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WO2014069996A1 - Procédé et système pour une interface cerveau-machine - Google Patents

Procédé et système pour une interface cerveau-machine Download PDF

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Publication number
WO2014069996A1
WO2014069996A1 PCT/NL2013/050762 NL2013050762W WO2014069996A1 WO 2014069996 A1 WO2014069996 A1 WO 2014069996A1 NL 2013050762 W NL2013050762 W NL 2013050762W WO 2014069996 A1 WO2014069996 A1 WO 2014069996A1
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Prior art keywords
input signal
whitening
classification model
parameters
brain
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English (en)
Inventor
Boris Reuderink
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Radboud Universiteit Nijmegen
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Stichting Katholieke Universiteit
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present invention relates to a method and system for a brain-computer interface (BCI), more particularly to a method for providing a brain-computer interface, comprising obtaining a classification model as part of a processing pipeline for processing an input signal, the input signal comprising a neural signature to be detected by the classification model.
  • BCI brain-computer interface
  • a key element in a BCI system is the software or classification model that classifies the neural signature of specific brain activity that the user produces with a cognitive task used to interact with the BCI.
  • Creating a BCI classifier that uses a different cognitive tasks is a laborious task, which typically requires extensive neuroscientific knowledge, and in addition requires months to even years to fine-tune parameters for a delicate series of signal processing and machine learning steps.
  • This complex pipeline can then be calibrated to work for a specific user. For this reason, perhaps, the number of distinct types of brain activity that can be reliably used for BCI remains limited to date.
  • EEG electroencephalography
  • BCI detectors that can be separated in methods for detecting specific neural signatures, such as the P300 ERP [3], and methods for detecting novel or anomalous data, wherein deviations from a "normal" resting state are interpreted as intentions to control the BCI [10].
  • the article 'Real Coded GA-Based SVM for Motor Imagery Classification in a Brain-Computer Interface' by A. Bamdadian et al., 2011 9th IEEE International Conference on Control and Automation (ICC A), Santiago, Chile, December 19-21, 2011 discloses a motor imagery-based Brain Computer Interface implementation.
  • the article discloses the use of a real-coded generic algorithm to determine the free kernel parameters of the Support Vector Machine (SVM).
  • SVM Support Vector Machine
  • the method uses a Common Spatial Pattern (CSP) algorithm which is known as such.
  • CSP Common Spatial Pattern
  • the implementation as disclosed can be seen as a spatial whitening specifically and only aimed at removing spatial correlations (disregarding any temporal correlations).
  • an EEG signal is frequency filtered, subsequently the CSP algorithm is applied, after which the variance is calculated (i.e. a non-linear transformation).
  • the output is then fed to a SVM for classification.
  • the present invention seeks to provide an improved, more robust BCI method, which is less dependent on specific parameter choices, especially user-dependent parameter selections.
  • obtaining the classification model comprises training the classification model using the input signal and assigning one or more labels to the input signal at different time points, wherein each label indicates whether the input signal at the associated time point of the label should be classified as target activity by the classification model, further comprising processing steps for
  • training the classification model comprises determining weights to be used in the classifying step using a numerical optimization procedure.
  • the input signal used is for a BCI application of biological origin, e.g. a (sub)set of EEG signals.
  • the method further comprises executing the classification model using the determined weights and an input signal (without the offset vector) to detect the specific neural signature trained for.
  • the detection may be a presence/absence detector, or more complex prediction or classification schemes.
  • the present invention allows to avoid the laborious tuning of classifiers commonly required by prior art methods.
  • a unified method is provided called
  • the word “detector” is used to describe a classifier that discriminates data having a particular neural signature from all other data.
  • non-linear classifier in the present invention allows to implicitly use the information in the EEG signal related to the power of the EEG signal. In combination with the further features of the present independent claim embodiment, this allows to obtain an optimal filtering maintaining spatial, temporal and functional information in the processed signals.
  • the few remaining free parameters directly control aspects of the behaviour of the classifier (e.g. the rate of false positives), or only bias the classifier to specific solutions as opposed to imposing hard limits.
  • the method of the present invention can learn well- performing detectors despite a badly chosen bias.
  • Such a black-box method for BCI development allows non-experts to design BCIs, even for novel user tasks with currently unknown neural signatures. It is expected that the associated faster development cycles and the increased number of researchers that can design BCIs will further accelerate the progress in BCI research.
  • the polynomial kernel is a non-linear polynomial kernel, i.e. the polynomial kernel is at least a second degree polynomial kernel.
