Enhancing EEG Signals Recognition Using ROC Curve

DOI

10.2991/jrnal.2018.4.4.5How to use a DOI?

Keywords

EEG, FFT, ROC, AUC, SVM

Abstract

Mental tasks, such as calculation, reasoning, motor imagery, etc., can be recognized by the pattern of electroencephalograph (EEG) signals. So EEG signal recognition plays an important role in brain-computer interaction (BCI). In this study, to enhance the ability of classifiers such as support vector machine (SVM), deep neural networks (DNN), k-nearest neighbor method (kNN), decision tree (DT), a feature extraction method 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 and BCI competition II data showed the effectiveness of the proposed method for all above classifiers.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - JOUR
AU  - Takashi Kuremoto
AU  - Yuki Baba
AU  - Masanao Obayashi
AU  - Shingo Mabu
AU  - Kunikazu Kobayashi
PY  - 2018
DA  - 2018/03/31
TI  - Enhancing EEG Signals Recognition Using ROC Curve
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 283
EP  - 286
VL  - 4
IS  - 4
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2018.4.4.5
DO  - 10.2991/jrnal.2018.4.4.5
ID  - Kuremoto2018
ER  -