BCI Competition 2008 { Graz data set B

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Physics-Informed Attention Temporal Convolutional Network for EEG-Based Motor Imagery Classification (2023, methodology): “4) The proposed model achieves outstanding results in the BCI Competition IV-2a (BCI-2a) dataset [30].”
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “5% using the BCI Competition IV-2a dataset [131].”
Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces (2018, unknown): “The analyzed dataset is the one from BCI competition [25],”
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain–Machine Interfaces (2020, methodology): “Timing scheme of the BCI Competition IV-2a [14]. converted to a common format, and bundled in a software package.”
Multiclass Brain–Computer Interface Classification by Riemannian Geometry (2012, methodology): “We analyze the dataset IIa from the BCI competition IV [18].”
Deep learning for healthcare applications based on physiological signals: A review (2018, methodology): “, 2017 [95] EEG decoding and visualization CNN BCI competition IV dataset 2a [96] and measurement data Up 89.”
Toward Adaptive BCIs: Enhancing Decoding Stability via User State-Aware EEG Filtering (2025, unknown): “BNCI2014004 dataset corresponds to the well-known BCI Competition IV-2b benchmark [32].”