Classification of chaotic signals using HMM classifiers:EEG-based mental task classification

Mental task classification using brain signals, mostly electroencephalogram (EEG), is an approach to understand human brain functions. As EEG seems to be chaotic, it is important to verify the capability of probabilistic and statistical processing tools (such as HMM-based classifiers) in working with chaotic signals. At first, we study the performance of HMM's in classification of different classes of synthetically generated chaotic signals. Then performance of such classifiers in EEG-based mental task classification is studied. Results show good performance in both cases.

[1]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  R Ferri,et al.  Chaotic behavior of EEG slow-wave activity during sleep. , 1996, Electroencephalography and clinical neurophysiology.

[3]  Kouhyar Tavakolian,et al.  Selecting better EEG channels for classification of mental tasks , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[4]  Christa Neuper,et al.  Hidden Markov models for online classification of single trial EEG data , 2001, Pattern Recognit. Lett..

[5]  G. Pfurtscheller,et al.  How many people are able to operate an EEG-based brain-computer interface (BCI)? , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  S. Nishida,et al.  A new brain-computer interface design using fuzzy ARTMAP , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Jukka Heikkonen,et al.  A local neural classifier for the recognition of EEG patterns associated to mental tasks , 2002, IEEE Trans. Neural Networks.

[8]  F. Cincotti,et al.  Neural networks for robust classification of mental tasks , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[9]  A. Murata,et al.  Analysis of chaotic dynamics in EEG and its application to assessment of mental workload , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[10]  Andrew C. Singer,et al.  Modeling chaotic systems with hidden Markov models , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Joydeep Ghosh,et al.  HMMs and Coupled HMMs for multi-channel EEG classification , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[12]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[13]  Touradj Ebrahimi,et al.  Brain-computer interface in multimedia communication , 2003, IEEE Signal Process. Mag..

[14]  S. Sarbadhikari,et al.  Chaos in the brain: a short review alluding to epilepsy, depression, exercise and lateralization. , 2001, Medical engineering & physics.

[15]  Agostinho C. Rosa,et al.  Asymmetric hemisphere modeling in an offline brain-computer interface , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[16]  Philippe Faure,et al.  Is there chaos in the brain? II. Experimental evidence and related models. , 2003, Comptes rendus biologies.