EEG Emotion Copilot: Pruning LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation
<|reference_start|>EEG Emotion Copilot: Pruning LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation: In the fields of affective computing (AC) and brain-machine interface (BMI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including real-time processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system leveraging a lightweight large language model (LLM) operating in a local setting. The system is designed to first recognize emotional states directly from EEG signals, subsequently generate personalized diagnostic and treatment suggestions, and finally support the automation of electronic medical records. The proposed solution emphasizes both the accuracy of emotion recognition and an enhanced user experience, facilitated by an intuitive interface for participant interaction. We further discuss the construction of the data framework, model pruning, training, and deployment strategies aimed at improving real-time performance and computational efficiency. Privacy concerns are also addressed, with a focus on ethical data collection, processing, and the protection of users' personal information. Through these efforts, we aim to advance the application of AC in the medical domain, offering innovative approaches to mental health diagnostics and treatment.<|reference_end|>
@article{chen2024eeg,
    title={EEG Emotion Copilot: Pruning LLMs for Emotional EEG Interpretation with
  Assisted Medical Record Generation},
    author={Hongyu Chen, Weiming Zeng, Chengcheng Chen, Luhui Cai, Fei Wang, Lei
  Wang, Wei Zhang, Yueyang Li, Hongjie Yan, Wai Ting Siok and Nizhuan Wang},
    journal={arXiv preprint arXiv:2410.00166},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00166},
    primaryClass={cs.CV}
}