Fast STF model and applications on EEG analysis

Searching for the tool that can efficiently summarize a multi-channel EEG signal is a challenging problem in EEG processing. In this paper, we propose the fast implementation of the 3-way parallel factor analysis (PARAFAC) called Fast STF model (fSTF model) which can simultaneously employ all the space, time, and frequency domains of a multi-channel EEG. The multi-channel EEG signal is first subdivided along space and time domains into the selected numbers of segments. By carefully selecting the number of segments according to the structure of the brain, signatures (features) extracted from the fSTF model are comparable with those from the conventional STF model while the time used in computation is reduced by more than 50%. Signatures obtained from the fSTF model are further summarized as a single number to indicate the quality of the multi-channel EEG signal. The simulation results illustrate the merits of the proposed model via the applications on eyeblink artifact-contaminated EEG decomposition and EEG quality assessment.

[1]  Saeid Sanei,et al.  Parallel space-time-frequency decomposition of EEG signals for brain computer interfacing , 2006, 2006 14th European Signal Processing Conference.

[2]  Y. Wongsawat,et al.  A Robust Minimum Variance Beamforming Approach for the Removal of the Eye-Blink Artifacts from EEGs , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[4]  T. Lagerlund,et al.  Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. , 1997, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[5]  Lars Kai Hansen,et al.  Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG , 2006, NeuroImage.

[6]  O. Bertrand,et al.  Time-frequency digital filtering based on an invertible wavelet transform: an application to evoked potentials , 1994, IEEE Transactions on Biomedical Engineering.

[7]  Andrzej Cichocki,et al.  EEG Windowed Statisticalwavelet Deviation for Estimation of Muscular Artifacts , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[8]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[9]  Z.J. Koles,et al.  Principal-component localization of the sources of the background EEG , 1995, IEEE Transactions on Biomedical Engineering.

[10]  R. Grave de Peralta Menendez,et al.  Non‐stationary distributed source approximation: An alternative to improve localization procedures , 2001, Human brain mapping.

[11]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[12]  Rasmus Bro,et al.  MULTI-WAY ANALYSIS IN THE FOOD INDUSTRY Models, Algorithms & Applications , 1998 .

[13]  Soontorn Oraintara,et al.  Reduced Complexity Space-Time-Frequency Model for Multi-Channel EEG and Its Applications , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[14]  Fumikazu Miwakeichi,et al.  Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis , 2004, NeuroImage.

[15]  S. Makeig Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. , 1993, Electroencephalography and clinical neurophysiology.