TY - GEN
T1 - Selective Transfer Learning for EEG-Based Drowsiness Detection
AU - Wei, Chun-Shu
AU - Lin, Yuan Pin
AU - Wang, Yu Te
AU - Jung, Tzyy Ping
AU - Bigdely-Shamlo, Nima
AU - Lin, Chin-Teng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/12
Y1 - 2016/1/12
N2 - 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.
AB - 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.
KW - brain-computer interface
KW - EEG
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=84964424950&partnerID=8YFLogxK
U2 - 10.1109/SMC.2015.560
DO - 10.1109/SMC.2015.560
M3 - Conference contribution
AN - SCOPUS:84964424950
SN - 9781479986965
T3 - IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
SP - 3229
EP - 3232
BT - 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015): Big Data Analytics For Human-Centric Systems
PB - IEEE
T2 - IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Y2 - 9 October 2015 through 12 October 2015
ER -