Hybrid EEG-fNIRS asynchronous brain-computer interface for multiple motor tasks

Tools

Buccino, A.P., Keles, H.O. and Omurtag, A. ORCID: 0000-0002-3773-8506, 2016. Hybrid EEG-fNIRS asynchronous brain-computer interface for multiple motor tasks. PLOS ONE, 11 (1): e0146610. ISSN 1932-6203

Preview

Text
PubSub10244_707a_Omurtag.PDF - Published version
Download (5MB) | Preview

Official URL: http://doi.org/10.1371/journal.pone.0146610

Abstract

Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm—Left-Arm—Right-Hand—Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs) have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.

Item Type:

Journal article

Publication Title:

PLOS ONE

Creators:

Buccino, A.P., Keles, H.O. and Omurtag, A.

Publisher:

Public Library of Science

Date:

5 January 2016

Volume:

Number: