Abstract
A brain–computer interface (BCI) utilizes brain signals such as electroencephalogram (EEG) and provides a pathway for people to interact with external assistive devices. The objective of this work is to classify the tasks so that we can assist the disabled person in doing things on own way with the aid of BCI. The raw EEG signals have a chance of being affected with interference and hence have low signal-to-noise ratio (SNR) which may lead to erroneous results. These EEG signals are decomposed into intrinsic mode functions (IMFs) using different standard algorithms like empirical mode decomposition (EMD) and multivariate empirical mode decomposition (MEMD). Different features like skewness, k-nearest neighbour (kNN) entropy, sample entropy and permutation entropy are extracted from these IMFs which will significantly contribute to the classification of tasks. This work is carried out on the well-established BCI motor imagery data set, BCI competition IVa data set 1 which will support the analysis. These extracted features are subjected to classifiers like random forest, Naive Bayes and J48 classifiers. The classification accuracies have been recorded, and improved results are achieved using MEMD.
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
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791
Jahankhani P, Kodogiannis V, Revett K (2006) EEG signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 international symposium on modern computing (JVA’06)
Qin L, Ding L, He B (2004) Motor imagery classification by means of source analysis for brain-computer interface applications. J Neural Eng 1(3):135
Pijn JP, Van Neerven J, Noest A, da Silva FHL (1991) Chaos or noise in EEG signals; dependence on state and brain site. Electroencephalogr Clin Neurophysiol 79(5):371–381
Park C, Looney D, urRehman N, Ahrabian A, Mandic DP (2012) Classification of motor imagery BCI using multivariate empirical mode decomposition. IEEE Trans Neural Syst Rehabil Eng 21(1):10–22
Gaur P, Pachori RB, Wang H, Prasad G (2019) An automatic subject specific intrinsic mode function selection for enhancing two-class EEG based motor imagery-brain computer interface. IEEE Sens J
Allen J (1977) Short term spectral analysis, synthesis, and modification by discrete fourier transform. IEEE Trans Acoust, Speech, Signal Process 25(3):235–238
Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 36(5):961–1005
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London Ser A Math Phys Eng Sci 454(1971):903–995
Mandic DP, ur Rehman N, Wu Z, Huang NE (2013) Empirical mode decomposition-based time-frequency analysis of multivariate signals: the power of adaptive data analysis. IEEE Signal Process Mag 30(6):74–86
Dutta S, Singh M, Kumar A (2018) Automated classification of non-motor mental task in electroencephalogram based brain-computer interface using multivariate autoregressive model in the intrinsic mode function domain. Biomed Signal Process Control 43:174–182
Gaur P, Kaushik G, Pachori RB, Wang H, Prasad G (2019) Comparison analysis: single and multichannel EMD-based filtering with application to BCI. In: Machine intelligence and signal analysis. Springer, pp 107–118
Rehman N, Mandic DP (2009) Multivariate empirical mode decomposition. Proc R Soc A Math, Phys Eng Sci 466(2117):1291–1302
Zhang H, Guan C, Ang KK, Chin ZY (2012) BCI competition IV-data set I: learning discriminative patterns for self-paced EEG-based motor imagery detection. Front Neurosci 6:7
Gupta A, Agrawal R (2012) Relevant feature selection from EEG signal for mental task classification. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 431–442
Kraskov A, Stögbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E 69(6):066138
Azami H, Fernández A, Escudero J (2017) Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis. Med Biol Eng Comput 55(11):2037–2052
Khushaba RN, Al-Jumaily A, Al-Ani A (2007) Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control. In: 2007 international symposium on communications and information technologies. IEEE, pp 352–357
Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):174102
Lewis DD (1998) Naive (bayes) at forty: the independence assumption in information retrieval. In: European conference on machine learning. Springer, pp 4–15
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217–222
Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learning
Kohavi R et al (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, vol 14. Montreal, Canada, pp 1137–1145
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sri Ramya, P., Yashasvi, K., Anjum, A., Bhattacharyya, A., Pachori, R.B. (2020). Development of an Effective Computing Framework for Classification of Motor Imagery EEG Signals for Brain–Computer Interface. In: Jain, S., Sood, M., Paul, S. (eds) Advances in Computational Intelligence Techniques. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2620-6_2
Download citation
DOI: https://doi.org/10.1007/978-981-15-2620-6_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2619-0
Online ISBN: 978-981-15-2620-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)