Hand movement decoding from magnetoencephalographic signals for BCI applications

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• 作者：Marie-Caroline Schaeffer ; ^{mariecaroline.schaeffer@cea.fr" class="auth_mail" title="E-mail the corresponding author} ; Etienne Labyt ; Victor Rohu ; Nicolas Tarrin ; Ignacio Vergara ; Serpil Cokgungor ; Andrey Eliseyev ; Guillaume Charvet ; Corinne Mestais ; Alim-Louis Benabid ; Tetiana Aksenova.
• 关键词：BCI ; ECoG ; MEG
• 刊名：Neurophysiologie Clinique / Clinical Neurophysiology
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：46
• 期：2
• 页码：104
• 全文大小：46 K

文摘

Long-term BCI based on chronic electrocorticogram (ECoG) is one of the challenges of BCI project at CLINATEC^®/LETI/CEA. Brain signal decoding algorithms are crucial to convert the preserved tetraplegia patient's brain activity into motor command for an exoskeleton. Robust model identification methods have been designed in CLINATEC^®, and have already provided relevant models of dependence between neural activity and intended movement when applied on ECoG non-human primate (NHP) data. To prepare for BCI clinical application in patients, preliminary studies on human data have been carried out using non-invasive magnetoencephalography (MEG). Five able-bodied subjects participated in the study. Each subject performed two 4-minute sessions for each of the following paradigms: execution of real stereotypical movements after disappearance of a specific visual cue (1), execution of self-paced stereotypical movements (2). Additionally, cue-paced motor imaginary (3) was performed by one of the subjects. The stereotypical movements used for the three paradigms consisted in rectilinear trajectories of the hand from an idle position, and back (along axis x, y and z). Hand coordinates were tracked during real hand movement sessions. Scaled real movement templates were used as estimations of the imagined movements for paradigm (3). Brain activity was recorded by a MEG Elekta Neuromag system. Thirty-six sensors were selected over the subject's sensorimotor area. Time-frequency representations of the corresponding signals were computed by means of continuous wavelet transforms. A model between these brain activity features and hand coordinates was identified using Partial Least Squares combined with Generalized Linear Model. Cross-validation was performed to assess model accuracy. Preliminary results yield promising average correlation levels between predicted and real movement, suggesting that CLINATEC^® model identification methods are relevant for hand movement decoding from MEG signals in human subjects. Experiments in progress focus on closed-loop decoding sessions in which subjects aim at controlling avatar movement based on real-time decoding of their brain sensorimotor activity. This is the last validation step before going to tetraplegia patients with ECoG signals.