  • the input signal is a sampled input signal (vector) in time space
  • quadratic expansion allows the detection of power in different frequency bands
  • cubic expansion reveals cross-frequency coupling between power and phase
  • a fourth degree polynomial would allow to find power-power cross frequency coupling.
  • the numerical optimization procedure is executed for a kernel machine, e.g. using a one-class support vector machine.
  • a kernel machine in various implementations is well known in the art and can advantageously be used in the present invention embodiments.
  • whitening the input signal comprises an adaptive whitening step for removing correlations in the input signal. This assures that the feature(s) to be detected are uncorrected and have unity variance.
  • the traces in the input signal (which are e.g. EEG channel traces) are less correlated and represent the activity local to the associated sensor.
  • the whitening comprises adaptive sensor covariance estimation having a number of predetermined parameters to select: a rate of adaptation defined by a half-life of less than a desired value (e.g. 60 seconds, 50 seconds or 30 seconds) or a forgetting factor ( ⁇ ) between zero and one, i.e. ⁇ G [0,1], which determines the rate of adaptation.
  • a rate of adaptation defined by a half-life of less than a desired value (e.g. 60 seconds, 50 seconds or 30 seconds) or a forgetting factor ( ⁇ ) between zero and one, i.e. ⁇ G [0,1], which determines the rate of adaptation.
  • the whitening in a further embodiment comprises filtering the input signal using a high- pass filter with a cut-off frequency of less than 5 Hz, e.g. 1 Hz. This ensures that the neural signature to be trained and detected is not filtered out from the input signal and that correlations over time (covariance over time) are reduced in the input signal.
  • the processing pipeline further comprises, after the whitening, windowing the input signal using predetermined windowing parameters to bias use of primarily the newest parts of the input signal (as in general for
  • the windowing in one embodiment comprises a variance biasing step, using a function that specifies the fraction of variance for a sample age.
  • this relates to simple predetermined windowing parameters, which are user independent.
  • the present invention relates to a brain computer interface comprising an input unit for receiving (and optionally pre-processing) an input signal, and a processing unit connected to the input unit, the processing unit being arranged for executing the method according to any one of the present invention method
  • the present invention relates to a computer program product comprising computer executable instructions, which when loaded on a processing system (arranged to receive an input signal) provide the processing system with the functionality of the method according to any one of the present invention embodiments.
  • the present invention method embodiments may be advantageously used in a number of specific applications, such as an entertainment application (e.g. a computer game, for operating a further apparatus (e.g. a machine, an appliance, etc), or for training applications (e.g. in user feedback training).
  • an entertainment application e.g. a computer game
  • a further apparatus e.g. a machine, an appliance, etc
  • training applications e.g. in user feedback training.
  • Fig. 1 shows a schematic view of an embodiment of the method according to the present invention
  • Fig. 2 shows a schematic view of a further embodiment of the method according to the present invention
  • Fig. 3 a shows a graph of an exemplary input signal as used in the present invention embodiments
  • Fig. 3b shows a graph of a quadratic expansion of the time trace of Fig.3a
  • Fig. 3c shows a graph of the weights of a linear classifier that is trained using the present invention for detecting oscillations with an ERD in the input signal of Fig. 3a;
  • Fig. 4 shows a graphical representation of a function specifying the fraction of variance for a sample age as applied in an embodiment of the present invention
  • Fig. 5a shows a graph of an input signal as can be used with the present invention embodiments.
  • Fig. 5b shows a graph of the input signal of Fig. 5a, after execution of the whitening according to an embodiment of the present invention.
  • the present invention aims to provide a method of learning detectors for characteristic brain activity fully from data.
  • BCIs brain- computer interfaces
  • creating brain- computer interfaces (BCIs) typically requires extensive knowledge of the brain and of signal processing methods. This slows scientific progress and dissemination of BCI technology.
  • a layperson can record his/her own brain signals and detect at which point in time he/she recorded brain activity of specific interest. Based on only these recorded signals and a label for each time point, the method of the present invention can create software for detecting the presence of certain brain activity in real-time. Effectively, the method of the present invention enables anyone to build BCIs.
  • the present invention embodiments relate to a method for providing a brain- computer interface, comprising obtaining a classification model 13 as part of a processing pipeline 10 for processing an input signal E, the input signal E comprising a neural signature to be detected by the classification model (or detector) 13.
  • a processing pipeline 10 for the brain-computer interface is shown in general form in the schematic drawing of Fig. 1.
  • the input signal 11 is a signal of biological origin, e.g. an electro-physiological signal such as an EEG signal E (having multiple traces from the associated multiple sensors).
