Design of extraction method of SSVEP brain activity with IIR Chebyshev

In this paper, an extraction and classification of steady state-visual evoked potentials using the IIR Chebyshev I of 4 order and the adaptive feed-forward Neural Networks algorithm, respectively are applied. The classification results of the extracted signals is directly used to make a user able of controlling the directions (stop, forward, right, and left with stimuli frequencies of 7.5, 10, 15, and 20 Hz, respectively) of a wheelchair based brain computer interface. The data was collected during a session in which fourteen subjects with age about 24±2 years were tested. The average classification accuracy level around 82% of four directions is achieved. It is improve about 13.75% compare with the obtained previous result.

[1]  Arun Khosla,et al.  Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines , 2014, Journal of medical engineering & technology.

[2]  Arjon Turnip,et al.  THE PERFORMANCE OF EEG-P300 CLASSIFICATION USING BACKPROPAGATION NEURAL NETWORKS , 2013 .

[3]  Arjon Turnip,et al.  Adaptive Principal Component Analysis Based Recursive Least Squares for Artifact Removal of EEG Signals , 2014 .

[4]  Keum-Shik Hong,et al.  Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis , 2011, Biomedical engineering online.

[5]  Dwi Esti Kusumandari,et al.  A Comparison of Extraction Techniques for the Rapid Electroencephalogram-P300 Signals , 2014 .

[6]  S. Hillyard,et al.  Selective attention to stimulus location modulates the steady-state visual evoked potential. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[7]  P. Sajda,et al.  A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  A. Kübler,et al.  Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials , 2014, Journal of NeuroEngineering and Rehabilitation.

[9]  Dwi Esti Kusumandari,et al.  A Comparison of Extraction Techniques for the rapid EEG-P300 Signals , 2018 .

[10]  Arjon Turnip,et al.  Electrooculography Detection from Recorded Electroencephalogram Signals by Extended Independent Component Analysis , 2015 .

[11]  Francesco Piccione,et al.  User adaptive BCIs: SSVEP and P300 based interfaces , 2003, PsychNology J..

[12]  A Belitski,et al.  P300 audio-visual speller , 2011, Journal of neural engineering.

[13]  O. Bai,et al.  Electroencephalography (EEG)-Based Brain–Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Dwi Esti Kusumandari,et al.  Improvement of BCI Performance Through Nonlinear Independent Component Analysis Extraction , 2014, J. Comput..

[15]  J P Rosenfeld,et al.  A modified, event-related potential-based guilty knowledge test. , 1988, The International journal of neuroscience.

[16]  Matthias M. Müller,et al.  Effects of spatial selective attention on the steady-state visual evoked potential in the 20-28 Hz range. , 1998, Brain research. Cognitive brain research.

[17]  Dwi Esti Kusumandari,et al.  A Comparison of EEG Processing Methods to Improve the Performance of BCI , 2013, SiPS 2013.

[18]  P. Rutecki,et al.  Neuronal excitability: voltage-dependent currents and synaptic transmission. , 1992, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[19]  Abbas Erfanian,et al.  An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network. , 2010, Medical engineering & physics.

[20]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[21]  K. Hong,et al.  CLASSIFYING MENTAL ACTIVITIES FROM EEG-P 300 SIGNALS USING ADAPTIVE NEURAL NETWORKS , 2012 .