the variation modal components and the channel number of the second electroencephalogram signal, wherein m=Ns2+1, n=Ns-m+1, Ns is the channel number”.
Chintalpudi S.L. Prasanna, Md Zia Ur Rahman, A reference free non-negative adaptive learning system for health care monitoring and adaptive physiological artifact elimination in brain waves, Healthcare Analytics, Volume 4, 2023, 100225, ISSN 2772-4425, https://doi.org/10.1016/j.health.2023.100225 teaches a Hankel matrix on page 3. Chintalpudi does not teach “constructing an m×n order Hankel matrix based on the variation modal components and the channel number of the second electroencephalogram signal, wherein m=Ns2+1, n=Ns-m+1, Ns is the channel number”.
Wang et al., (2022). Feature Analysis of Electroencephalogram Signals Evoked by Machine Noise. In: Yang, S., Lu, H. (eds) Artificial Intelligence and Robotics. ISAIR 2022. Communications in Computer and Information Science, vol 1700. Pages 332-342, Springer, Singapore. https://doi.org/10.1007/978-981-19-7946-0_28 teaches a Hankel matrix on page 335. Wang does not teach “constructing an m×n order Hankel matrix based on the variation modal components and the channel number of the second electroencephalogram signal, wherein m=Ns2+1, n=Ns-m+1, Ns is the channel number”.
F. Demir, H. Arslan and I. Uysal, "Bio-inspired filter banks for SSVEP-based brain-computer interfaces," 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Las Vegas, NV, USA, 2016, pp. 144-147, doi: 10.1109/BHI.2016.7455855.
CN-116785085-A English Translation directed to brain-computer interface technology.
CN-114916945-A English Translation directed to electronic devices for detecting brain-computer interfaces based on electroencephalogram (EEG) signals.
CN-109684296-A English Translation directed to a method, apparatus, computer device, and storage medium for constructing a brainprint recognition database.
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/A.E.H./Examiner, Art Unit 3791
/AURELIE H TU/
Primary Examiner, Art Unit 3791