  • EEG signal E having multiple traces from the associated multiple sensors.
  • these types of signals are sampled over time, and may have undergone pre-processing (e.g. amplification).
  • the input signal is processed in a whitening block 12 and an optional windowing block 17, before being fed to the classification model 13.
  • the output of the classification model 13 is a detection or prediction 14 of the actual occurrence of a neural signature of interest.
  • the classification model 13 comprises application of a polynomial kernel 15 and classifying with a kernel method 16.
  • obtaining the classification model 13 comprises training the classification model 13 using the input signal and assigning (one or more) labels to the input signal at different time points, wherein each label indicates whether the input signal at the associated time point of the label should be classified as target activity by the classification model 13, and the method comprises processing steps for
  • training the classification model 13 comprises determining weights to be used in the classifying step using a numerical optimization procedure.
  • Matrices are indicated with capitals (e.g. A), where / is the identity matrix.
  • Column vectors are indicated with a harpoon (e.g. x).
  • Lower case variables denote scalars (e.g. s).
  • the Euclidean norm is indicated with
  • the transpose of is indicated with M T .
  • a strict linear relation between a and b is indicated with a ⁇ b.
  • the set of integers is indicated with ⁇ .
  • a ⁇ i-dimensional real- valued vectors are G E d .
  • a classification model g ⁇ 3 ⁇ 4 ⁇ E is learned based on example data of brain activity comprising a neural signature of interest.
  • the classification model g(w T ⁇ x ) is automatically found based on features *i derived from past samples of E that detect the neural signature for that detector 13, but does not respond to other feature distributions.
  • the classification model is learned from examples or training data by using a numerical optimization procedure that minimizes a loss (cost) function that is defined by the classification method.
  • a loss function I: (MP ' , E) ⁇ E is chosen containing a term penalizing a modelling error e and a term penalizing model complexity with a so-called regularizer r:
  • the prediction model m is parametrized with w that linearly combines a numerical feature description.
  • a representation of an object to be classified is mapped from an input space X to a feature space through a map ⁇ :
  • the numerical optimization procedure is executed for a kernel machine.
  • the numerical optimization procedure is executed for a one-class support vector machine (SVM) [11, 13], which works well with high-dimensional feature spaces.
  • SVM support vector machine
  • the numerical optimization procedure is executed for other types of kernel machines [12].
  • Equation (6) has the same functional form but now introduces a linear transformation ⁇ _ ⁇ . Therefore, a transformation of the feature space ⁇ ( ⁇ ) with ⁇ _ ⁇ of an £ 2 regularized classifier can be used to simulate Tikhonov regularization.
  • a generalized feature space is used to eliminate the choice between methods based on signal amplitude (ERP) and methods based on signal power (ERD). That is, the generalized feature space can capture even more complex signal characteristics, such as cross-frequency coupling (CFC).
  • ERP signal amplitude
  • ERP signal power
  • CFC cross-frequency coupling
  • a quadratic expansion allows the detection of power in different frequency bands
  • cubic expansion reveals cross-frequency coupling (CFC) between power and phase
  • the third degree terms can be interpreted as a product of amplitude and a power feature
  • a fourth degree polynomial expansion can be used to find power-power CFC.
  • Fig. 3 shows graphs relating to an exemplary ERD processed in the processing pipeline 10 of the present invention embodiments with quadratic expansion of features (i.e. the polynomial kernel 15 is a second degree polynomial kernel, i.e. a non-linear polynomial kernel).
  • the polynomial kernel 15 is a second degree polynomial kernel, i.e. a non-linear polynomial kernel.
  • Fig. 3a an example of a time trace of the amplitude of an oscillation is shown.
  • Target traces all have a slight decrease in power (variance) in the middle of the segment. This ERD cannot be detected with a linear transform of the raw samples, since the phase of the oscillation is unknown and variable.
  • Fig. 3b a quadratic expansion of the time trace is shown.
  • Fig. 3c shows the weights w of a linear classifier that is trained on these quadratic features to separate oscillations with an ERD from those without. Note that feature products with features from the middle segment carry all weight, and display diagonal banding at At's of multiples of a (half a) cycle's length.
  • the feature space of the present invention may be high-dimensional ('blow up'), requiring a large memory capacity and search space for training the detector.
  • kernel trick [1] known in the field of machine learning (ML), which allows us to work efficiently in high-dimensional features spaces by representing a linear classifier with implicit dot products that can be efficiently evaluated in high-dimensional feature spaces.
