Characterization of four-class motor imagery EEG data for the BCI-competition 2005

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Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, unknown): “been applied as a preprocessing of covariance matrices from EEG data [53] and followed by a simple Minimum Distance”
BCILAB: a platform for brain–computer interface development (2013, methodology): “Dataset A.2 is the dataset IIIa 4 of the BCI competition III, recorded at Graz University [47].”
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms (2011, background): “2) Data Set IIIa, BCI Competition III: Data set IIIa [23], from BCI competition III [6], comprises EEG signals from three”
A novel deep learning approach for classification of EEG motor imagery signals (2017, methodology): “In the classification part, many traditional algorithms like support vector machine (SVM) [2, 16], linear discriminant analysis (LDA) [17] and Bayesian classifier [18] have been employed in different studies [8, 15, 19].”
Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces (2009, unknown): “In the past, model range of 3 to 9 (see Schl¨ogl et al. (1997); Schl¨ogl (2000); Schl¨ogl et al. (2005)) have been used.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “Thus, the movement of those body limbs via imagination can be controlled by BCI applications (Schlögl et al., 2005 ).”