On-Device Implementation for Deep-Learning-Based Cognitive Activity Prediction

Cognitive activity prediction (CAP) from electroencephalogram (EEG) signals is progressively utilized in the field of brain–computer interface (BCI) and mental health management. Various machine and deep learning methods have been proposed recently for CAP. However, since Internet-of-Things-based real-time BCI systems demand low latency, power, and portability, these methods need to be deployable on resource-constrained edge devices. Towards this aspect, we propose a real-time implementation of a lightweight 1-D convolutional neural network on an Arduino Due microcontroller for CAP from EEG signals. The performance evaluation on two public datasets and one real-time recorded dataset indicates that the proposed work achieves subject-independent prediction accuracies of 99.30%, 82.50%, and 99.02% in these datasets. Furthermore, the prediction of real-time recorded EEG signals is accurate for majority of the subjects. The proposed work outperforms the existing techniques and achieves low power consumption of
$\text{0.63}\,\text{W}$
in real-time on-device implementation with an average latency of
$\text{455.12}\,\text{ms}$
in model deployment, test output prediction, and activity-based transmission.