Abstract
Brain Computer Interface (BCI) does not only help people of physical disability but also being used popularly in several other applications. Motor Imagery (MI) classification is one of the major contributions in BCI which works on segment of EEG signal within particular frequency band. Herein, feature selection plays important role in obtaining good classification results. In this article, Beta and Gamma frequency are considered with Statistical DWT (SDWT) based features for classification of EEG signals (MI classification) for patient monitoring, assistance healthcare services and daily living activities. Harmony search algorithm of feature selection is used to obtain the optimal feature set for classification of MI. The results show that frequency centric SDWT achieves average accuracy of 92.49% for weighted KNN (K-Nearest Neighbour) method. Comparison of accuracies before and after feature selection portrays that feature selection with harmony search improves the performance of proposed MI classification.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
