Assessment of CSP-based two-stage channel selection approach and local transformation-based feature extraction for classification of motor imagery/movement EEG data

OpenAlex İngilizce

The main idea of brain-computer interfaces (BCIs) is to facilitate the lives of patients having difficulties to move their muscles due to a disorder of their motor nervous systems but healthy cognitive functions. BCIs are usually electroencephalography (EEG)-based, and the success of the BCIs relies on the precision of signal preprocessing, detection of distinctive features, usage of suitable classifiers and selection of effective channels. In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imagery/movement tasks. In the first stage of the channel selection, the channels were combined according to the neurophysiological information about brain functions acquired from the literature, then averaged and a single channel was formed. In the second stage, selective channels were specified with the common spatial pattern-linear discriminant analysis (CSP-LDA)-based sequential channel removal. After the channel selection phase, the feature extraction was carried out with local transformation-based methods (LTBM): local centroid pattern (LCP), one-dimensional-local gradient pattern (1D-LGP), local neighborhood descriptive pattern (LNDP) and one-dimensional-local ternary pattern (1D-LTP). The distinctions and deficiencies of these methods were compared with other methods in the literature and the classification performances of the k-nearest neighbor (k-NN) and the support vector machines (SVM) were evaluated. As a result, the proposed methods yielded the highest average classification accuracies as 99.34%, 95.95%, 98.66% and 99.90% with the LCP, 1D-LGP, LNDP and 1D-LTP when using k-NN, respectively. The two-stage channel selection and 1D-LTP method showed promising results for recognition of motor tasks. The LTBM will contribute to the development of EEG-based BCIs with the advantages of high classification accuracy, easy implementation and low computational complexity.