EEG Classification Using Modified KNN Algorithm
B M Thejaswini, T. Y. Satheesha, Sathish Bhairannawar
Abstract
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.
Topics & Concepts
Computer scienceElectroencephalographyStatistical classificationArtificial intelligencePattern recognition (psychology)Identification (biology)SIGNAL (programming language)Noise reductionBlock (permutation group theory)AlgorithmMathematicsGeometryBiologyProgramming languagePsychiatryBotanyPsychologyEEG and Brain-Computer InterfacesECG Monitoring and AnalysisGaze Tracking and Assistive Technology