Optimal Sensor Set for Decoding Motor Imagery from EEG - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Optimal Sensor Set for Decoding Motor Imagery from EEG

Résumé

Brain-computer interfaces can be used to operate devices by detecting a person's intention from their brain activity. Decoding motor imagery (MI) from electroencephalogram (EEG) signals is a commonly used approach for this purpose. To reliably identify MI from EEG signals, a sufficient number of sensors is usually required. However, a large number of sensors increases the computational cost of discriminating MI classes. Furthermore, consumer-grade devices that measure EEG signals often employ a reduced number of sensors compared to medical- or research-grade devices. In this experimental study, we investigate the tradeoff between accuracy and complexity when decoding MI from a restricted number of EEG sensors. For this purpose, several decoding pipelines were trained on EEG data using different subsets of electrode locations employing well-established decoding methods. We found that there is no significant difference (p=[0.18−0.91]) in average decoding accuracy when using fewer sensors. The largest loss in performance for a single individual was a reduction in mean decoding accuracy of 0.1 when using 8 out of 64 available sensors. Decoding MI from a limited number of sensors is therefore feasible, highlighting the potential of using commercial sensor devices for this purpose to reduce both monetary and computational costs.

Dates et versions

Licence

Copyright (Tous droits réservés)

Identifiants

Citer

Arnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Romain Meeusen, et al.. Optimal Sensor Set for Decoding Motor Imagery from EEG. 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE; EMBS, Apr 2023, Baltimore, United States. pp.1-4, ⟨10.1109/NER52421.2023.10123875⟩. ⟨hal-04104118⟩
30 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More