Classifications of motor imagery tasks using k-nearest neighbors

Author

Aldea, Roxana ; Fira, Monica ; Lazar, Anca

Author_Institution

Telecommun. & Inf. Technol., “Gheorghe Asachi” Tech. Univ. of Iasi, Iasi, Romania

fYear

2014

fDate

25-27 Nov. 2014

Firstpage

115

Lastpage

120

Abstract

We address a classification method for motor imagery tasks-based brain computer interface (BCI). The wavelet coefficients are used to extract the features from the motor imagery electroencephalographic (EEG) signals and the k-nearest neighbor classifier is applied to classify the pattern of left or right hand imagery movement and rest. The performance of the proposed method is evaluated using EEG data recorded with 8 g.tec active electrodes by means of g.MOBIlab+ module. The maximum classification accuracy is 91%.

Keywords

brain-computer interfaces; electroencephalography; feature extraction; image classification; BCI; EEG data; EEG signals; active electrodes; classification method; feature extraction; k-nearest neighbor classifier; k-nearest neighbors; left hand imagery movement; maximum classification accuracy; motor imagery electroencephalographic; motor imagery tasks-based brain computer interface; right hand imagery movement; wavelet coefficients; Accuracy; Electroencephalography; Feature extraction; Image resolution; Signal resolution; Software; Wavelet analysis; Brain computer interface; k-nearest neighbor; motor imagery; wavelet coefficients;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Network Applications in Electrical Engineering (NEUREL), 2014 12th Symposium on

Conference_Location

Belgrade

Print_ISBN

978-1-4799-5887-0

Type

conf

DOI

10.1109/NEUREL.2014.7011475

Filename

7011475