Decoding Hand Trajectory from Primary Motor Cortex ECoG Using Time Delay Neural Network

Kifouche, Abdessalam; Vigneron, Vincent; Shamsollahi, Mohammad Bagher; Guessoum, Abderrezak

doi:10.1007/978-3-319-11071-4_23

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…First, we start our simulation with a single input as in a similar work presented in [48], where the best channel is selected with the correlation method (channel 21) which was the unique input to the TDNN but with a try error time regression. In this time, the best selected channel is given using the variation coefficient and the time shifting is defined by the template matching.…”

Section: Performance Assessment 61 Experiments Assessment With One Nmentioning

confidence: 99%

Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study

Kifouche

Guessoum

2020

IJECE

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Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system behavior. In this study, a recent multi-electrode technology allows the record of the simultaneous neuron activities in behaving animals. Intra-cortical data are processed according to these steps: spike detection and sorting, than desired action extraction from the rate of the obtained signal. We focus on the following important questions about (i) the possibility of linking the brain signal time events with some time-delayed mapping tools; (ii) the use of some suitable inputs than others for the decoder; (iii) a consideration of separated data or a special representation founded on multi-dimensional statistics. This paper concentrates mostly on the analysis of parallel spike train when certain critical hypotheses are ignored by the data for the working method. We have made efforts to define explicitly whether the underlying hypotheses are actually achieved. In this paper, we propose an algorithm to define the embedded memory order of NARX recurrent neural networks to the hand trajectory tracking process. We also demonstrate that this algorithm can improve performance on inference tasks.

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Section: Performance Assessment 61 Experiments Assessment With One Nmentioning

confidence: 99%

Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study

Kifouche

Guessoum

2020

IJECE

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“…TDNNs are feedforward NNs with delayed versions of inputs that implement a short-term memory mechanism (Waibel et al, 1989). Of these NNs, RNNs are highly accurate in BMI applications (Sanchez et al, 2004(Sanchez et al, , 2005Sussillo et al, 2012;Kifouche et al, 2014;Shah et al, 2019). Therefore, the present work designed an RNN with error feedback as the neural decoder.…”

Section: Introductionmentioning

confidence: 99%

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

Yang

Wang

et al. 2020

Front. Comput. Neurosci.

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Decoding Hand Trajectory from Primary Motor Cortex ECoG Using Time Delay Neural Network

Cited by 2 publications

References 6 publications

Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study

Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

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