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Abstract
The EEG (Electroencephalogram) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. In recent years, brain computer interface and intelligent signal segmentation have attracted a great interest ranging from medicine to military objectives. To facilitate brain-computer interface assembly, a professional method of feature extraction from EEG signal is desired. The brain electrical activity is represented by the electroencephalogram (EEG) signals. This paper presents a short review of mathematical methods for extracting features from EEG signals. The review considers different methods such as FFT,WT,CWT and DWT for EEG signal extracting . The adopted approach is such that a full literature review is introduced for the different techniques, summarizing their strengths and weaknesses. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.
Keywords— ERP, EEG, fMRI,EEG- fMRI integration , EEG classification, EEG diseases.##plugins.themes.academic_pro.article.details##
How to Cite
Sudha.M, M., A, K., & Kumar.L, D. (2014). Literature Survey about Brainwave Test. International Journal of Emerging Trends in Science and Technology, 1(07). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/353
References
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5. H. Cecotti, “Spelling with non-invasive brain-computer interfaces—current and future trends,†Journal of Physiology, vol. 105, no. 1–3, pp. 106–114, 2011.
6. S. Kotchetkov, B. Y. Hwang, G. Appelboom, C. P. Kellner, and E. S. Connolly Jr., “Brain-computer interfaces: military, neurosurgical, and ethical perspective,†Neurosurgical Focus, vol. 28, no. 5, article E25, 2010.
7. D. Übeyli, “Statistics over features: EEG signals analysis,†Computers in Biology and Medicine, vol. 39, no. 8, pp. 733–741, 2009.
8. R. Agarwal, J. Gotman, D. Flanagan, and B. Rosenblatt, “Automatic EEG analysis during long-term monitoring in the ICU,†Electroencephalography and Clinical Neurophysiology, vol. 107, no. 1, pp. 44–58, 1998.
9. Meyer-Lindenberg, “The evolution of complexity in human brain development: an EEG study,â€Electroencephalography and Clinical Neurophysiology, vol. 99, no. 5, pp. 405–411, 1996.
10. Subasi and E. Erçelebi, “Classification of EEG signals using neural network and logistic regression,â€Computer Methods and Programs in Biomedicine, vol. 78, no. 2, pp. 87–99, 2005.
A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,â€Expert Systems with Applications, vol. 32, no. 4, pp. 1084–1093, 2007.
11. H. Jasper and L. D. Proctor, Reticular Formation of the Brain, 1958.
12. M. R. N. Kousarrizi, A. A. Ghanbari, M. Teshnehlab, M. Aliyari, and A. Gharaviri, “Feature extraction and classification of EEG signals using wavelet transform, SVM and artificial neural networks for brain computer interfaces,†in Proceedings of the International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS '09), pp. 352–355, August 2009.
13. Cvetkovic, E. D. Übeyli, and I. Cosic, “Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study,†Digital Signal Processing, vol. 18, no. 5, pp. 861–874, 2008.
14. H. Kordylewski, D. Graupe, and K. Liu, “A novel large-memory neural network as an aid in medical diagnosis applications,†IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 3, pp. 202–209, 2001.
15. Güler and E. D. Übeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,†Journal of Neuroscience Methods, vol. 148, no. 2, pp. 113–121, 2005.
16. D. Übeyli, “Wavelet/mixture of experts network structure for EEG signals classification,†Expert Systems with Applications, vol. 34, no. 3, pp. 1954–1962, 2008.
17. Subasi, M. K. Kiymik, A. Alkan, and E. Koklukaya, “Neural network classification of EEG signals by using AR with MLE preprocessing for epileptic seizure detection,†Mathematical and Computational Applications, vol. 10, no. 1, pp. 57–70, 2005.
18. O. Faust, R. U. Acharya, A. R. Allen, and C. M. Lin, “Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques,†IRBM, vol. 29, no. 1, pp. 44–52, 2008.
19. Procházka, J. Kukal, and O. Vyšata, “Wavelet transform use for feature extraction and EEG signal segments classification,†in Proceedings of the 3rd International Symposium on Communications, Control, and Signal Processing (ISCCSP '08), pp. 719–722, March 2008.
20. Daubechies, “Wavelet transform, time-frequency localization and signal analysis,†IEEE Transactions on Information Theory, vol. 36, no. 5, pp. 961–1005, 1990.
21. S. Soltani, “On the use of the wavelet decomposition for time series prediction,†Neurocomputing, vol. 48, no. 1, pp. 267–277, 2002
22. N. Hazarika, J. Z. Chen, A. C. Tsoi, and A. Sergejew, “Classification of EEG signals using the wavelet transform,†Signal Processing, vol. 59, no. 1, pp. 61–72, 1997.
23. M. Unser and A. Aldroubi, “A review of wavelets in biomedical applications,†Proceedings of the IEEE, vol. 84, no. 4, pp. 626–638, 1996.
24. E. D. Übeyli, “Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks,†Digital Signal Processing, vol. 19, no. 1, pp. 134–143, 2009.
25. E. D. Übeyli, “Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines,†Computers in Biology and Medicine, vol. 38, no. 1, pp. 14–22, 2008.
26. E. D. Übeyli and I. Güler, “Features extracted by eigenvector methods for detecting variability of EEG signals,†Pattern Recognition Letters, vol. 28, no. 5, pp. 592–603, 2007.
27. S. A. Awang, M. Paulraj, and S. Yaacob, “Analysis of EEG signals by eigenvector methods,†inProceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES '12), pp. 778–783, December 2012.
28. Guerrero-Mosquera and A. N. Vazquez, “New approach in features extraction for EEG signal detection,†in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '09), pp. 13–16, September 2009.
29. L. Cohen, Time-Frequency Analysis, Prentice Hall, Upper Saddle River, NJ, USA, 1995.
30. P. Stoica and R. L. Moses, Introduction to Spectral Analysis, Prentice hall, Upper Saddle River, NJ, USA, 1997.
31. M. Polak and A. Kostov, “Feature extraction in development of brain-computer interface: a case study,†in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '98), pp. 2058–2061, November 1998.
32. Jung T-P, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski TJ (2001) Analysis and visualization of single-trial event-related potentials. Hum Brain Mapp 14(3):166–185
33. Miwakeichi F, MarÃnez-Montes E, Valdés-Sosa PA, Nishiyama N, Mizuhara H, Yamaguchi Y (2004) Decomposing EEG data into space-time-frequency components using parallel factor analysis. Neuroimage 22:1035–1045
34. Zervakis M, Michalopoulos K, Iordanidou V, Sakkalis V (2011) Intertrial coherence and causal interaction among independent EEG components. J Neurosci Methods 197:302–314 [33] Demiralp T, Ademoglu A, Comerchero M, Polich J (2011) Wavelet analysis of p3a and p3b. Brain Topogr 13(4):251–267
35. Makeig S, Debener S, Onton J, Delorme A (2004) Mining event-related brain dynamics. Trends Cogn Sci 8(5):204–210
36. Tallon-Baudry C, Bertrand O, Delpuech C, Pernier J (1996) Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. J Neurosci 16(13):4240–4249
37. Lazar N (2008) The statistical analysis of functional MRI data. Statistics for biology and health. Springer, New York
38. Skup M (2010) Longitudinal fMRI analysis: a review of methods. Stat Interface 3(2):235–252. PMID: 21691445; PMCID: PMC3117465
39. Lavric A, Bregadze N, Benattayallah A (2011) Detection of experimental ERP effects in combined EEG-fMRI: evaluating the benefits of interleaved acquisition and independent component analysis. Clin Neurophysiol 122:267–277
40. Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288
41. Lehmann D, Faber PL, Galderisi S, Herrmann WM, Kinoshita T, Koukkou M, Mucci A, Pascual-Marqui RD, Saito N, Wackermann J, Winterer G, Koenig T (2005) EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study. Psychiatry Res 138:141–156.
