Scopus

A CNN Approach in Building EEG-Based Emotion Recognition System for AIoT Applications

Năm XB 2024 Tạp chí / Hội thảo Intelligence of Things: Technologies and Applications (ICIT 2024) LNDECT, volume 230, pp 123–133 Đơn vị CNTT DOI / Link https://doi.org/10.1007/978-3-031-75596-5_12 ↗

Tác giả

Tóm tắt

In practice, when deploying AIoT applications for emotion recognition based on EEG signals, the devices not only need to be mobile and wireless but often have a small number of electrodes to ensure portability, compactness, and energy efficiency. Additionally, the classification blocks using deep learning networks also need to be adjusted to achieve architectures with fewer parameters while maintaining effective recognition capabilities. Therefore, improvements to the deep learning architecture are necessary to meet these criteria. In this paper, we propose an architecture named EEG_MCIS, which is a 1D-CNN enhanced with Inception and Squeeze and Excitation blocks to suit the EEG data collected from devices with a small number of electrodes. The Inception block divides the 1D-CNN into two branches. The first branch with small kernel size aims to extract local features from the data, and the other with a larger kernel size to enable the model to perceive features over a wider range and capture relationships between more distant data points along the time axis. Meanwhile, the Squeeze and Excitation block leverages information about the importance of each channel, optimizing the classification process for compact EEG-based emotion recognition applications.

Tài liệu tham khảo

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