Feature extraction and classification in a two-state brain-computer interface
2016 Medical Technologies National Conference, TIPTEKNO 2016
Institute of Electrical and Electronics Engineers Inc.
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Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method. © 2016 IEEE.
Relative band power
Brain computer interface
Classification (of information)
Nearest neighbor search
Support vector machines
Feature extraction and classification
K nearest neighbor (KNN)
Linear discriminant analysis
Biomedical signal processing
Published Version (Please cite this version)http://dx.doi.org/10.1109/TIPTEKNO.2016.7863118
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