Enhancing EEG Signals Recognition Using ROC Curve
- 10.2991/jrnal.2018.4.4.5How to use a DOI?
- EEG, FFT, ROC, AUC, SVM
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.
- © 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/).
Cite this article
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 -