Analysing Enhanced EEG Based Brain Computer Interface for Motor Imagery Tasks Using Statistical Analysis

BCIs have emerged as a useful tool for helping people with neurological disorders like epilepsy, ALS, and cerebral palsy, who have severely limited communication, by analyzing EEG signals and turning them into actionable signals. This paper aims to investigate the application of BCIs in analyzing EEG signals and designing them to give meaningful signals to the users. Five healthy, right-handed university students (18–21 years) were selected for the study and were asked to spell the words mentally without vocalizing for four cognitive tasks; forward, stop, left, and right. At 100 Hz, EEG signals were sampled and pre-processed with a notch filter and feature extraction was done using DWT to extract important features and KNN was used for classification. All the subjects showed high accuracy with more than 90% and maximum accuracy of 95.89% was obtained by Subject S3. Standard deviations between 1.32 to 1.41, which indicate low variability in performance among all the subjects. These results showed that the DWT with KNN combination can be used for real-time BCI applications and can offer a reliable communication method for people with motor impairment and disabilities.