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Motor-imagery based brain-computer interfaces (MI-BCI) have the potential tobecome ground-breaking technologies for neurorehabilitation, thereestablishment of non-muscular communication and commands for patientssuffering from neuronal disorders and disabilities, but also outside ofclinical practice, for video game control and other entertainment purposes.However, due to the noisy nature of the used EEG signal, reliable BCI systemsrequire specialized procedures for features optimization and extraction. Thispaper compares the two approaches, the Common Spatial Patterns with LinearDiscriminant Analysis classifier (CSP-LDA), widely used in BCI for extractingfeatures in Motor Imagery (MI) tasks, and the Dual-Augmented Lagrangian (DAL)framework with three different regularization methods: group sparsity with rowgroups (DAL-GLR), dual-spectrum (DAL-DS) and l1-norm regularization (DAL-L1).The test has been performed on 7 healthy subjects performing 5 BCI-MI sessionseach. The preliminary results show that DAL-GLR method outperforms standardCSP-LDA, presenting 6.9% lower misclassification error (p-value = 0.008) anddemonstrate the advantage of DAL framework for MI-BCI.

Performance of Dual-Augmented Lagrangian Method and Common Spatial Patterns applied in classification of Motor-Imagery BCI