NDLI: Selective Transfer Learning for EEG-Based Drowsiness Detection

Content Provider	IEEE Xplore Digital Library
Author	Chun-Shu Wei Yuan-Pin Lin Yu-Te Wang Tzyy-Ping Jung Bigdely-Shamlo, N. Chin-Teng Lin
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Nat. Chiao Tung Univ. Hsinchu, Hsinchu, Taiwan (Chin-Teng Lin) \|\| Inst. of Eng. in Med., Univ. of California, San Diego, La Jolla, CA, USA (Chun-Shu Wei; Yuan-Pin Lin; Yu-Te Wang; Tzyy-Ping Jung) \|\| Syntrogi, Inc., San Diego, CA, USA (Bigdely-Shamlo, N.)
Abstract	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.
Starting Page	3229
Ending Page	3232
File Size	370678
Page Count	4
File Format	PDF
e-ISBN	9781479986972
DOI	10.1109/SMC.2015.560
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Brain models Predictive models Data models Robustness Estimation transfer learning EEG brain-computer interface
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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