42. Lei X, Qiu C, Xu P, Yao D (2010) A parallel framework for simultaneous EEG/fMRI analysis: methodology and simulation. Neuroimage 52:1123–1134
43. Leicht G, Kirsch V, Giegling I, Karch S, Hantschk I, Moller HJ, Pogarell O, Hegerl U, Rujescu D, Mulert C (2010) Reduced early auditory evoked gamma-band response in patients with schizophrenia. Biol Psychiatry 67:224–231
44. Leicht G, Karch S, Karamatskos E, Giegling I, Moller HJ, Hegerl U, Pogarell O, Rujescu D, Mulert C (2011) Alterations of the early auditory evoked gamma-band response in first-degree relatives of patients with schizophrenia: hints to a new intermediate phenotype. J Psychiatr Res 45:699–705
45. Lemieux L (2004) Electroencephalography-correlated functional MR imaging studies of epileptic activity. Neuroimaging Clin N Am 14:487–506
46. Lemieux L, Allen PJ, Franconi F, Symms MR, Fish DR (1997) Recording of EEG during fMRI experiments: patient safety. Magn Reson Med 38:943–952
47. Levan P, Maclaren J, Herbst M, Sostheim R, Zaitsev M, Hennig J (2013) Ballistocardiographic artifact removal from simultaneous EEG-fMRI using an optical motion-tracking system. Neuroimage 75:1–11
48. Li R, Principe JC, Bradley M, Ferrari V (2007) Robust single-trial ERP estimation based on spatiotemporal filtering. Conf Proc IEEE Eng Med Biol Soc 2007:5206–5209
49. Lindsley DB (1952) Psychological phenomena and the electroencephalogram. Electroencephalogr Clin Neurophysiol 4:443–456
50. Liu Z, Kecman F, He B (2006) Effects of fMRI-EEG mismatches in cortical current density estimation integrating fMRI and EEG: a simulation study. Clin Neurophysiol 117:1610–1622
51. Liu Z, de Zwart JA, van Gelderen P, Kuo LW, Duyn JH (2012) Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings. Neuroimage 59:2073–2087
52. Llinas RR (1988) The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242:1654–1664
2. X.-Y. Wang, J. Jin, Y. Zhang, et al., “Brain control: human-computer integration control based on brain-computer interface approach,†Acta Automatica Sinica, vol. 39, no. 3, pp. 208–221, 2013.
3. G. Al-Hudhud, “Affective command-based control system integrating brain signals in commands control systems,†Computers in Human Behavior, vol. 30, pp. 535–541, 2014.
4. J. L. S. Blasco, E. Iáñez, A. Úbeda, and J. M. AzorÃn, “Visual evoked potential-based brain-machine interface applications to assist disabled people,†Expert Systems with Applications, vol. 39, no. 9, pp. 7908–7918, 2012.
5. H. Cecotti, “Spelling with non-invasive brain-computer interfaces—current and future trends,†Journal of Physiology, vol. 105, no. 1–3, pp. 106–114, 2011.
6. S. Kotchetkov, B. Y. Hwang, G. Appelboom, C. P. Kellner, and E. S. Connolly Jr., “Brain-computer interfaces: military, neurosurgical, and ethical perspective,†Neurosurgical Focus, vol. 28, no. 5, article E25, 2010.
7. D. Übeyli, “Statistics over features: EEG signals analysis,†Computers in Biology and Medicine, vol. 39, no. 8, pp. 733–741, 2009.
8. R. Agarwal, J. Gotman, D. Flanagan, and B. Rosenblatt, “Automatic EEG analysis during long-term monitoring in the ICU,†Electroencephalography and Clinical Neurophysiology, vol. 107, no. 1, pp. 44–58, 1998.
9. Meyer-Lindenberg, “The evolution of complexity in human brain development: an EEG study,â€Electroencephalography and Clinical Neurophysiology, vol. 99, no. 5, pp. 405–411, 1996.
10. Subasi and E. Erçelebi, “Classification of EEG signals using neural network and logistic regression,â€Computer Methods and Programs in Biomedicine, vol. 78, no. 2, pp. 87–99, 2005.
