Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification

Abstract

Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain–computer interface tasks to extract feature maps and classify the target output. However, the EEG analysis remains challenging since it requires consideration of various architectural design components that influence the representational ability of extracted features. This study proposes an EEG-based emotion classification model called the multi-kernel temporal and spatial convolution network (MultiT-S ConvNet). The multi-scale kernel is used in the model to learn various time resolutions, and separable convolutions are applied to find related spatial patterns. In addition, we enhanced both the temporal and spatial filters with a lightweight gating mechanism. To validate the performance and classification accuracy of MultiT-S ConvNet, we conduct subject-dependent and subject-independent experiments on EEG-based emotion datasets: DEAP and SEED. Compared with existing methods, MultiT-S ConvNet outperforms with higher accuracy results and a few trainable parameters. Moreover, the proposed multi-scale module in temporal filtering enables extracting a wide range of EEG representations, covering short- to long-wavelength components. This module could be further implemented in any model of EEG-based convolution networks, and its ability potentially improves the model’s learning capacity.

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“…When applying deep learning to extract features from EEG signals, researchers mostly focus on multi-scale convolution in the temporal domain and ignore the spatial relationships between channels (42)(43)(44). Introducing multi-scale spatial convolution can extract spatial features more efficiently, thereby improving model performance.…”

“…This technique can capture different levels of features at different scales, thereby enhancing the characterization ability of the model. Researchers have successfully applied multi-scale convolution to feature extraction, yielding favorable outcomes (42)(43)(44). For instance, Wu et al introduced a parallel multi-scale filter bank CNN for EEG classification, and achieved excellent classification performance (44).…”