標題: Selective Transfer Learning for EEG-Based Drowsiness Detection
作者: Wei, Chun-Shu
Lin, Yuan-Pin
Wang, Yu-Te
Jung, Tzyy-Ping
Bigdely-Shamlo, Nima
Lin, Chin-Teng
電子工程學系及電子研究所
腦科學研究中心
Department of Electronics Engineering and Institute of Electronics
Brain Research Center
關鍵字: EEG;brain-computer interface;transfer learning
公開日期: 1-Jan-2015
摘要: On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject\'s pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual\'s pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects\' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.
URI: http://dx.doi.org/10.1109/SMC.2015.560
http://hdl.handle.net/11536/129827
ISBN: 978-1-4799-8696-5
ISSN: 1062-922X
DOI: 10.1109/SMC.2015.560
期刊: 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS
起始頁: 3229
結束頁: 3232
Appears in Collections:Conferences Paper