A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,â€Expert Systems with Applications, vol. 32, no. 4, pp. 1084–1093, 2007.
11. H. Jasper and L. D. Proctor, Reticular Formation of the Brain, 1958.
12. M. R. N. Kousarrizi, A. A. Ghanbari, M. Teshnehlab, M. Aliyari, and A. Gharaviri, “Feature extraction and classification of EEG signals using wavelet transform, SVM and artificial neural networks for brain computer interfaces,†in Proceedings of the International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS '09), pp. 352–355, August 2009.
13. Cvetkovic, E. D. Übeyli, and I. Cosic, “Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study,†Digital Signal Processing, vol. 18, no. 5, pp. 861–874, 2008.
14. H. Kordylewski, D. Graupe, and K. Liu, “A novel large-memory neural network as an aid in medical diagnosis applications,†IEEE Transactions on Information Technology in Biomedicine, vol. 5, no. 3, pp. 202–209, 2001.
15. Güler and E. D. Übeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,†Journal of Neuroscience Methods, vol. 148, no. 2, pp. 113–121, 2005.
16. D. Übeyli, “Wavelet/mixture of experts network structure for EEG signals classification,†Expert Systems with Applications, vol. 34, no. 3, pp. 1954–1962, 2008.
17. Subasi, M. K. Kiymik, A. Alkan, and E. Koklukaya, “Neural network classification of EEG signals by using AR with MLE preprocessing for epileptic seizure detection,†Mathematical and Computational Applications, vol. 10, no. 1, pp. 57–70, 2005.
18. O. Faust, R. U. Acharya, A. R. Allen, and C. M. Lin, “Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques,†IRBM, vol. 29, no. 1, pp. 44–52, 2008.
19. Procházka, J. Kukal, and O. Vyšata, “Wavelet transform use for feature extraction and EEG signal segments classification,†in Proceedings of the 3rd International Symposium on Communications, Control, and Signal Processing (ISCCSP '08), pp. 719–722, March 2008.
20. Daubechies, “Wavelet transform, time-frequency localization and signal analysis,†IEEE Transactions on Information Theory, vol. 36, no. 5, pp. 961–1005, 1990.
21. S. Soltani, “On the use of the wavelet decomposition for time series prediction,†Neurocomputing, vol. 48, no. 1, pp. 267–277, 2002
22. N. Hazarika, J. Z. Chen, A. C. Tsoi, and A. Sergejew, “Classification of EEG signals using the wavelet transform,†Signal Processing, vol. 59, no. 1, pp. 61–72, 1997.
23. M. Unser and A. Aldroubi, “A review of wavelets in biomedical applications,†Proceedings of the IEEE, vol. 84, no. 4, pp. 626–638, 1996.
24. E. D. Übeyli, “Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks,†Digital Signal Processing, vol. 19, no. 1, pp. 134–143, 2009.
25. E. D. Übeyli, “Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines,†Computers in Biology and Medicine, vol. 38, no. 1, pp. 14–22, 2008.
26. E. D. Übeyli and I. Güler, “Features extracted by eigenvector methods for detecting variability of EEG signals,†Pattern Recognition Letters, vol. 28, no. 5, pp. 592–603, 2007.
27. S. A. Awang, M. Paulraj, and S. Yaacob, “Analysis of EEG signals by eigenvector methods,†inProceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES '12), pp. 778–783, December 2012.
28. Guerrero-Mosquera and A. N. Vazquez, “New approach in features extraction for EEG signal detection,†in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '09), pp. 13–16, September 2009.
29. L. Cohen, Time-Frequency Analysis, Prentice Hall, Upper Saddle River, NJ, USA, 1995.
30. P. Stoica and R. L. Moses, Introduction to Spectral Analysis, Prentice hall, Upper Saddle River, NJ, USA, 1997.
31. M. Polak and A. Kostov, “Feature extraction in development of brain-computer interface: a case study,†in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '98), pp. 2058–2061, November 1998.
