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

Abdessalam Kifouche (1) , Vincent Vigneron (1) , Mohammad-Bagher Shamsollahi (2) , Abderrezak Guessoum (3)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
2 BiSIPL - Biomedical Signal and Image Processing Laboratory [Teheran]
3 LATSI - Laboratoire de Traitement de Signal et Imagerie [Blida]

Brain-machines - also termed neural prostheses, could potentially increase substantially the quality of life for people suffering from motor disorders or even brain palsy. In this paper we investigate the non-stationary continuous decoding problem associated to the rat's hand position. To this aim, intracortical data (also named ECoG for electrocorticogram) are processed in successive stages: spike detection, spike sorting, and intention extraction from the firing rate signal. The two important questions to answer in our experiment are (i) is it realistic to link time events from the primary motor cortex with some time-delay mapping tool and are some inputs more suitable for this mapping (ii) shall we consider separated channels or a special representation based on multidimensional statistics. We propose our own answers to these questions and demonstrate that a nonlinear representation might be appropriate in a number of situations.

Mots clés: BMI; nonlinear regression; spikes; Time Delay Neural Network

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Abdessalam Kifouche, Vincent Vigneron, Mohammad-Bagher Shamsollahi, Abderrezak Guessoum. Decoding Hand Trajectory from Primary Motor Cortex ECoG Using Time Delay Neural Network. 15th International Conference on Engineering Applications of Neural Networks (EANN 2014), Sep 2014, Sofia, Bulgaria. pp.237--247, ⟨10.1007/978-3-319-11071-4_23⟩. ⟨hal-01061779⟩