OS12-2 A Method of Feature Extraction for EEG Signals Recognition Using ROC Curve

Title:	OS12-2 A Method of Feature Extraction for EEG Signals Recognition Using ROC Curve
Publication:	ICAROB2017
Volume:	22
Pages:	654-657
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2017.OS12-2
Author(s):	Takashi Kuremoto, Yuki Baba, Masanao Obayashi, Shingo Mabu, Kunikazu KobayashiI
Publication Date:	January 19, 2017
Keywords:	EEG, FFT, ROC, AUC, SVM
Abstract:	The feature extraction of Electroencephalograph (EEG) signals plays an important role in mental task recognition of brain-computer interaction (BCI). In this study, a novel method of EEG signal feature extraction is proposed using techniques of fast Fourier transform (FFT) and receiver operating characteristic (ROC) curve. In the proposed method, the raw EEG data was transformed into power spectrum of FFT at first, and then to find frequencies decided by area under curve (AUC) of ROC between the value of spectrums of different classes of metal tasks. Experiment results using benchmark data of EEG signals showed the effectiveness of the proposed feature extraction method when support vector machine (SVM) was used as a classifier.
PDF File:	https://alife-robotics.co.jp/members2017/icarob/data/html/data/OS_pdf/OS12/OS12-2.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/