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公开(公告)号：US20180005105A1
公开(公告)日：2018-01-04
申请号：US15615080
申请日：2017-06-06
CPC分类号： G06N3/0427 , A61F2/72 , G06F3/015 , G06K9/6297 , G06N3/063 , G06T7/277
摘要： A method of continuous decoding of motion for a direct neural interface. The method of decoding estimates a motion variable from an observation variable obtained by a time-frequency transformation of the neural signals. The observation variable is modelled using a HMM model whose hidden states include at least an active state and an idle state. The motion variable is estimated using a Markov mixture of experts where each expert is associated with a state of the model. For a sequence of observation vectors, the probability that the model is in a given state is estimated, and from this a weighting coefficient is deduced for the prediction generated by the expert associated with this state. The motion variable is then estimated by combination of the estimates of the different experts with these weighting coefficients.
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公开(公告)号：US20220110570A1
公开(公告)日：2022-04-14
申请号：US17450601
申请日：2021-10-12
摘要： The present invention relates to a method for calibrating on-line a direct neural interface implementing a REW-NPLS regression between an output calibration tensor and an input calibration tensor. The REW-NPLS regression comprises a PARAFAC iterative decomposition of the cross covariance tensor between the input calibration tensor and the output calibration tensor, each PARAFAC iteration comprising a sequence of M elementary steps (2401, 2401, . . . 240M) of minimisation of a metric according to the alternating least squares method, each elementary minimisation step relating to a projector and considering the others as constant, said metric comprising a penalisation term that is a function of the norm of this projector, the elements of this projector not being subjected to a penalisation during a PARAFAC iteration f not being penalisable during following PARAFAC iterations. Said calibration method makes it possible to obtain a predictive model of which the non-zero coefficients are sparse blockwise.
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公开(公告)号：US20160282941A1
公开(公告)日：2016-09-29
申请号：US15032546
申请日：2013-10-31
IPC分类号： G06F3/01 , A61B5/0476 , A61B5/04
CPC分类号： G06F3/015 , A61B5/04001 , A61B5/04012 , A61B5/0476 , A61B5/7203 , G06K9/00536
摘要： A direct neural interface system comprises: a signal acquisition subsystem for acquiring electrophysiological signals representative of neuronal activity of a subject's brain; and a processing unit for representing electrophysiological signals acquired over an observation time window in the form of a N-way data tensor, N being greater than or equal to two, and generating command signals for a machine by applying a regression model over the data tensor; wherein the processing unit is configured or programmed for generating command signals for a machine by applying Generalized Linear regression, with a nonlinear link function, over the data tensor. A method of interfacing a subject's brain to a machine by using such a direct neural interface system is provided.
摘要翻译： 直接神经接口系统包括：信号采集子系统，用于获取表示受试者脑部神经元活动的电生理信号; 以及处理单元，用于表示通过N次数据张量N的大小等于2的观测时间窗口获取的电生理信号，并且通过在数据张量上应用回归模型来生成机器的命令信号 ; 其中所述处理单元被配置或编程以通过在数据张量上应用具有非线性链接功能的广义线性回归来为机器产生命令信号。 提供了通过使用这样的直接神经接口系统将受试者的大脑与机器接口的方法。
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4.发明申请公开(公告)号：US20220207424A1
公开(公告)日：2022-06-30
申请号：US17563700
申请日：2021-12-28
发明人： Vincent ROUANNE , Tetiana AKSENOVA
摘要： The present invention relates to an adaptive training method of a brain computer interface. The ECoG signals expressing the neural command of the subject are preprocessed to provide at each observation instant an observation data tensor to a predictive model that deduces therefrom a command data tensor making it possible to control a set of effectors. A satisfaction/error mental state decoder predicts at each epoch a satisfaction or error state from the observation data tensor. The mental state predicted at a given instant is used by an automatic data labelling module to generate on the fly new training data from the pair formed by the observation data tensor and the command data tensor at the preceding instant. The parameters of the predictive model are subsequently updated by minimising a cost function on the training data thus generated.
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公开(公告)号：US20210064942A1
公开(公告)日：2021-03-04
申请号：US17011276
申请日：2020-09-03
发明人： Alexandre MOLY , Tetiana AKSENOVA
摘要： This invention relates to a method of calibrating a direct neural interface with continuous coding. The observation variable is modelled by an HMM model and the control variable is estimated by means of a Markov mixture of experts, each expert being associated with a state of the model.
During each calibration phase, the predictive model of each of the experts is trained on a sub-sequence of observation instants corresponding to the state with which it is associated, using an REW-NPLS (Recursive Exponentially Weighted N-way Partial Least Squares) regression model.
A second predictive model giving the probability of occupancy of each state of the HMM model is also trained during each calibration phase using an REW-NPLS regression method. This second predictive model is used to calculate Markov mixture coefficients during a later operational prediction phase.
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