EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI'23 CC6 Competition)
Creators
- 1. DFKI-RIC
- 2. SMT-University of Duisburg-Essen
- 3. DFKI-RIC, Robotics Lab-University of Bremen
- 4. DFKI-RIC, SMT-University of Duisburg-Essen
Description
This dataset was a part of the IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (IJCAI'23 Official Website). This dataset repository is divided into 3 versions:
- Version 1: Training data + Metadata
- Version 2: Test data
- Version 3: Complete dataset (EEG + EMG)
For more detailed information about the competition, please visit our competition webpage.
This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis.
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).
If you use our dataset, cite our paper.
Frontiers in Human Neuroscience DOI: 10.3389/fnhum.2024.1304311
BibTeX citation: