Investigation of the most appropriate mother wavelet for characterizing imaginary EEG signals used in BCI systems

Feature extraction is a very challenging task, since choosing discriminative features directly affects the recognition rate of the brain computer interface (BCI) system. The objective of this paper is to investigate the effect of mother wavelets (MWs) on classification results. To this end, features were extracted from 3 different datasets using 12 MWs, and then the signals were classified using 3 classification algorithms, including k-nearest neighbor, support vector machine, and linear discriminant analysis. The experiments proved that Daubechies and Shannon were the most suitable wavelet families for extracting more discriminative features from imaginary EEG/ECoG signals.

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[1] Aydemir O, Kayikcioglu T. Wavelet transform based classification of invasive brain computer interface data. Radioengineering 2011; 20: 3138.
[2] Nicolas-Alonso LF, Gomez-Gil J. Brain computer interfaces, a review. Sensors 2012; 12: 12111279.
[3] Zalay OC, Kang EE, Cotic M, Carlen PL, Bardakjian BL. A wavelet packet-based algorithm for the extraction of neural rhythms. Ann Biomed Eng 2009; 37: 595613.
[4] Herman P, Prasad G, McGinnity TM, Coyle D. Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE T Neur Sys Reh 2008; 16: 317326.
[5] Burke DP, Kelly SP, de Chazal P, Reilly RB, Finucane C. A parametric feature extraction and classification strategy for braincomputer interfacing. IEEE T Neur Sys Reh 2005; 13: 1217.
[6] Kamrunnahar M, Dias NS, Schiff SJ. Toward a model-based predictive controller design in brain-computer interfaces. Ann Biomed Eng 2011; 39: 14821492.
[7] Birbaumer N, Kubler A, Ghanayim N, Hinterberger T, Perelmouter J, Kaiser J, Iversen I, Kotchoubey B, Neumann N, Flor H. The thought translation device (TTD) for completely paralyzed patients. IEEE T Rehabil Eng 2000; 8: 190193.
[8] Pfurtscheller G, Brunner C, Schl¨ogl A, Da Silva FHL. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 2006; 31: 153159.
[9] Samar VJ, Bopardikar A, Rao R, Swartz K. Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain Lang 1999; 66: 760.
[10] Hsu WY, Sun YN. EEG-based motor imagery analysis using weighted wavelet transform features. J Neurosci Meth 2009; 176: 310318.
[11] Daubechies I. Orthonormal bases of compactly supported wavelets. Comm Pure Appl Math 1988; 41: 909996.
[12] Darvishi S, Al-Ani A. Braincomputer interface analysis using continuous wavelet transform and adaptive neurofuzzy classifier. In: Proceedings of the 29th International Annual Conference of the IEEE EMBS; 2226 August 2007; Lyon, France. New York, NY, USA: IEEE. pp. 32203223.
[13] Bostanov V. BCI competition 2003data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE T Biomed Eng 2004; 51: 10571061.
[14] Blankertz B, M¨uller KR, Curio G, Vaughan TM, Schalk G, Wolpaw GR, Schl¨ogl A, Neuper C, Pfurtscheller G, Hinterberger T et al. The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE T Biomed Eng 2004; 51: 10441051.
[15] Lin SC, Chang YCI, Yang WN. Meta-learning for imbalanced data and classification ensemble in binary classification. Neurocomputing 2009; 73: 484494.
[16] Neuper C, Schl¨ogl A, Pfurtscheller G. Enhancement of left-right sensorimotor EEG differences during feedbackregulated motor imagery. J Clin Neurophysiol 1999; 16: 373382.
[17] Lal TN, Hinterberger T, Widman G, Schr¨oder M, Hill J, Rosenstiel W, Elger CE, Sch¨olkpf B, Birbaumer N. Methods towards invasive human brain computer interfaces. Adv Neur In 2005; 17: 737744.
[18] Duda RO, Hart PE, Stork DG. Pattern Classification. 2nd ed. New York, NY, USA: Wiley, 2001.
[19] Avendano-Valencia LD, Godino-Lorente JI, Blanco-Velasco M, Castellanos-Dominguez G. Feature extraction from parametric time-frequency representations for heart murmur detection. Ann Biomed Eng 2010; 38: 27162732.
[20] Vapnik VN. Statistical Learning Theory. New York, NY, USA: Wiley, 1998.
[21] Tu W, Sun S. Semi-supervised feature extraction for EEG classification. Pattern Anal Appl 2013; 16: 213222.
[22] Zadeh AE, Khazaee A. High efficient system for automatic classification of the electrocardiogram beats. Ann Biomed Eng 2011; 39: 9961011.
[23] M¨uller KR, Anderson CW, Birch GE. Linear and nonlinear methods for brain-computer interfaces. IEEE T Neur Syst Reh 2003; 11: 165169.