  • ML machine learning
  • the dot product between input vectors x and x' of a polynomial expansion ⁇ with degree d can be efficiently computed with the kernel trick:
  • the polynomial kernel is inhomogeneous, meaning that terms with degrees up to d are included in the expansion. Note that an explicit map ⁇ from the input space to the feature space is not needed. Instead, the kernel function k(x, x') can be specified directly, thereby inducing an implicit mapping from the input space to the feature space. Using a polynomial kernel to classify ERD and ERP effects
  • adaptive whitening 12 is applied to the input signal for removing correlations therein (measurement sensors providing EEG signals are often heavily correlated).
  • the whitening may further comprise adaptive sensor covariance estimation to account for e.g. changing background activity.
  • the adaptive sensor covariance estimation may have a rate of adaptation defined by a half-life of less than a desired value (e.g. less than 60 seconds, e.g. less than 50 seconds, e.g. 30 seconds) or a forgetting factor ( ⁇ ) between zero and one, i.e. ⁇ G
  • the whitening may comprise filtering 17 the input signal with a high-pass filter having a suitable band or cut-off frequency, e.g. a cut-off frequency of less than 5 Hz, e.g. lHz.
  • the method may in a further group of embodiments further comprise windowing the input signal using predetermined windowing parameters to bias use of primarily the newest parts of the input signal.
  • the window in principle is used with a (time) length between zero and infinity, hence the present invention does not impose a temporal limit for capturing samples up to a current point in time.
  • Fig. 1 discloses a very general description of the method according the present invention. Details and further embodiments will be explained and clarified in the following paragraphs, and with reference to the schematic drawing of Fig. 2.
  • the (optional) windowing step 17 of the present invention aims to remove parameters necessary in prior art methods that define temporal intervals of interest. Ideally, no limit is imposed and the classifier uses arbitrary long windows capturing all samples up to the current point in time. That is, the windows may have a length between zero and infinity.
  • the windowing comprises a variance biasing step, using a function that specifies the fraction of variance for a sample age.
  • the classifier is biased towards the last seconds by scaling the amplitude in the window such that the most recent h seconds are as important or influential as much as all the rest of the history.
  • Tikhonov regularization can be implemented for methods that only implement an £ 2 regularizer by mapping the features through r- r .
  • Fig. 4 shows a graph of an exemplary embodiment of a function / that specifies the fraction of variance for a sample age.
  • the number of features n in (12) that share a given sample age needs to reflect the increase in variance in feature space caused by increasing the input space with terms of age a in input space.
  • the growth in feature space caused by adding ⁇ ⁇ features in input space is the following for a polynomial kernel with degree
  • the whitening step 12 of the present invention aims to remove correlations in the input space. More precisely, the above presented feature space assumes that the features in input space are white, that is, they are uncorrelated and have unit variance. Temporally, the EEG displays an _1 spectrum, meaning that most power is contained in the lower frequencies. In an embodiment, a low order high-pass infinite impulse response (IIR) filter removes most power from these low frequencies and can be used to reduce temporal correlations in the EEG signal. Spatially, the signals are heavily correlated due to volume conductance. In a further embodiment, to remove the spatial correlations and to rescale the high-pass filtered signals to unit variance, a whitening transform P is used, based on the sensor covariance matrix C :
  • E is a continuous recording as introduced earlier in this document, wherein each row of E comprises the time series for a specific sensor.
  • the covariance can be adaptively estimated with a low-pass filter such as the exponentially weighted moving average (EMWA), see [8,7]:
  • EMWA exponentially weighted moving average
  • Fig. 5a and 5b show examples of fragments of biologically originating signals before and after whitening.
  • the different channels Before whitening (Fig. 5a), the different channels are heavily correlated and contain signals at different scales.
  • Fig 2. shows a more detailed embodiment of the method for learning BCI detectors according to the present invention, including the system parameters to be selected beforehand, i.e. predetermined whitening parameter 12a ( ⁇ , ti /2 ), predetermined windowing parameter 17a ( ⁇ , ti /2 ), predetermined polynomial kernel parameters 15a (d, c).
  • predetermined whitening parameter 12a ⁇ , ti /2
  • predetermined windowing parameter 17a ⁇ , ti /2
  • predetermined polynomial kernel parameters 15a d, c.
  • one or more raw EEG signals 11 are provided, wherein linear trends are removed from the raw EEG signals.
  • the data is subsequently filtered using a hi-pass filter 18 with a band or cut-off frequency of less than 5 Hz, e.g. 1 Hz (parameter 18a, Band).
  • the data is then whitened 12 based on adaptive covariance estimation, wherein the half-life t x / 2 for the whitener is set to e.g. 30 seconds.
