Function Classification of EEG Signals Based on ANN

The motor imagery is limited in pattern variety, so in our work, six motor imageries including wrist, elbow, wrist rotation clockwise/anticlockwise and ankle backward/forward moment were used in this system. This paper described the auditory paradigm for recording of motor imagery signals and the relevant coefficient was used for signal analysis and recognition. EEG signals were decomposed into wavelet coefficients by discrete wavelet transform on which SVD technique is applied to get singular value used as feature vectors, presenting them into ANN classifier.

[1]  Bin He,et al.  Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns , 2004, Clinical Neurophysiology.

[2]  Lei Ding,et al.  Motor imagery classification by means of source analysis for brain–computer interface applications , 2004, Journal of neural engineering.

[3]  Klaus-Robert Müller,et al.  Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.

[4]  Klaus-Robert Müller,et al.  Spatio-spectral filters for improving the classification of single trial EEG , 2005, IEEE Transactions on Biomedical Engineering.

[5]  Bin He,et al.  A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications , 2005, Journal of neural engineering.

[6]  Steven Lemm,et al.  BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.

[7]  William Z Rymer,et al.  Brain-computer interface technology: a review of the Second International Meeting. , 2003, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  Aiguo Song,et al.  Algorithm of Imagined Left-Right Hand Movement Classification Based on Wavelet Transform and AR Parameter Model , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[9]  Kip A Ludwig,et al.  Naïve coadaptive cortical control , 2005, Journal of neural engineering.

[10]  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.

[11]  Klaus-Robert Müller,et al.  Towards Zero Training for Brain-Computer Interfacing , 2008, PloS one.

[12]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[13]  Jiang Wang,et al.  Feature extraction of brain-computer interface based on improved multivariate adaptive autoregressive models , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

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

[15]  Stefan Geyer,et al.  Imagery of voluntary movement of fingers, toes, and tongue activates corresponding body-part-specific motor representations. , 2003, Journal of neurophysiology.

[16]  Brian Lithgow,et al.  Wavelet Common Spatial Pattern in asynchronous offline brain computer interfaces , 2011, Biomed. Signal Process. Control..

[17]  A. Kübler,et al.  A Brain–Computer Interface Controlled Auditory Event‐Related Potential (P300) Spelling System for Locked‐In Patients , 2009, Annals of the New York Academy of Sciences.

[18]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.