Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces

Authors
Suk, Heung-IlLee, Seong-Whan
Issue Date
2월-2013
Publisher
IEEE COMPUTER SOC
Keywords
Discriminative feature extraction; spatiospectral filter optimization; Brain-Computer Interface (BCI); ElectroEncephaloGraphy (EEG); motor imagery classification
Citation
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.35, no.2, pp.286 - 299
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume
35
Number
2
Start Page
286
End Page
299
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/104051
DOI
10.1109/TPAMI.2012.69
ISSN
0162-8828
Abstract
As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Lee, Seong Whan photo

Lee, Seong Whan
인공지능학과
Read more

Altmetrics

Total Views & Downloads

BROWSE