DocumentCode :
695685
Title :
Hidden conditional random fields for classification of imaginary motor tasks from EEG data
Author :
Delgado Saa, Jaime F. ; Cetin, Mujdat
Author_Institution :
Signal Process. & Inf. Syst. Lab., Sabanci Univ., Istanbul, Turkey
fYear :
2011
fDate :
Aug. 29 2011-Sept. 2 2011
Firstpage :
171
Lastpage :
175
Abstract :
Brain-computer interfaces (BCIs) are systems that allow the control of external devices using information extracted from brain signals. Such systems find application in rehabilitation of patients with limited or no muscular control. One mechanism used in BCIs is the imagination of motor activity, which produces variations on the power of the electroencephalography (EEG) signals recorded over the motor cortex. In this paper, we propose a new approach for classification of imaginary motor tasks based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they involve learned statistical models matched to the classification problem; they do not suffer from some of the limitations of generative models; and they include latent variables that can be used to model different brain states in the signal. Our approach involves auto-regressive modeling of the EEG signals, followed by the computation of the power spectrum. Frequency band selection is performed on the resulting time-frequency representation through feature selection methods. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV and the results show that our approach overperforms all methods proposed in the competition. In addition, we present a comparison with an HMM-based method, and observe that the proposed method produces better classification accuracy.
Keywords :
autoregressive processes; brain-computer interfaces; electroencephalography; feature selection; hidden Markov models; learning (artificial intelligence); medical signal processing; patient rehabilitation; random processes; signal classification; statistical analysis; BCI; EEG data; HCRF; HMM-based method; auto-regressive modeling; brain signals; brain-computer interfaces; discriminative graphical models; electroencephalography signal; external device control; feature selection method; frequency band selection; hidden conditional random field; imaginary motor task classification; inference algorithm; latent variables; learned statistical models; motor cortex; patient rehabilitation; power spectrum computation; time-frequency representation; training data; Brain models; Computational modeling; Data models; Electroencephalography; Hidden Markov models; Time-frequency analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference, 2011 19th European
Conference_Location :
Barcelona
ISSN :
2076-1465
Type :
conf
Filename :
7074235
Link To Document :
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