An efficient scheme for mental task classification utilizing reflection coefficients obtained from autocorrelation function of EEG signal

Author:

Rahman M. M.,Chowdhury M. A.,Fattah S. A.

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Neurology

Reference18 articles.

1. Siuly S, Li Y (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 20(4):526

2. Suk HI, Lee SW (2013) A novel Bayesian framework for discriminative feature extraction in brain–computer interfaces. IEEE Trans Pattern Anal Mach Intell 35(2):286

3. Yong X, Ward RK, Birch GE (2008) Sparse spatial filter optimization for EEG channel reduction in brain–computer interface. In: Proceedings ICASSP, pp 417–420

4. Lu H, Plataniotis KN, Venetsanopoulos AN (2009) Regularized common spatial patterns with generic learning for EEG signal classification. In: Proceedings of IEEE engineering in medicine and biology society, pp 6599–6602

5. Palaniappan R (2006) Utilizing gamma band to improve mental task based brain–computer interface design. IEEE Trans Neural Syst Rehabil Eng 14(3):299

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