Auto-adaptation of ecog-based motor bci using neural response decoder: a cross-patient study - CEA - Commissariat à l’énergie atomique et aux énergies alternatives
Conference Papers Year : 2024

Auto-adaptation of ecog-based motor bci using neural response decoder: a cross-patient study

Abstract

Motor imagery brain-computer interfaces (BCIs) face challenges in practical application, notably in decoder training. Traditionally, decoders are trained in a supervised manner. This approach requires labeled data and restricts users to predefined actions during the training period. Moreover, regular decoders are needed. To tackle this, the auto-adaptive BCI (aBCI) infers training labels directly from brain signals using a neural response (NR) decoder, eliminating the need for supervised sessions. This study investigates the replicability of the aBCI and explores labeling strategies using electrocorticography data from three spinal cord injured patients across diverse paradigms. Results indicate performance variations among patients. The labeling strategy, focusing on correct neural responses (CNR), demonstrates significantly improved performance compared to correct/error neural responses (CENR) labeling strategy. Despite limitations of pseudo-online simulation, our findings underscore the aBCI's promise in advancing BCI technology.
Fichier principal
Vignette du fichier
9th_bci_conference_013.pdf (1.06 Mo) Télécharger le fichier
Origin Publisher files allowed on an open archive
Licence

Dates and versions

cea-04709829 , version 1 (11-10-2024)

Licence

Identifiers

Cite

Hugo Lafaye de Micheaux, Felix Martel, Fabien Sauter, Guillaume Charvet, Henri Lorach, et al.. Auto-adaptation of ecog-based motor bci using neural response decoder: a cross-patient study. 9th Graz Brain-Computer Interface Conference 2024, Sep 2024, Graz, Austria. pp.68-73, ⟨10.3217/978-3-99161-014-4-013⟩. ⟨cea-04709829⟩
76 View
12 Download

Altmetric

Share

More