EEG and EMG dataset for the detection of errors introduced by an active orthosis device

This dataset is a part of the training data for the IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (IJCAI'23 Official Website). This dataset repository is divided into 2 versions: <strong><em>Version 1: </em>Training data + Metadata</strong> <strong><em>Version 2: </em>Test data </strong> <strong>N.B.: </strong> <strong>After conducting a small survey to determine the willingness of the participating teams to travel to Macao, it became evident that a significant number of them preferred not to travel. With this in mind, we have decided to modify the initial plan for the online stage of the competition wherein the participating teams can participate from anywhere on Earth. Hope that this motivates more teams to participate. For more detailed information, please visit our competition webpage.</strong> <strong>Although the registration for the offline stage is officially closed, if you still wish to participate, please reach out to us via the contact form available on our webpage.</strong> This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. This is only a part of the complete dataset which also contains electromyogram (EMG) data and the complete dataset will be made public after the end of the competition. The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/). For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). <strong>If you use our dataset, cite our paper.</strong> arXiv-issued DOI: https://doi.org/10.48550/arXiv.2305.11996 BibTeX citation: @misc{kueper2023eeg,<br> title={EEG and EMG dataset for the detection of errors introduced by an active orthosis device}, <br> author={Niklas Kueper and Kartik Chari and Judith Bütefür and Julia Habenicht and Su Kyoung Kim and Tobias Rossol and Marc Tabie and Frank Kirchner and Elsa Andrea Kirchner},<br> year={2023},<br> eprint={2305.11996},<br> archivePrefix={arXiv},<br> primaryClass={cs.HC}<br> }本数据集为IJCAI 2023竞赛CC6赛道“IntEr-HRI：人机交互中的内在误差评估”（IJCAI'23官方网站）的训练数据之一。本数据集仓库分为两个版本：版本1：训练数据 + 元数据；版本2：测试数据。 备注：此前我们开展了一项小型调研，以了解参赛团队赴澳门参赛的意愿，结果显示多数团队不愿前往。基于此，我们调整了竞赛线上赛段的初始方案，参赛团队可在全球任意地点参与本次赛事。希望此举能吸引更多团队参赛。如需了解更多详细信息，请访问我们的竞赛官网。 尽管线下赛段的注册已正式截止，但若您仍希望参赛，请通过官网提供的联系表单与我们取得联系。 本数据集包含8名受试者的脑电图（electroencephalogram, EEG）数据录制结果，受试者在主动式矫形器辅助下完成右臂运动。本数据集仅为完整数据集的一部分，完整数据集还包含肌电图（electromyogram, EMG）数据，将于竞赛结束后公开。 矫形器辅助的运动为肘关节活动，即右臂的屈伸运动。在矫形器主动带动受试者手臂运动的过程中，我们会在短时间内故意引入误差，此时矫形器会向相反方向运动。此类误差简单易识别。本次提供了EEG与EMG数据。 录制的EEG数据遵循BrainVision Core Data Format 1.0标准，包含二进制数据文件（.eeg）、头文件（.vhdr）及标记文件（.vmrk），详情可参考https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/。为便于使用，该数据可导出为广泛采用的BIDS格式。此外，针对数据分析、处理与分类任务，提供了两种主流工具选择：基于Python的MNE及基于MATLAB的EEGLAB。 若您使用本数据集，请引用我们的论文。 arXiv发布的DOI：https://doi.org/10.48550/arXiv.2305.11996 BibTeX引用格式： @misc{kueper2023eeg, title={EEG and EMG dataset for the detection of errors introduced by an active orthosis device}, author={Niklas Kueper and Kartik Chari and Judith Bütefür and Julia Habenicht and Su Kyoung Kim and Tobias Rossol and Marc Tabie and Frank Kirchner and Elsa Andrea Kirchner}, year={2023}, eprint={2305.11996}, archivePrefix={arXiv}, primaryClass={cs.HC} }