NDLI: Transfer learning and active transfer learning for reducing calibration data in single-trial classification of visually-evoked potentials

Content Provider	IEEE Xplore Digital Library
Author	Dongrui Wu Lance, B. Lawhern, V.
Copyright Year	2014
Description	Author affiliation: Translational Neurosci. Branch, U.S. Army Res. Lab., Adelphi, MD, USA (Lance, B.; Lawhern, V.) \|\| Machine Learning Lab., GE Global Res., Niskayuna, NY, USA (Dongrui Wu)
Abstract	Single-trial Event-Related Potential (ERP) classification is a key requirement for several types of Brain-Computer Interaction (BCI) technologies. However, strong individual differences make it challenging to develop a generic single-trial ERP classifier that performs well for all subjects. Usually some subject-specific training samples need to be collected in an initial calibration session to customize the classifier. However, if implemented into an actual BCI system, then this calibration process would decrease the utility of the system, potentially decreasing its usability. In this paper we propose a Transfer Learning approach for reducing the amount of subject-specific data in online single-trial ERP classifier calibration, and an Active Transfer Learning approach for offline calibration. By applying these approaches to data from a Visually-Evoked Potential EEG experiment, we demonstrate that they improve the classification performance, given the same number of labeled subject-specific training samples. In other words, these approaches can also attain a desired level of classification accuracy with less labeling effort when compared to a randomly selected training set.
Starting Page	2801
Ending Page	2807
File Size	332031
Page Count	7
File Format	PDF
ISBN	9781479938407
DOI	10.1109/SMC.2014.6974353
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Electroencephalography Calibration Accuracy Support vector machines Labeling Laboratories active transfer learning Single-trial classification ERP VEP EEG transfer learning active learning
Content Type	Text
Resource Type	Article

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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
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