Selection of an efficient feature space for EEG-based mental task discrimination

• Autorzy: Noshadi S. , Abootalebi V. , Sadeghi M. T. , Shahvazian M. S.

Identyfikatory

DOI

10.1016/j.bbe.2014.03.004

• Języki publikacji: EN

Abstrakty

EN

The aim of this paper is to contribute toward exploring an optimal feature space for discriminating mental tasks. Empirical mode decomposition (EMD) algorithm seems useful for designing such a feature space. The adjustment of nonlinear and non-stationary properties of the EEG signals with this algorithm and the successful application of this approach together biomedical signal processing problems encourage us to examine a variety of statistical and spectral measures within the EMD space as the adapted features. In this sense, as a measure of complexity, the Lempel–Ziv algorithm is utilized within the frame-work of the EMD algorithm. A modified form of the Lempel–Ziv complexity algorithm is then proposed. The features derived from the modified algorithm outperform the other features individually. By combining the modified Lempel–Ziv features with the other adopted features, in average, 97.78% classification accuracy is achieved for different subjects. It is concluded that the EMD–LZ kernel allows for achieving of better performances in classifying mental tasks than the results obtained with other methods.

Słowa kluczowe

EN

mental task; electroencephalogram signals (EEG); empirical mode decomposition; EMD; Lempel–Ziv algorithm

PL

elektroencefalografia (EEG); EMD; algorytm LZW

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2014
• Tom: Vol. 34, no. 3
• Strony: 159--168
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Noshadi S.

• Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran

autor

Abootalebi V.

• Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran

autor

Sadeghi M. T.

• Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran

autor

Shahvazian M. S.

