Abstract
Machine learning and pattern recognition have been widely applied in EEG analysis. They provide new approaches to decode and characterize task-related brain states and extract them from non-informative high-dimensional EEG data. Given the growth in the interest and breadth of application, we introduce how to apply machine learning techniques in EEG analysis. First, we give an overview of machine learning analysis and introduce several basic concepts. Then, we propose a scientific question of discriminating EEG data under eyes-open and eyes-closed resting-state conditions, and provide a step-by-step tutorial including extracting features, training features, feature selection and dimension reduction, selecting a classifier, testing the classifier, evaluating results, and pattern expression. We also discuss perspective, particularly the deep learning algorithms, for future study. In the last section of this chapter, we give detailed MATLAB codes for implementing machine learning analysis for classifying eyes-open and eyes-closed EEG data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blankertz B, Lemm S, Treder M, Haufe S, Müller KR. Single-trial analysis and classification of ERP components – a tutorial. Neuroimage. 2011;56:814–25.
Cecotti H, Graeser A. Convolutional neural network with embedded Fourier transform for EEG classification. In: 19th international conference on pattern recognition. 2008. pp. 1–4.
Gajraj RJ, Doi M, Mantzaridis H, Kenny GN. Analysis of the EEG bispectrum, auditory evoked potentials and the EEG power spectrum during repeated transitions from consciousness to unconsciousness. Br J Anaesth. 1998;80:46–52.
Gysels E, Renevey P, Celka P. SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in brain-computer interfaces. Signal Process. 2005;85:2178–89.
Hu L, et al. Single-trial detection of somatosensory evoked potentials by probabilistic independent component analysis and wavelet filtering. Clin Neurophysiol. 2011;122:1429–39.
Hu L, Xiao P, Zhang ZG, Mouraux A, Iannetti GD. Single-trial time-frequency analysis of electrocortical signals: baseline correction and beyond. Neuroimage. 2014;84:876–87.
Hu L, Zhang ZG, Mouraux A, Iannetti GD. Multiple linear regression to estimate time-frequency electrophysiological responses in single trials. Neuroimage. 2015;111:442–53.
Huang G, et al. A novel approach to predict subjective pain perception from single-trial laser-evoked potentials. Neuroimage. 2013;81:283–93.
Jolliffe I. International encyclopedia of statistical science 1094–1096. Berlin Heidelberg: Springer; 2011.
Li K, Li X, Zhang Y, Zhang A Affective state recognition from EEG with deep belief networks. In: IEEE international conference on bioinformatics and biomedicine. 2013. pp. 305–310.
Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng. 2007;4:R1–R13.
Makeig S, et al. Evolving signal processing for brain-computer interfaces. Proc IEEE. 2012;100:1567–84.
Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods. 2007;164:177–90.
Mirowski P, Madhavan D, LeCun Y, Kuzniecky R. Classification of patterns of EEG synchronization for seizure prediction. Clin Neurophysiol. 2009;120:1927–40.
Müller KR, et al. Machine learning for real-time single-trial EEG-analysis: from brain–computer interfacing to mental state monitoring. J Neurosci Methods. 2008;167:82–90.
Mwangi B, Tian TS, Soares JC. A review of feature reduction techniques in Neuroimaging. Neuroinformatics. 2014;12:229–44.
Schoffelen JM, Gross J. Source connectivity analysis with MEG and EEG. Hum Brain Mapp. 2009;30:1857–65.
Shen KQ, Ong CJ, Li XP, Zheng H, Wilder-Smith EP. Feature selection method for multilevel mental fatigue EEG classification. IEEE Trans Biomed Eng. 2007;54:1231–7.
Srinivasan R, Winter WR, Ding J, Nunez PL. EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J Neurosci Methods. 2007;166:41–52.
Subasi A, Ismail Gursoy M. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl. 2010;37:8659–66.
Tabar YR, Halici U. A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng. 2017;14:016003.
Tibshirani R. Regression Shrinkage and Selection via the Lasso. J R Stat Soc Ser B (Methodological). 1996;58:267–88.
Tu Y, et al. An automated and fast approach to detect single-trial visual evoked potentials with application to brain–computer interface. Clin Neurophysiol. 2014;125:2372–83.
Tu Y, Hung YS, Hu L, Zhang Z. PCA-SIR: a new nonlinear supervised dimension reduction method with application to pain prediction from EEG. In: 7th International IEEE/EMBS Conference on Neural Engineering (NER). 2015. pp. 1004–1007.
Tu Y, et al. Alpha and gamma oscillation amplitudes synergistically predict the perception of forthcoming nociceptive stimuli. Hum Brain Mapp. 2016;37:501–14.
Van de Ville D, Britz J, Michel CM. EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Natl Acad Sci U S A. 2010;107:18179–84.
Wang XW, Nie D, Lu BL. Emotional state classification from EEG data using machine learning approach. Neurocomputing. 2014;129:94–106.
Zhang ZG, Hu L, Hung YS, Mouraux A, Iannetti GD. Gamma-band oscillations in the primary somatosensory cortex – a direct and obligatory correlate of subjective pain intensity. J Neurosci. 2012;32:7429–38.
Zheng W-L, Zhu J-Y, Peng Y, Lu B-L EEG-based emotion classification using deep belief networks. In: IEEE International Conference on Multimedia and Expo (ICME). 2014. pp. 1–6.
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Statistical Methodology). 2005;67:301–20.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
15.1 Supplementary Electronic Material (S)
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Tu, Y. (2019). Machine Learning. In: Hu, L., Zhang, Z. (eds) EEG Signal Processing and Feature Extraction. Springer, Singapore. https://doi.org/10.1007/978-981-13-9113-2_15
Download citation
DOI: https://doi.org/10.1007/978-981-13-9113-2_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9112-5
Online ISBN: 978-981-13-9113-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)