Enhance motor imagery EEG classification using DWT and chirplet transform

Abstract

Brain Computer Interfaces promote seamless interaction between individuals with movement limitations and their surrounding environment by transforming electroencephalography signals derived using Motor Imagery. The procedure relies on accurately classifying various MI activities, which requires dependable approaches for EEG signal classification to be continuously improved. In this paper, discrete wavelet transform, and chriplet transform are proposed to enhance the performance of test system with demonstrating critical importance of time-related data and is implemented in visual studio code python. The proposed method holds 91% efficiency, accuracy 94.8% for CBCIC and 93.72% for BCI Competition IV Dataset with response time of 1.03 sec.