# CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG Emotion Recognition

## TL;DR

This paper introduces a new neural network model for EEG emotion recognition that improves accuracy and efficiency using attention mechanisms and dual-stream processing.

## Contribution

The CSA-SA-CRTNN model combines adaptive dual-stream convolutional-recurrent networks with attention mechanisms to enhance emotion recognition from EEG data.

## Key findings

- The model achieved 99.26% and 99.15% accuracy for arousal and valence in binary classification on the DEAP dataset.
- It achieved 98.63% accuracy on the SEED dataset, outperforming existing algorithms.
- The model is more efficient, offering better accuracy with lower computational resource consumption.

## Abstract

In recent years, EEG-based emotion recognition technology has made progress, but there are still problems of low model efficiency and loss of emotional information, and there is still room for improvement in recognition accuracy. To fully utilize EEG’s emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification and 97.69% and 98.05% in ternary classification, and on the SEED dataset, we achieved an accuracy of 98.63%, surpassing relevant algorithms. Additionally, the model’s efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.