Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

Description:

Article discussing collaborative filtering for brain-computer interaction using transfer learning and active class selection.

Creator(s):
Creation Date: February 21, 2013
Partner(s):
UNT College of Arts and Sciences
Collection(s):
UNT Scholarly Works
Usage:
Total Uses: 1
Past 30 days: 1
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Creator (Author):
Wu, Dongrui

GE Global Research

Creator (Author):
Lance, Brent J.

Army Research Laboratory

Creator (Author):
Parsons, Thomas D.

University of North Texas

Publisher Info:
Publisher Name: Public Library of Science
Place of Publication: [San Francisco, California]
Date(s):
  • Creation: February 21, 2013
Description:

Article discussing collaborative filtering for brain-computer interaction using transfer learning and active class selection.

Degree:
Department: Psychology
Note:

Abstract: Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

Physical Description:

18 p. : col. ill.

Language(s):
Subject(s):
Keyword(s): brain computer interaction | transfer learning | active class selection
Source: PLoS One, 2013, San Francisco: PLoS One
Partner:
UNT College of Arts and Sciences
Collection:
UNT Scholarly Works
Identifier:
  • DOI: 10.1371/journal.pone.0056624
  • ARK: ark:/67531/metadc306986
Resource Type: Article
Format: Text
Rights:
Access: Public
Citation:
Publication Title: PLoS One
Volume: 8
Issue: 2
Pages: 18
Peer Reviewed: Yes