Last edited: May 2021
This is a demonstration of how to use EEGLAB with the public dataset BCI Competition IV-2a.
The BCI Competition IV-2a dataset, provided by the Laboratory of Brain-Computer Interfaces at Graz University of Technology, was used to evaluate the performance of the proposed framework.
This dataset was collected from nine right-handed subjects (A01-A09) using a 22-channel Ag/AgCl electrode EEG system at a sampling rate of 250 Hz. The dataset includes EEG recordings for left and right-hand motor imagery (MI) tasks, with 72 trials per task per subject.
If you find the figures or description useful for your research, please consider citing the following paper:
@inproceedings{hong2022deep,
title={A deep learning framework based on dynamic channel selection for early classification of left and right hand motor imagery tasks},
author={Hong, Jiazhen and Shamsi, Foroogh and Najafizadeh, Laleh},
booktitle={2022 44th Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
pages={3550--3553},
year={2022},
organization={IEEE}
}