Title :
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
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;
Conference_Titel :
Neural Network Applications in Electrical Engineering (NEUREL), 2014 12th Symposium on
Conference_Location :
Belgrade
Print_ISBN :
978-1-4799-5887-0
DOI :
10.1109/NEUREL.2014.7011475
