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Mental task identification and classification using single/limited channel(s)electroencephalogram (EEG) signals in real-time play an important role in thedesign of portable brain-computer interface (BCI) and neurofeedback (NFB)systems. However, the real-time recorded EEG signals are often contaminatedwith noises such as ocular artifacts (OAs) and muscle artifacts (MAs), whichdeteriorate the hand-crafted features extracted from EEG signal, resultinginadequate identification and classification of mental tasks. Therefore, weinvestigate the use of recent deep learning techniques which do not require anymanual feature extraction or artifact suppression step. In this paper, wepropose a light-weight one-dimensional convolutional neural network (1D-CNN)architecture for mental task identification and classification. The robustnessof the proposed architecture is evaluated using artifact-free andartifact-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 classificationbut also different mental/mental multi-tasks classification. Evaluation resultsdemonstrate that the proposed architecture achieves the highestsubject-independent classification accuracy of $99.7\%$ and $100\%$ formulti-class classification and pair-wise mental tasks classificationrespectively in database $K$. Further, the proposed architecture achievessubject-independent classification accuracy of $99\%$ and $98\%$ in database$E$ and the recorded database $R$ respectively. Comparative performanceanalysis demonstrates that the proposed architecture outperforms existingapproaches not only in terms of classification accuracy but also in robustnessagainst artifacts.

Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG