Efficient event related oscillatory pattern classification for EEG based BCI utilizing spatial brain dynamics

This paper features the spatial characteristics of the brain towards brain-computer interface (BCI) research. A study on motor imagery (MI) based BCI has been carried out and important implications are identified. Common Spatial Pattern (CSP) is applied to the EEG signals before proceeding to the classification. The primary focus of this research is to utilize the spatial dynamics of the brain to develop BCI with reduced number of electrodes which contribute to the motor imagery tasks with optimal impact. It is observed that computational cost can be reduced drastically by selecting channels from specific regions of interests (ROIs) of the brain without compromising the classification accuracy making BCI efficient. Here, we have reported the best classification accuracies 72.5% and 97.1% which are achieved for two subjects (`av' and `ay', respectively, in the dataset IVa in the BCI competition III) using less number of electrodes.

[1]  Yijun Wang,et al.  Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[2]  M. Lotze,et al.  Motor imagery , 2006, Journal of Physiology-Paris.

[3]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[4]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[5]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  S. Small,et al.  Fine modulation in network activation during motor execution and motor imagery. , 2004, Cerebral cortex.

[7]  R. Ward,et al.  Robust Common Spatial Patterns for EEG signal preprocessing , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  M. Erb,et al.  Activation of Cortical and Cerebellar Motor Areas during Executed and Imagined Hand Movements: An fMRI Study , 1999, Journal of Cognitive Neuroscience.

[9]  Haiping Lu,et al.  Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting , 2010, IEEE Transactions on Biomedical Engineering.