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 0.63 W in real-time on-device implementation with an average latency of 455.12 ms in model deployment, test output prediction, and activity-based transmission.

Automated summary

In this study, a lightweight 1-D convolutional neural network method is proposed for predicting cognitive activity from electroencephalogram signals. The real-time recorded results demonstrate high prediction accuracy and low power consumption on resource-constrained edge devices.