• Department of Electrical and Computer Engineering, Yazd University, Yazd, Iran

Bibliografia

• [1] Diez PF, Mut VA, Laciar E, Torres A, Avila E. New features extraction method for mental tasks classification based on the empirical mode decomposition. Proc. 31st Annual International Conference of the IEEE/EMBS; 2009.
• [2] Bamdadian A, Guan C, Ang KK, Xu JX. Real coded GA-based SVM for motor imagery classification in a brain–computer interface. Proc. 9th IEEE International Conference on Control and Automation (ICCA); 2011.
• [3] Kim DW, Cho JH, Hwang HJ, Lim JH, Im ChH. A vision-free brain–computer interface (BCI) paradigm based on auditory selective attention. Proc. 33rd Annual International Conference of the IEEE EMBS; 2011.
• [4] Sellers EW, Donchin E. A P300-based brain–computer interface: initial tests by ALS patients. Clin Neurophysiol 2006;117:538–48.
• [5] Lin ZL, Zhang CS, Wu W, Gao XR. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 2006;53:2610–4.
• [6] Decety J, Ingvar DH. Brain structures participating in mental simulation of motor behavior: a neuropsychological interpretation. Acta Psychol (Amst) 1990;73:13–34.
• [7] Penny WD, Roberts SJ, Curran EA, Stokes MJ. EEG-based communication: a pattern recognition approach. IEEE Trans Rehab Eng 2000;8:214–5.
• [8] McFarland DJ, Anderson CW, Müller K-R, Schlögl A, Krusienski DJ. BCI Meeting 2005—Workshop on BCI Signal Processing: Feature Extraction and Translation. IEEE Trans Neural Syst Rehab Eng 2006;14(2):135–8.
• [9] Cheng DS, Amato VD, Murino V. Wavelet-based processing of EEG data for brain–computer interfaces. Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'2005). 2005. pp. 1063–6919.
• [10] Jing-tian T, Qing Z, Yan T, Bin L, Xiao-kai Z. Hilbert–Huang transform for ECG denoising. Proc. 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE 2007). 2007. pp. 664–7.
• [11] Liang H, Bressler SL, Desimone R, Fries P. Empirical mode decomposition: a method for analyzing neural data. Neurocomputing 2005;65–66:801–7.
• [12] McKeown MJ, Saab R, Abu-Gharbieh R. A combined independent component analysis (ICA)/empirical mode decomposition (EMD) method to infer corticomuscular coupling. Proc. 2nd IEEE EMBS Conference on Neural Engineering; 2005. pp. 679–82.
• [13] Torres A, Jané R, Fiz JA, Laciar E, Galdiz JB, Gea J, et al. Analysis of respiratory mechanomyographic signals by means of the empirical mode decomposition. J Phys Conf Series 2007;90:1–8.
• [14] Ziqiang Z, Puthusserypady S. Analysis of schizophrenic EEG synchrony using empirical mode decomposition. Proc. 15th International Conference on Digital Signal Processing; 2007. pp. 131–4.
• [15] Hu M, Li G, Ding Q, Li J. Classification of normal and hypoxia EEG based on Hilbert–Huang transform. International Conference on Neural Network and Brain (ICNN&B 2005), vol. 2. 2005. pp. 851–4.
• [16] Rutkowsk TM, Zdunek R, Cichock A. Multichannel EEG brain activity pattern analysis in time–frequency domain with nonnegative matrix factorization support. Int Cong Series 2007;1301:266–9.
• [17] Solis-Escalante T, Gentiletti GG, Yañez-Suarez O. Single trial P300 detection based on the empirical mode decomposition. Proc. 28th IEEE EMBS Annual International Conference; 2006. pp. 1157–60.
• [18] Kaleem M, Sugavaneswaran F, Guergachi L, Krishnan AS. Application of empirical mode decomposition and teager energy operator to EEG signals for mental task classification. 32nd Annual International Conference of the IEEE EMBS; 2010.
• [19] Palaniappan R. Utilizing gamma band to improve mental task based brain–computer interface design. IEEE Trans Neural Syst Rehab Eng 2006;14(3):299–303.
• [20] Garrett D, Peterson DA, Anderson CW, Thaut MH. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans Neural Syst Rehab Eng 2003;11:141–4.
• [21] Keirn ZA, Aunon JI. A new mode of communication between man and his surroundings. IEEE Trans Biomed Eng 1990;37:1209–14.
• [22] Huang NE, Shen SSP. Hilbert-Huang transform and its applications. Interdisciplinary mathematical sciences, vol. 5. 2005.
• [23] Chen M, Mandic DP, Kidmose P, Ungstrup M. Qualtitiveassesment of intrinsic mode functions of empirical mode decomposition. Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2008). 2008. pp. 1905–8.
• [24] Rilling G, Flandrin P, Gonçalvès P. On empirical mode decomposition and its algorithms. IEEE-Eurasip Workshop on Nonlinear Signal and Image Processing NSIP-2003 Grado (I); 2003.
• [25] Huang NE, Shen Z, Long SR, Wu ML, Shih HH, Zheng Q, et al. The empirical mode decomposition and Hilbert spectrumfor nonlinear and nonstationary time series analysis. Proc Royal Soc Lond A 1998;454:903–95.
• [26] Xie H, Wang Zh. Mean frequency derived via Hilbert– Huang transform with application to fatigue EMG signal analysis. J Comput Methods Programs Biomed 2006;82 (2):114–20.
• [27] Noshadi S. Selection of an efficient feature space for EEG-based mental task discrimination.[MSc thesis] Yazd, Iran: University of Yazd; 2010.
• [28] Shannon CE. A mathematical theory of communication. AT&T Tec J 1948;27:379–423. 623–56.
• [29] Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000;278:2039–49.
• [30] Amoud H, Snoussi H, Hewson D, Doussot M, Duchêne J. Intrinsic mode entropy for nonlinear discriminant analysis. IEEE Signal Proc Lett 2007;14(5):297–300.
• [31] Radhakrishnan N, Janusz S, Eligiusz W. Interpreting non-random signatures in biomedical signals with Lempel–Ziv complexity. Proc. 28th IEEE EMBS Annual International Conference; 2006. pp. 1190–3.
• [32] Ziv J, Lempel A. On the complexity of finite sequences. IEEE Trans Inform Theory 1976;22:75–81.
• [33] Radhakrishnan N, Gangadhar BN. Estimating egularity in epileptic seizure time-series. Proc IEEE Eng Med Biol Mag 1998;17(3):89–94.
• [34] Zhou N, Wang L. A modified t-test feature selection method and its application on the hap map genotype data. J Genomics Proteomics Bioinform 2007;5(3):242–9.
• [35] Sepulveda F, Dyson M, Gan JQ. A comparison of mental task combinations for asynchronous EEG-based BCIs. Proceedings of the 29th Annual International Conference of the IEEE EMBS; 2007. pp. 5055–8.