DEAP DIVE：用于脑电图评估的视觉Transformer数据集研究
DEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation
AI总结：
本研究针对低成本EEG设备的情绪预测需求，利用DEAP数据集的通道子集，通过连续小波变换将EEG转为尺度图并训练ViT模型，仅用12个通道实现91.57%以上的四象限情绪分类准确率，验证了通道精简后的优异性能。
摘要：
从脑信号中准确预测情绪有望实现改善心理健康、优化人机交互与情感计算等目标。通过神经信号进行情绪预测为传统方法提供了一种有前景的替代方案，传统方法如自我评估和面部表情分析可能存在主观性或模糊性。通过脑电图（EEG）测量脑活动提供了更直接、更无偏的数据源。然而，进行完整的EEG检测是一个复杂且资源密集的过程，这推动了具有简化测量功能的低成本EEG设备的兴起。本研究探讨了如何利用DEAP数据集的EEG通道子集，通过低成本EEG设备实现足够准确的情绪预测，而非依赖全套EEG测量设备。我们使用连续小波变换将EEG数据转换为尺度图，训练了一个用于情绪分类的视觉Transformer（ViT）模型。该模型仅用12个测量点（也称为通道），在预测4个象限（唤醒度和效价各分高/低两类）的任务中达到了91.57%以上的准确率。我们的研究清晰表明，与使用32个通道达到96.9%准确率的现有最优结果相比，大幅减少输入通道数量仍能取得较高的结果。复现我们代码的训练脚本可在此处获取：https://gitlab.kit.edu/kit/aifb/ATKS/public/AutoSMiLeS/DEAP-DIVE。
英文摘要：
Accurately predicting emotions from brain signals has the potential to achieve goals such as improving mental health, human-computer interaction, and affective computing. Emotion prediction through neural signals offers a promising alternative to traditional methods, such as self-assessment and facial expression analysis, which can be subjective or ambiguous. Measurements of the brain activity via electroencephalogram (EEG) provides a more direct and unbiased data source. However, conducting a full EEG is a complex, resource-intensive process, leading to the rise of low-cost EEG devices with simplified measurement capabilities. This work examines how subsets of EEG channels from the DEAP dataset can be used for sufficiently accurate emotion prediction with low-cost EEG devices, rather than fully equipped EEG-measurements. Using Continuous Wavelet Transformation to convert EEG data into scaleograms, we trained a vision transformer (ViT) model for emotion classification. The model achieved over 91,57% accuracy in predicting 4 quadrants (high/low per arousal and valence) with only 12 measuring points (also referred to as channels). Our work shows clearly, that a significant reduction of input channels yields high results compared to state-of-the-art results of 96,9% with 32 channels. Training scripts to reproduce our code can be found here: https://gitlab.kit.edu/kit/aifb/ATKS/public/AutoSMiLeS/DEAP-DIVE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
