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Brain-Computer Interface systems based on motor imagery are able to identify an individual’s intent to initiate control through the classification of encephalography patterns. Correctly classifying such patterns is instrumental and strongly depends in a robust machine learning block that is able to properly process the features extracted from a subject’s encephalograms. The main objective of this work is to provide an overall view on machine learning stages, aiming to answer the following question: “What are the steps in the classification process that we should worry about?”. The obtained results suggest that future research in the field should focus on two main aspects: exploring techniques for dimensionality reduction, in particular, supervised linear approaches, and evaluating adequate validation schemes to allow a more precise interpretation of results.

the international journal of computational intelligence systems/international journal of computational intelligence systems

Improving the Classifier Performance in Motor Imagery Task Classification: What are the steps in the classification process that we should worry about?