32. Jung T-P, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski TJ (2001) Analysis and visualization of single-trial event-related potentials. Hum Brain Mapp 14(3):166–185
33. Miwakeichi F, MarÃnez-Montes E, Valdés-Sosa PA, Nishiyama N, Mizuhara H, Yamaguchi Y (2004) Decomposing EEG data into space-time-frequency components using parallel factor analysis. Neuroimage 22:1035–1045
34. Zervakis M, Michalopoulos K, Iordanidou V, Sakkalis V (2011) Intertrial coherence and causal interaction among independent EEG components. J Neurosci Methods 197:302–314 [33] Demiralp T, Ademoglu A, Comerchero M, Polich J (2011) Wavelet analysis of p3a and p3b. Brain Topogr 13(4):251–267
35. Makeig S, Debener S, Onton J, Delorme A (2004) Mining event-related brain dynamics. Trends Cogn Sci 8(5):204–210
36. Tallon-Baudry C, Bertrand O, Delpuech C, Pernier J (1996) Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. J Neurosci 16(13):4240–4249
37. Lazar N (2008) The statistical analysis of functional MRI data. Statistics for biology and health. Springer, New York
38. Skup M (2010) Longitudinal fMRI analysis: a review of methods. Stat Interface 3(2):235–252. PMID: 21691445; PMCID: PMC3117465
39. Lavric A, Bregadze N, Benattayallah A (2011) Detection of experimental ERP effects in combined EEG-fMRI: evaluating the benefits of interleaved acquisition and independent component analysis. Clin Neurophysiol 122:267–277
40. Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288
41. Lehmann D, Faber PL, Galderisi S, Herrmann WM, Kinoshita T, Koukkou M, Mucci A, Pascual-Marqui RD, Saito N, Wackermann J, Winterer G, Koenig T (2005) EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study. Psychiatry Res 138:141–156.
42. Lei X, Qiu C, Xu P, Yao D (2010) A parallel framework for simultaneous EEG/fMRI analysis: methodology and simulation. Neuroimage 52:1123–1134
43. Leicht G, Kirsch V, Giegling I, Karch S, Hantschk I, Moller HJ, Pogarell O, Hegerl U, Rujescu D, Mulert C (2010) Reduced early auditory evoked gamma-band response in patients with schizophrenia. Biol Psychiatry 67:224–231
44. Leicht G, Karch S, Karamatskos E, Giegling I, Moller HJ, Hegerl U, Pogarell O, Rujescu D, Mulert C (2011) Alterations of the early auditory evoked gamma-band response in first-degree relatives of patients with schizophrenia: hints to a new intermediate phenotype. J Psychiatr Res 45:699–705
45. Lemieux L (2004) Electroencephalography-correlated functional MR imaging studies of epileptic activity. Neuroimaging Clin N Am 14:487–506
46. Lemieux L, Allen PJ, Franconi F, Symms MR, Fish DR (1997) Recording of EEG during fMRI experiments: patient safety. Magn Reson Med 38:943–952
47. Levan P, Maclaren J, Herbst M, Sostheim R, Zaitsev M, Hennig J (2013) Ballistocardiographic artifact removal from simultaneous EEG-fMRI using an optical motion-tracking system. Neuroimage 75:1–11
48. Li R, Principe JC, Bradley M, Ferrari V (2007) Robust single-trial ERP estimation based on spatiotemporal filtering. Conf Proc IEEE Eng Med Biol Soc 2007:5206–5209
49. Lindsley DB (1952) Psychological phenomena and the electroencephalogram. Electroencephalogr Clin Neurophysiol 4:443–456
50. Liu Z, Kecman F, He B (2006) Effects of fMRI-EEG mismatches in cortical current density estimation integrating fMRI and EEG: a simulation study. Clin Neurophysiol 117:1610–1622
51. Liu Z, de Zwart JA, van Gelderen P, Kuo LW, Duyn JH (2012) Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings. Neuroimage 59:2073–2087
52. Llinas RR (1988) The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242:1654–1664