Electroencephalography (EEG) signals are crucial for investigating brain function and cognitive processes. This study aims to address the challenges of efficiently recording and analyzing high-dimensional EEG signals while listening to music to recognize emotional states. We propose a method combining Bidirectional Long Short-Term Memory (Bi-LSTM) networks with attention mechanisms for EEG signal processing. Using wearable EEG devices, we collected brain activity data from participants listening to music. The data was preprocessed, segmented, and Differential Entropy (DE) features were extracted. We then constructed and trained a Bi-LSTM model to enhance key feature extraction and improve emotion recognition accuracy. Experiments were conducted on the SEED and DEAP datasets. The Bi-LSTM-AttGW model achieved 98.28% accuracy on the SEED dataset and 92.46% on the DEAP dataset in multi-class emotion recognition tasks, significantly outperforming traditional models such as SVM and EEG-Net. This study demonstrates the effectiveness of combining Bi-LSTM with attention mechanisms, providing robust technical support for applications in brain-computer interfaces (BCI) and affective computing. Future work will focus on improving device design, incorporating multimodal data, and further enhancing emotion recognition accuracy, aiming to achieve practical applications in real-world scenarios.

本研究解决了在聆听音乐时高效记录和分析高维脑电图（EEG）信号以识别情感状态的挑战。我们提出了一种结合双向长短时记忆（Bi-LSTM）网络和注意机制的EEG信号处理方法，实验结果显示该方法在情感识别任务中显著优于传统模型。研究成果为脑机接口（BCI）和情感计算的应用提供了可靠的技术支持。

结合双向长短时记忆网络的可穿戴脑电图设备在音乐聆听过程中的脑活动记录