EEG classification with spiking neural network: Smaller, better, more energy efficient

Abstract

Real-time emotion recognition with electroencephalograph (EEG) has been an active field of research in recent years. In particular, deep learning has been shown to be effective in emotion classification tasks. However, the monitoring of EEG signals is a continuous process, there is a need for energy-efficient emotion classification methods. Compared with artificial neural networks (ANNs), spiking neural networks (SNNs), in which weight multiplications are replaced by additions, are more energy efficient. In this paper, we propose a near-lossless transfer learning method for SNNs, specially designed for EEG signals. Data is preprocessed, and its power spectral density (PSD) is extracted to represent the frequency domain of the raw EEG signal. Using a 3-layer pretrained SNN, running on the DEAP dataset, we achieved an accuracy of 78.87% and 76.5% for valence and arousal dimensions, respectively. By training a model based on one dimension and fine-tuning on another, we achieve an even higher accuracy, 82.75% for the valence and 84.22% for the arousal. As far as we know, our results yield the smallest SNN with the highest accuracy for this task to date. The power of our SNNs for valence and arousal dimensions is 13.8% that of our CNN-based solutions. The framework was developed by PyTorch and is available under an open-source license.1