School of Mechatronic Engineering and Automation, School of Medicine, Research Center of Brain-Computer Engineering, Shanghai University, Shanghai, China.
Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
Sci Data. 2022 Sep 1;9(1):531. doi: 10.1038/s41597-022-01647-1.
In building a practical and robust brain-computer interface (BCI), the classification of motor imagery (MI) from electroencephalography (EEG) across multiple days is a long-standing challenge due to the large variability of the EEG signals. We collected a large dataset of MI from 5 different days with 25 subjects, the first open-access dataset to address BCI issues across 5 different days with a large number of subjects. The dataset includes 5 session data from 5 different days (2-3 days apart) for each subject. Each session contains 100 trials of left-hand and right-hand MI. In this report, we provide the benchmarking classification accuracy for three conditions, namely, within-session classification (WS), cross-session classification (CS), and cross-session adaptation (CSA), with subject-specific models. WS achieves an average classification accuracy of up to 68.8%, while CS degrades the accuracy to 53.7% due to the cross-session variability. However, by adaptation, CSA improves the accuracy to 78.9%. We anticipate this new dataset will significantly push further progress in MI BCI research in addressing the cross-session and cross-subject challenge.
在构建实用且稳健的脑机接口 (BCI) 时，由于脑电图 (EEG) 信号的高度可变性，因此在多天内对运动想象 (MI) 进行分类是一个长期存在的挑战。我们收集了来自 25 名受试者的 5 天的大量 MI 数据，这是第一个解决跨 5 天和大量受试者的 BCI 问题的公开数据集。该数据集包含每个受试者来自 5 个不同日子（相隔 2-3 天）的 5 个会话数据。每个会话包含 100 次左手和右手 MI 试验。在本报告中，我们提供了三种条件的基准分类准确性，即会话内分类 (WS)、跨会话分类 (CS) 和跨会话自适应 (CSA)，使用特定于主题的模型。WS 的平均分类准确性高达 68.8%，而 CS 由于跨会话的可变性而将准确性降低到 53.7%。但是，通过自适应，CSA 将准确性提高到 78.9%。我们预计这个新数据集将在解决跨会话和跨主题挑战方面，极大地推动 MI BCI 研究的进一步发展。
