Two-Layer Hidden Markov Models for Multi-class Motor Imagery Classification

Classifiers in a high dimensional space based on the signals of multiple electrodes in EEG-based BCIs suffer from the curse of dimensionality due to the limited training dataset. In order to tackle this problem, we design a framework of two-layer hidden Markov models (HMMs) for probabilistic classif...

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Bibliographic Details
Published in2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging pp. 5 - 8
Main Authors Heung-Il Suk, Seong-Whan Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
Brain modeling
brain-computer interface (BCI)
Electroencephalography
electroencephalography (EEG)
Feature extraction
Hidden Markov models
Hidden Markov Models (HMMs)
motor Imagery classification
Principal component analysis
Time domain analysis
Training
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Summary:Classifiers in a high dimensional space based on the signals of multiple electrodes in EEG-based BCIs suffer from the curse of dimensionality due to the limited training dataset. In order to tackle this problem, we design a framework of two-layer hidden Markov models (HMMs) for probabilistic classification of EEG signals. We first independently model the characteristics of EEG signals embedded in each channel for different motor imagery tasks in the lower-layer, and then represent the holistic task-related dynamic EEG patterns in the upper-layer by considering the relationships among channels. From the experimental results based on the dataset II-a of BCI Competition IV (2008), we demonstrated that our method achieved high session-to-session transfer results and was superior to previous methods.
ISBN:9781424484867
1424484863
DOI:10.1109/WBD.2010.16