EEG Classification Using Modified KNN Algorithm

Automatic classification of EEG signals is required for the early identification of various disorders. In this research paper, statistical denoising and a modified KNN algorithm are coupled to create a smart method for classifying the EEG signal in order to accurately detect disorders. The EEG data are denoised statistically to produce a clearer signal, and the modified KNN algorithm is then used to classify the appropriate disease features with the aid of a classification block. The suggested algorithm's detection accuracy is compared to the detection accuracy of other existing algorithms, demonstrating the algorithm's effectiveness.