Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 18; p. 6813
• Main Authors: Kim, Sungkyu, Kim, Tae-Seong, Lee, Won Hee
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2022; MDPI
• Subjects: Accuracy; affective computing; Arousal; Classification; convolutional neural network; Datasets; DEAP; Deep learning; EEG; Electroencephalography; Emotion recognition; Emotions; Machine learning; Neural networks; Object recognition (Computers); Pattern recognition; Physiology; Representations; Signal processing; Technology application; Wavelet transforms; South Korea
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22186813

Deep learning-based emotion recognition using EEG has received increasing attention in recent years. The existing studies on emotion recognition show great variability in their employed methods including the choice of deep learning approaches and the type of input features. Although deep learning models for EEG-based emotion recognition can deliver superior accuracy, it comes at the cost of high computational complexity. Here, we propose a novel 3D convolutional neural network with a channel bottleneck module (CNN-BN) model for EEG-based emotion recognition, with the aim of accelerating the CNN computation without a significant loss in classification accuracy. To this end, we constructed a 3D spatiotemporal representation of EEG signals as the input of our proposed model. Our CNN-BN model extracts spatiotemporal EEG features, which effectively utilize the spatial and temporal information in EEG. We evaluated the performance of the CNN-BN model in the valence and arousal classification tasks. Our proposed CNN-BN model achieved an average accuracy of 99.1% and 99.5% for valence and arousal, respectively, on the DEAP dataset, while significantly reducing the number of parameters by 93.08% and FLOPs by 94.94%. The CNN-BN model with fewer parameters based on 3D EEG spatiotemporal representation outperforms the state-of-the-art models. Our proposed CNN-BN model with a better parameter efficiency has excellent potential for accelerating CNN-based emotion recognition without losing classification performance.