School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea.
School of Computer Science and Electrical Engineering, Handong Global University, 558 Handong-ro Buk-gu, Pohang Gyeongbuk 37554, Korea.
Gigascience. 2017 Jul 1;6(7):1-8. doi: 10.1093/gigascience/gix034.
Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states.
We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information.
Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.
大多数脑机接口（BCI）研究的调查人员认为，BCI 可以通过皮层诱导的神经元活动来实现，但不能通过诱发的神经元活动来实现。基于运动想象（MI）的 BCI 是 BCI 的标准概念之一，因为用户可以通过想象运动来产生诱导活动。然而，不同会话和不同受试者之间的性能变化过于严重，难以轻易克服；因此，对 BCI 性能变化进行基本的理解和研究是必要的，以找到性能变化的关键证据。在这里，我们不仅提供了 52 名受试者的 MI-BCI 的 EEG 数据集，还提供了心理和生理问卷、EMG 数据集、3D-EEG 电极的位置以及非任务相关状态的 EEG 的结果。
我们通过使用坏试次数百分比、事件相关去同步/同步（ERD/ERS）分析和分类分析来验证我们的 EEG 数据集。在常规剔除坏试后，我们显示了感觉区域的对侧 ERD 和同侧 ERS，这是 MI 的已知模式。最后，我们表明 73.08%的数据集（38 名受试者）包含了相当有区分度的信息。
我们的 EEG 数据集包含了确定统计显著性所需的信息；它们由区分度较好的数据集（38 名受试者）和区分度较差的数据集组成。这些可能为研究人员提供机会，研究与 MI-BCI 性能变化相关的人为因素，并通过使用元数据（包括问卷、EEG 坐标和非任务相关状态的 EEG）来实现从一个受试者到另一个受试者的转移。
