Neighborhood based decision theoretic rough set under dynamic granulation for BCI motor imagery classification

Abstract

Brain Computer Interface is an interesting and important research field that has contributed widespread application systems. In the medical field, it is important for physically challenged persons to aid in rehabilitation and restoration. In Brain Computer Interface, computer acts as interface between brain signals and external device. The computer processes the brain signals and sends necessary instructions to external device. The external device helps in restoring the movement ability of patient. Motor imagery is the imagination of motor movements like hand, foot and tongue. There is an associated brain signal when the normal person moves their hand, foot and tongue. Similarly, there is an associated brain signal when the physically challenged person imagines moving their hand, foot and tongue. When this brain signal is analyzed by brain computer interface, it can facilitate motor movements through external device. The aim of this work is to analyze and classify the brain signals for motor movements to aid in rehabilitation and restoration. In this paper BCI Competition IV Dataset I, Dataset IIa, BCI Competition III Dataset IIIa and Neuroprosthetic EEG Dataset are analyzed A novel optimization technique with Neighborhood Decision Theoretic Rough Set under Dynamic Granulation is proposed for motor imagery classification. Neighborhood based Decision Theoretic Rough Set under Dynamic Granulation (NDTRS under DG) is hybrid approach combining two algorithms Neighborhood Rough Set and Decision Theoretic Rough Set under Dynamic Granulation ((DTRS under DG). Neighborhood Rough Set overcomes the drawback of discretization step in Rough Set. Decision Theoretic Rough Set under Dynamic Granulation algorithm has loss function for classification. The effectiveness of classification is improved since the loss function is involved in the construction of algorithm. The proposed method Neighborhood based Decision Theoretic Rough Set under Dynamic Granulation gives higher classification accuracy compared to existing approaches.