Bi-CapsNet: A Binary Capsule Network for EEG-based Emotion Recognition.

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Abstract

In recent years, deep learning has gained widespread attention in electroencephalogram (EEG)-based emotion recognition. However, deep learning methods are usually time-consuming with a large amount of memory usage, which obstructs their practical usage on resource-constrained devices. In this paper, we propose a binary capsule network (Bi-CapsNet) for EEG emotion recognition with low computational cost and memory usage. The Bi-CapsNet binarizes 32-bit weights and activations to 1 bit, and replaces floating-point operations with efficient bitwise operations. To address the issue of function discontinuity in backward propagation, we use a continuous function to approximate the binarization process. Two popular EEG emotion databases, namely, DEAP and DREAMER, are used for performance evaluation. In comparison to its full-precision counterpart, the Bi-CapsNet achieves a reduction on the computational cost and a reduction on the memory usage, while with only a 1% drop on the recognition accuracy. Compared to some state-of-the-art EEG emotion recognition methods, the proposed method obtains more competitive performance. In addition, the Bi-CapsNet is implemented on a mobile phone via an open-source binary inference framework named Bolt, and it achieves an  ∼ 5× inference acceleration in comparison to its full-precision counterpart.