脑电信号与其他信号结合的多模态大模型

目前，以脑电信号为基础结合其他多模态信号的相关研究不断深入，同时大模型出色的表现也引起了脑电领域相关研究者的注意。如何利用多模态大模型的能力来解决脑电研究的相关问题，论文《Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Health》给出了一个较为新颖的结合方式。

一、本文的出发点：

1、多模态数据联合的必要性：结合访谈音频数据可以更好地了解患者的情况，模拟医生的诊断场景

2、利用大模型的zero-shot/few-shot能力：通过少量注入提示信息，把EEG和常见的LLM信息结合在一起。

二、本文的研究背景：

随着LLM相关模型研究的发展，已经有将EEG转为文本形式用于LLM的prompt注入以此来进行脑电分类的先例，因此本文考虑将更多模态的信息转为已有的LLM能接受的输入形式，然后进行zero-shot/few-shot的推理。

三、本文的创新点：

多模态数据的形式转换以结合LLM的能力：将脑电信号转换为LLM能理解的图形数据；将音频信号通过梅尔倒谱系数的计算输入特征的文本数据；同时将对话内容利用ASR转为文本数据输入。

四、本文采用的数据集：

MODMA：

抑郁二分类数据集；音频数据+EEG数据

PME4:

愤怒、恐惧、厌恶、悲伤、快乐、惊讶和中性状态七种情绪分类数据集；音频+视频（未公开）+EEG+EMG数据

LUMED-2:

中性、快乐、悲伤三种情绪分类数据集；面部表情+EEG+皮肤电反应数据

五、利用GPT大模型进行few-shot learning的示例：

六、实验结果

参考文献：Hu Y, Zhang S, Dang T, et al. Exploring large-scale language models to evaluate eeg-based multimodal data for mental health[C]//Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2024: 412-417.

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