Design and Implementation of an Emotion Analysis System Based on EEG Signals

TL;DR

This paper presents a novel EEG-based emotion analysis system using a deep learning model with attention mechanisms, achieving high accuracy in classifying four emotional states with a user-friendly hardware setup.

Contribution

It introduces the ACPA-ResNet model combining attention and residual networks for improved EEG emotion classification, along with a practical hardware system for signal acquisition and processing.

Findings

Achieved an average classification accuracy of 95.1%.

Demonstrated stable EEG signal acquisition and wireless transmission.

Enhanced emotion recognition by adaptive channel weighting.

Abstract

Traditional brain-computer systems are complex and expensive, and emotion classification algorithms lack repre-sentations of the intrinsic relationships between different channels of electroencephalogram (EEG) signals. There is still room for improvement in accuracy. To lower the research barrier for EEG and harness the rich information embedded in multi-channel EEG, we propose and implement a simple and user-friendly brain-computer system for classifying four emotions: happiness, sorrow, sadness, and tranquility. This system utilizes the fusion of convolutional attention mechanisms and fully pre-activated residual blocks, termed Attention-Convolution-based Pre-Activated Residual Network (ACPA-ResNet).In the hardware acquisition and preprocessing phase, we employ the ADS1299 integrated chip as the analog front-end and utilize the ESP32 microcontroller for initial EEG signal processing. Data is wirelessly transmitted to a PC through UDP protocol for further preprocessing. In the emotion analysis phase, ACPA-ResNet is designed to automatically extract and learn features from EEG signals, thereby enabling accurate classification of emotional states by learning time-frequency domain characteristics. ACPA-ResNet introduces an attention mechanism on the foundation of residual networks, adaptively assigning different weights to each channel. This allows it to focus on more meaningful EEG signals in both spatial and channel dimensions while avoiding the problems of gradient dispersion and explosion associated with deep network architectures.Through testing on 16 subjects, our system demonstrates stable EEG signal acquisition and transmission. The novel network significantly enhances emotion recognition accuracy, achieving an average emotion classification accuracy of 95.1%.