Open AccessKlasifikasi Sinyal EEG Menggunakan Model K-Nearest Neighbor Untuk Pengenalan Kata Yang Dibayangkan
Author(s) -
Abdur Rauf,
Efy Yosrita,
Rosida Nur Aziza
Publication year - 2021
Publication title -
petir/petir (jakarta. online)
Language(s) - English
Resource type - Journals
eISSN - 2655-5018
pISSN - 1978-9262
DOI - 10.33322/petir.v15i1.1335
Subject(s) - pattern recognition (psychology) , k nearest neighbors algorithm , electroencephalography , computer science , artificial intelligence , recall , speech recognition , psychology , neuroscience , cognitive psychology
Locked in syndrome (LIS) is a condition of complete paralysis in which people with LIS are conscious but unable to move or communicate verbally except to move their eyes or blink. One way that can help LIS sufferers to communicate and interact is through recording brain signals called Electroencephalogram (EEG). In this study, the data from the recording of the EEG signal has gone through the extraction stage. The extracted data is preprocessed and classified using the K-Nearest Neighbor (K-NN) algorithm to be visualized using a web-based application. The results of the classification using the K-Nearest Neighbor algorithm with a value of K = 1 resulted in 82% accuracy, 82% precision and 82% recall.
Keywords: LIS, EEG, K-Nearest Neighbor.