Key result
The proposed DSCNN-CAU model for mental activity classification achieved overall accuracies of 99.25%, 95.00%, 91.50%, and 89.25% across four EEG databases, outperforming existing techniques.
Why the study?
There is a need to deploy machine learning and deep learning techniques for mental activity classification on Internet of Things-enabled mobile devices for portability in real time.
The proposed DSCNN-CAU model enables accurate and efficient real-time mental activity classification on IoT-enabled mobile devices for portable BCI applications.
May enable portable EEG-BCI on IoT devices; leaves open prospective clinical validation before adoption.
Mental activity classification (MAC) based on electroencephalogram (EEG) is used in the brain–computer interface (BCI) and neurofeedback applications. For this purpose, machine learning and deep learning (DL) techniques are utilized in the previous studies. However, in real time, there is a need to deploy these techniques on Internet of Things (IoT)-enabled mobile devices for portability. Toward this aspect, we propose DL-based MAC using a depthwise separable convolutional neural network with a custom attention unit (DSCNN-CAU) and IoT implementation using a smartphone for portable BCI applications. The performance assessment on EEG signals from two public and two self-acquired databases demonstrates that the overall accuracies of 99.25%, 95.00%, and 91.50%, 89.25% are obtained, respectively. Evaluation results on smartphone depict that most of the real-time recorded EEG signals in self-acquired databases are classified correctly. Further comparative analysis demonstrates that the proposed model and the IoT implementation outperform the existing techniques in terms of accuracy, robustness against artifacts, latency, and battery current dissipation. A total current of 21 mAh is dissipated in EEG signal recording, processing, and event-based transmission to the server, and low latency of 71 ms is achieved in MAC. Further analysis of cross-database performance enables the selection of the <tex-math notation="LaTeX">FP1 </tex-math> channel for real-time MAC in IoT-based scenarios.
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Saini et al. (2022) studied Mental activity classification. DSCNN-CAU (depthwise separable convolutional neural network with a custom attention unit) vs. Existing techniques was evaluated on Classification accuracy. The proposed DSCNN-CAU model for mental activity classification achieved overall accuracies of 99.25%, 95.00%, 91.50%, and 89.25% across four EEG databases, outperforming existing techniques.
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