Hidden Conditional Random Fields for Classification of Imaginary Motor Tasks From EEG Data
Jaime Fernando Delgado Saa, Müjdat Çetin
EURASIP European Signal Processing Conference, Barcelona, Spain, August 2011
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.