Discrimination of task-related eeg signals using diffusion adaptation and S-transform coherency

This work presents a novel approach for discriminating complex mental and motor tasks using diffusion adaptation and brain connectivity measures. In particular, in this paper, we use a S-transform based measure to estimate the connectivity on single-trial basis and diffusion Kalman filtering to train a model that can classify different tasks. The superiority of the method is proven when compared with solutions that don't rely on cooperation.

[1]  Karl J. Friston,et al.  Resting oscillatory cortico-subthalamic connectivity in patients with Parkinson's disease. , 2011, Brain : a journal of neurology.

[2]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[3]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[4]  Ali H. Sayed,et al.  Modelling brain cortical connectivity using diffusion adaptation , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[6]  Ali H. Sayed,et al.  Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis , 2008, IEEE Transactions on Signal Processing.

[7]  D. Long.  Networks of the Brain , 2011 .

[8]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[9]  Saeid Sanei,et al.  Diffusion adaptive filtering for modelling brain responses to motor tasks , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).

[10]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[11]  Ali H. Sayed,et al.  Diffusion Strategies for Distributed Kalman Filtering and Smoothing , 2010, IEEE Transactions on Automatic Control.

[12]  Cheng Liu,et al.  Estimation of Time-Varying Coherence and Its Application in Understanding Brain Functional Connectivity , 2010, EURASIP J. Adv. Signal Process..

[13]  Boualem Boashash,et al.  Evaluation of the modified S-transform for time-frequency synchrony analysis and source localisation , 2012, EURASIP J. Adv. Signal Process..