Feature Extraction and Classification of Mental EEG for Motor Imagery

Electroencephalography (EEG) recognition was one of the key technology in brain-computer interface (BCI). For motor imagery EEG, a new EEG recognition algorithm (DWT-BP algorithm) which combined discrete wavelet transform (DWT) with BP neural network was presented. In DWT-BP, a rational time window was set through calculating the average power of motor imagery EEG on electrode C3 and C4, and then the average power during the time window was taken into DWT. The combinational signal of approximate coefficient A6 on the sixth level was selected as a signal feature and BP neural network was used as classifier to analyze the observed EEG data. The experiment results on “BCI Competition 2003” competition database showed that the recognition rate was better than the other several traditional algorithms. So, it proved that the algorithm was effective for EEG recognition of motor imagery, and provided a new idea for motor imagery recognition in brain computer interface.

[1]  Gilles Blanchard,et al.  BCI competition 2003-data set IIa: spatial patterns of self-controlled brain rhythm modulations , 2004, IEEE Transactions on Biomedical Engineering.

[2]  G. Pfurtscheller,et al.  15 years of BCI research at graz university of technology: current projects , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[4]  Rehab Bahauldeen Ashary.  Brain Computer Interface for Communication and Control , 2008 .

[5]  J.R. Wolpaw,et al.  BCI meeting 2005-workshop on signals and recording methods , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  William Z Rymer,et al.  Guest Editorial Brain–Computer Interface Technology: A Review of the Second International Meeting , 2001 .

[7]  G. Pfurtscheller,et al.  Using adaptive autoregressive parameters for a brain-computer-interface experiment , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[8]  Wolfgang Grodd,et al.  Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) , 2004, IEEE Transactions on Biomedical Engineering.

[9]  Jonathan R Wolpaw,et al.  Guest Editorial The Third International Meeting on Brain-Computer Interface Technology: Making a Difference , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  K.-R. Muller,et al.  BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  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.

[12]  Justin Werfel,et al.  BCI competition 2003-data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals , 2004, IEEE Transactions on Biomedical Engineering.