TY - GEN
T1 - Performance investigation of brain-computer interfaces that combine EEG and fNIRS for motor imagery tasks
AU - Verma, Pooja
AU - Heilinger, Alexander
AU - Reitner, Patrick
AU - Grunwald, Johannes
AU - Guger, Christoph
AU - Franklin, David
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Brain-Computer Interfaces (BCI) have proved to be a promising tool for neurorehabilitation. However, BCIs based on conventional methods are not highly accurate and reliable, different brain activity patterns are not optimal for all the users of BCIs and has low information transfer rate. Several studies have shown that the combination of different brain signal acquisition methods can lead to higher performance of BCIs. In this paper, we aim to investigate whether the performance of BCI increases if we combine Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) simultaneously for classifying Motor Imagery (MI) tasks of right-versus left-hand grasping movement. The results show enhancement in classification accuracy using a multimodal approach of an EEG + fNIRS BCI with an average increase of approximately 8-10% compared to only EEG-based BCI. This indicates that the hybrid approach in Brain-Computer Interface is capable of enhancing the BCI performance.
AB - Brain-Computer Interfaces (BCI) have proved to be a promising tool for neurorehabilitation. However, BCIs based on conventional methods are not highly accurate and reliable, different brain activity patterns are not optimal for all the users of BCIs and has low information transfer rate. Several studies have shown that the combination of different brain signal acquisition methods can lead to higher performance of BCIs. In this paper, we aim to investigate whether the performance of BCI increases if we combine Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) simultaneously for classifying Motor Imagery (MI) tasks of right-versus left-hand grasping movement. The results show enhancement in classification accuracy using a multimodal approach of an EEG + fNIRS BCI with an average increase of approximately 8-10% compared to only EEG-based BCI. This indicates that the hybrid approach in Brain-Computer Interface is capable of enhancing the BCI performance.
UR - http://www.scopus.com/inward/record.url?scp=85076779453&partnerID=8YFLogxK
U2 - 10.1109/SMC.2019.8914083
DO - 10.1109/SMC.2019.8914083
M3 - Conference contribution
AN - SCOPUS:85076779453
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 259
EP - 263
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -