DocumentCode :
3665034
Title :
To extraction the feature of EEG signals for mental task recognition
Author :
Takashi Kuremoto;Yuki Baba;Masanao Obayashi;Shingo Mabu;Kunikazu Kobayashi
Author_Institution :
Department of Information Science and Engineering, Yamaguchi University, Ube, Japan
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
264
Lastpage :
269
Abstract :
Electroencephalogram (EEG) signals are widely used in brain-computer interface (BCI) recently. By classifying the signals, mental tasks in the brain are available to be estimated and then the results can be used in the communication between human and external devices or robots. Many classifiers, such as support vector machine (SVM), multi-layer perceptron (MLP), and self-organizing map (SOM), etc., have been proposed, however, the crucial role in these techniques is how to extract the features of the EEG signals. In this paper, an efficiency feature extraction method is proposed. It includes a series processes such as windowing, Fourier transformation, frequency band filtering, and nonlinear normalization. Additionally, several supervised classifiers such as MLP, SVMs, and “class proximity self-organizing map (CP-SOM)” proposed by Saito & Harton are adopted to verify the effectiveness of proposed method. Using a benchmark data and the original data measured by the EEG sensor EPOC, mental task recognition experiments were executed and the priority of the proposed method for all classifiers was confirmed.
Keywords :
"Electroencephalography","Feature extraction","Support vector machines","Kernel","Benchmark testing","Discrete Fourier transforms","Brain-computer interfaces"
Publisher :
ieee
Conference_Titel :
Society of Instrument and Control Engineers of Japan (SICE), 2015 54th Annual Conference of the
Type :
conf
DOI :
10.1109/SICE.2015.7285468
Filename :
7285468
Link To Document :
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