  • the signals are subsequently windowed with an exponential decay, wherein the half life t 2 is set to 3 seconds for the function / specifying the fraction of variance for the sample age.
  • a support vector machine e.g. one-class SVM
  • the kernel method are used (block 16), wherein the classifier's weights w are learned from the EEG signals.
  • the high-pass cut-off frequency (band) and the rate of adaptation for the adaptive whitener ⁇ t 2 ) need to be set such that the neural signature is not filtered out. With a low cut-off and a long half-life for the whitener 12 this is generally the case.
  • the half- life for the variance decay 17 biases the detector 13, where we suspect that choosing a value encompassing the cognitive task is sufficient. Since we are not aware of any successful CFC-based BCIs, a quadratic kernel is presumably sufficient, but higher order kernels are conceivable in view of the present invention.
  • the SVM' s v- parameter controls the number of false negatives - a choice that is driven by the intended application (see e.g. [1 1] or [13]).
  • the default parameters support the learning of conventional BCI neural signatures, such as the P300, motor imagery and steady-state visually evoked potential (SSVEP).
  • the present invention method embodiments as described above may be implemented on a general purpose computer or special purpose processing system, e.g. in the form of a computer program product.
  • the computer program product comprises computer executable instructions, which when loaded on a processing system (adapted to properly receive or store the input signal) provide the processing system with the functionality of one of the present method embodiments.
  • the present invention may also be embodied as a brain computer interface comprising an input unit for receiving an input signal, and a processing unit connected to the input unit. Again, the processing unit is then arranged for executing the method according to the present invention embodiments.
  • the method embodiments may be specifically adapted for a dedicated application, by properly selecting the predetermined parameters as described above.
  • Applications of the present invention BCI method include, but are not limited to the use in an entertainment application (e.g. a computer game), for operating a further apparatus (e.g. a machine or an appliance), for training applications (e.g. using feedback training to a user), or for monitoring purposes (e.g. monitoring sleep deprivation in car or truck drivers).
  • an entertainment application e.g. a computer game
  • a further apparatus e.g. a machine or an appliance
  • training applications e.g. using feedback training to a user
  • monitoring purposes e.g. monitoring sleep deprivation in car or truck drivers.
  • Informal experimentation indicates that performance keeps improving with more training data, and with more than 3 of the 118 EEG channels.

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Abstract

La présente invention concerne un procédé permettant de fournir une interface cerveau-machine, et une interface cerveau-machine, comprenant un modèle de classification faisant partie d'un traitement en cascade. Le signal d'entrée comprend une signature neurale destinée à être détectée par le modèle de classification. L'obtention du modèle de classification comprend l'entraînement du modèle de classification à l'aide du signal d'entrée et l'attribution d'un ou plusieurs marqueurs au signal d'entrée à différents instants. Chaque marqueur indique si le signal d'entrée à l'instant associé au marqueur devrait être classifié comme activité cible. D'autres étapes de traitement consistent à blanchir le signal d'entrée à l'aide de paramètres de blanchiment pour réduire les corrélations temporelles et spatiales dans le signal d'entrée afin d'obtenir une série temporelle blanchie, spécifier un noyau polynomial à l'aide de paramètres de noyau polynomial, étape induisant un mappage de la série temporelle blanchie sur un espace de caractéristiques séparables de manière linéaire, et classifier l'espace de caractéristiques à l'aide de la sortie du noyau polynomial, des paramètres de classification et des pondérations.
PCT/NL2013/050762 2012-10-29 2013-10-29 Procédé et système pour une interface cerveau-machine Ceased WO2014069996A1 (fr)

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016033686A1 (fr) 2014-09-04 2016-03-10 University Health Network Procédé et système de traitement de l'activité cérébrale basé sur le signal et/ou de commande de dispositifs utilisateurs
CN107301675A (zh) * 2017-06-16 2017-10-27 华南理工大学 一种基于脑机接口的三维建模方法
CN110251119A (zh) * 2019-05-28 2019-09-20 深圳和而泰家居在线网络科技有限公司 分类模型获取方法、hrv数据分类方法、装置及相关产品
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CN110974221A (zh) * 2019-12-20 2020-04-10 北京脑陆科技有限公司 一种基于混合函数相关向量机的混合脑机接口系统
CN113762346A (zh) * 2021-08-06 2021-12-07 广东工业大学 一种融合脑区因果特征的半监督自闭症识别方法及系统
CN113762346B (zh) * 2021-08-06 2023-11-03 广东工业大学 一种融合脑区因果特征的半监督自闭症识别方法及系统
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