  Mental task identification and classification using single/limited channel(s)
electroencephalogram (EEG) signals in real-time play an important role in the
design of portable brain-computer interface (BCI) and neurofeedback (NFB)
systems. However, the real-time recorded EEG signals are often contaminated
with noises such as ocular artifacts (OAs) and muscle artifacts (MAs), which
deteriorate the hand-crafted features extracted from EEG signal, resulting
inadequate identification and classification of mental tasks. Therefore, we
investigate the use of recent deep learning techniques which do not require any
manual feature extraction or artifact suppression step. In this paper, we
propose a light-weight one-dimensional convolutional neural network (1D-CNN)
architecture for mental task identification and classification. The robustness
of the proposed architecture is evaluated using artifact-free and
artifact-contaminated EEG signals taken from two publicly available databases
(i.e, Keirn and Aunon ($K$) database and EEGMAT ($E$) database) and in-house
($R$) database recorded using single-channel neurosky mindwave mobile 2 (MWM2)
EEG headset in performing not only mental/non-mental binary task classification
but also different mental/mental multi-tasks classification. Evaluation results
demonstrate that the proposed architecture achieves the highest
subject-independent classification accuracy of $99.7\%$ and $100\%$ for
multi-class classification and pair-wise mental tasks classification
respectively in database $K$. Further, the proposed architecture achieves
subject-independent classification accuracy of $99\%$ and $98\%$ in database
$E$ and the recorded database $R$ respectively. Comparative performance
analysis demonstrates that the proposed architecture outperforms existing
approaches not only in terms of classification accuracy but also in robustness
against artifacts.

Signal Processing