用扩散模型生成运动想象脑电数据，提升脑机接口训练效果
Synthetic EEG Generation using Diffusion Models for Motor Imagery Tasks
- 用扩散模型从噪声重建脑电信号，模拟运动想象任务
- 生成数据分类准确率超95%，与真实信号相关性高
- 适合数据稀缺场景下的脑机接口研究者使用
脑电图（EEG）是脑机接口（BCI）中广泛使用的无创脑活动捕捉技术，但高质量数据的采集受限于传感器成本、采集时间及个体差异。为解决此问题，本研究提出基于扩散概率模型（DDPM）生成与运动想象任务相关的合成脑电信号。方法包括对真实脑电数据预处理，训练扩散模型从噪声中重建脑电通道，并通过信号级和任务级指标评估生成质量。采用KNN、CNN和U-Net分类器对比合成数据与真实数据在分类任务中的表现。结果表明，生成数据分类准确率超过95%，均方误差低，与真实信号相关性高。实验验证了扩散模型生成的合成脑电数据能有效补充数据集，提升基于EEG的脑机接口分类性能，缓解数据稀缺问题。
原文摘要 · Abstract (English)
Electroencephalography (EEG) is a widely used, non-invasive method for capturing brain activity, and is particularly relevant for applications in Brain-Computer Interfaces (BCI). However, collecting high-quality EEG data remains a major challenge due to sensor costs, acquisition time, and inter-subject variability. To address these limitations, this study proposes a methodology for generating synthetic EEG signals associated with motor imagery brain tasks using Diffusion Probabilistic Models (DDPM). The approach involves preprocessing real EEG data, training a diffusion model to reconstruct EEG channels from noise, and evaluating the quality of the generated signals through both signal-level and task-level metrics. For validation, we employed classifiers such as K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), and U-Net to compare the performance of synthetic data against real data in classification tasks. The generated data achieved classification accuracies above 95%, with low mean squared error and high correlation with real signals. Our results demonstrate that synthetic EEG signals produced by diffusion models can effectively complement datasets, improving classification performance in EEG-based BCIs and addressing data scarcity.
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