TY - JOUR
T1 - Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control
T2 - An offline study in patients with tetraplegia
AU - Blokland, Y.
AU - Spyrou, L.
AU - Thijssen, D.
AU - Eijsvogels, T.
AU - Colier, W.
AU - Floor-Westerdijk, M.
AU - Vlek, R.
AU - Bruhn, J.
AU - Farquhar, J.
PY - 2014
Y1 - 2014
N2 - Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the 'attempted movement' condition was replaced with 'actual movement.' A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research. © 2013 IEEE.
AB - Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the 'attempted movement' condition was replaced with 'actual movement.' A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research. © 2013 IEEE.
U2 - 10.1109/TNSRE.2013.2292995
DO - 10.1109/TNSRE.2013.2292995
M3 - Article
SN - 1534-4320
VL - 22
SP - 222
EP - 229
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 2
M1 - 6678785
ER -