TY - GEN
T1 - Deep Learning Models for Subject-Independent ERP-based Brain-Computer Interfaces
AU - Tuleuov, Adilet
AU - Abibullaev, Berdakh
PY - 2019/5/16
Y1 - 2019/5/16
N2 - Inter-subject variabilities in brain signals can significantly deteriorate the accuracy of brain-computer interface (BCI) systems. Every subject has different brain signals; also, the performance of a subject varies widely between sessions, within a session, between on-line and off-line settings, and even from epoch to epoch. Such variabilities arise in measurements obtained during different BCI sessions require subject and session specific decoder models. This work proposes three different deep learning models for subject-independent decoding of event-related potentials in electroencephalographic signals: 1) shallow convolutional neural network, 2) gated recurrent neural network, and 3) CNN-RNN-Net: a hybrid one-dimensional convolution and a gated recurrent unit model. Experimental results demonstrate that the proposed models outperform the conventional baseline models in decoding subject-independent data. Moreover, among the three models, the CNN-RNN-Net has shown improved classification results.
AB - Inter-subject variabilities in brain signals can significantly deteriorate the accuracy of brain-computer interface (BCI) systems. Every subject has different brain signals; also, the performance of a subject varies widely between sessions, within a session, between on-line and off-line settings, and even from epoch to epoch. Such variabilities arise in measurements obtained during different BCI sessions require subject and session specific decoder models. This work proposes three different deep learning models for subject-independent decoding of event-related potentials in electroencephalographic signals: 1) shallow convolutional neural network, 2) gated recurrent neural network, and 3) CNN-RNN-Net: a hybrid one-dimensional convolution and a gated recurrent unit model. Experimental results demonstrate that the proposed models outperform the conventional baseline models in decoding subject-independent data. Moreover, among the three models, the CNN-RNN-Net has shown improved classification results.
UR - http://www.scopus.com/inward/record.url?scp=85066730815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066730815&partnerID=8YFLogxK
U2 - 10.1109/NER.2019.8717088
DO - 10.1109/NER.2019.8717088
M3 - Conference contribution
AN - SCOPUS:85066730815
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 945
EP - 948
BT - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PB - IEEE Computer Society
T2 - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Y2 - 20 March 2019 through 23 March 2019
ER -