Abstract
Over the past twenty years, the various merits of brain–computer interface (BCI) have garnered much recognition in the industry and scientific institutes. An increase in the quality of life is the key benefit of BCI utilization. The majority of the published works are associated with the examination and assessment of classification algorithms due to the ever-increasing interest in electroencephalography-based (EEG) BCIs. Yet, another objective is to offer guidelines that aid the reader in picking the best-suited classification algorithm for a given BCI experiment. For a given BCI system, selecting the best-suited classifier essentially requires an understanding of the features to be utilized, their properties, and their practical uses. As a feature extraction method, the common spatial pattern (CSP) will project multichannel EEG signals into a subspace to highlight the variations between the classes and minimize the similarities. This work has evaluated the efficacy of various classification algorithms like Naive Bayes, k-nearest neighbor classifier, classification and regression tree (CART), and AdaBoost for the BCI framework. Furthermore, the work has offered the proposal for channel selection with recursive feature elimination.
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Subramanian, S.C., Daniel, D. (2022). A Framework for Enhancing Classification in Brain–Computer Interface . In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 111. Springer, Singapore. https://doi.org/10.1007/978-981-16-9113-3_48
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DOI: https://doi.org/10.1007/978-981-16-9113-3_48
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