Exploration of Many-Objective Feature Selection for Recognition of Motor Imagery Tasks

Brain–Computer Interfacing helps in creation of a communication pathway between brain and external device such that the biological modality of performing the task could be bypassed. This necessitates fast and reliable decoding of brain signals which mandate feature selection to play a crucial role. The literature discloses the improvement in performance of left/right motor imagery signal classification with many-objective feature selection where several classification performance metrics have been maximized for obtaining a good quality feature set. This work analyses the classification performance by varying the feature dimension and number of objectives. A recent many-objective optimization coupled with objective reduction algorithm viz. \(\alpha \)-DEMO has been used for modeling the feature selection as an optimization problem with six objectives. The results obtained in this work have been statistically validated by Friedman Test.