Motor imagery EEG classification with optimal subset of wavelet based common spatial pattern and kernel extreme learning machine

Park, Hyeong-jun; Kim, Jong-In; Min, Beomjun; Lee, Boreom

doi:10.1109/embc.2017.8037454

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…This method has unique advantages in nonstationary signal processing, so it has been widely used in EEG signal processing. Grossman and Morlet proposed the Wavelet Transform (WT) in 1984 [15]. The wavelet transform is a new development of the Fourier transform, and the wavelet transform coefficients can reflect the local information of the signal in the time and frequency domains.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Algorithm for EEG Signal Processing Based on Motor Imagery Brain-Computer Interface

Geng

Xue

et al. 2022

Wireless Communications and Mobile Computing

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Electroencephalogram (EEG) signal processing is a very important module in the brain-computer interface system. As an important physiological feature of the human body, EEG signals are closely related to the functional state of the cerebral nervous system. However, the EEG signals collected on the scalp are generally weak and inevitably subject to various noise interferences. In order to remove artifacts from the EEG in brain computer interfaces (BCIs), a fusion algorithm for EEG signal preprocessing is proposed. The fusion method includes the following steps: firstly, the raw EEG signals are separated into a set of statistics independent components (ICs) by the improved FastICA algorithm. Then, each independent component is decomposed into a series of intrinsic mode functions (IMFs) by using the improved empirical mode decomposition method (EMD). Many IMFs with high-frequency noise are deleted. The rest of the IMFs are reconstructed. Furthermore, artifacts are further eliminated by iterative process of the improved FastICA algorithm, and then, the EEG signals are reconstructed again by inverse ICA. Finally, the cleaned EEG signal was obtained. The comparative experiment shows that the EMD-ICA fusion algorithm not only accurately eliminates the artifact components but also better retains the local characteristics of the raw EEG. Continuous wavelet transform was used to extract energy features of μ rhythm and β rhythmic to represent the characteristics of EEG signals under different motor imageries. These two features are normalized and used as the input data of the convolutional neural network (CNN) designed by the paper, and the two kinds of features are learned by CNN, and then, the two-classification problem of motor imagery EEG signals is completed. The experimental results show that the average classification accuracy and kappa value of the proposed method are higher than those of SVM and SAE for most subjects.

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Section: Introductionmentioning

confidence: 99%

A Fusion Algorithm for EEG Signal Processing Based on Motor Imagery Brain-Computer Interface

Geng

Xue

et al. 2022

Wireless Communications and Mobile Computing

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“…6 depicts that the high neuron activation onset in the FC1 channel appears at approximately 800 msec after stimulation for S8. In other studies, usually the C3 and CZ area are reported as the activated area for imaginary right hand movements [4,31]; whilst in our study, the FC1 and CP6 channels are known as the affected area.…”

Section: Discussionmentioning

confidence: 51%

Combination of discrete wavelet packet transform with detrended fluctuation analysis using customized mother wavelet with the aim of an imagery-motor control interface for an exoskeleton

Hekmatmanesh

Nasrabadi

et al. 2019

Multimed Tools Appl

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One critical issue in brain computer interface (BCI) studies is to extract imaginary movement patterns from electroencephalograph (EEG). In this study, two different techniques -detrended fluctuation analysis (DFA) and discrete wavelet packet transform-are combined (DWPT-DFA) for feature extraction. Both approaches are known as self-similarity quantifier techniques. In wavelet technique, mother wavelets play an important role. Herein, A customized mother wavelet utilizing event related desynchronization (ERD) potential patterns are extracted and updated automatically for individual subjects. Also, three predefined mother wavelets are used, and the results are compared with the customized mother wavelet. The predefined mother wavelets are db4, db8 and coiflet 4. The soft margin support vector machine with the generalized radial basis function (SSVM-GRBF) is employed to classify the DWPT-DFA features. For the efficiency of the method, nine subjects have participated to record EEG based on the imaginary hand movements. The ERDs and features are extracted from FC1 and CP6 channels. Results show that the combination of the DWPT and DFA with the personalize ERD mother wavelet gives the best accuracy of 85.33% with p < 0.001. Based on the results, we conclude that the DWPT-DFA method using the ERD mother wavelets improves significantly the efficiency of the SSVM-GRBF classifier.Keywords Brain computer interface (BCI) · Detrended fluctuation analysis (DFA) · Discrete wavelet packet transform (DWPT) · Event related desynchronization (ERD) · Soft margin support vector machine with generalized radial basis function (SSVM-GRBF)

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“…If feature extractions are not performed well, it may result in consistently low performance and non-robust model for EEG motor imagery decoding [17]. Indeed, the performance of a BCI system using motor imagery is greatly depending on how features are extracted [18]. Additionally, the non-stationary nature of EEG and the amount of data generated with fewer electrodes makes it impractical to perform real time classification by manually extracting features [19].…”

Section: Related Workmentioning

confidence: 99%

A Channel Selection Approach Based on Convolutional Neural Network for Multi-channel EEG Motor Imagery Decoding

Mzurikwao

Samuel

Asogbon

et al. 2019

2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

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Motor imagery EEG classification with optimal subset of wavelet based common spatial pattern and kernel extreme learning machine

Cited by 9 publications

References 13 publications

A Fusion Algorithm for EEG Signal Processing Based on Motor Imagery Brain-Computer Interface

A Fusion Algorithm for EEG Signal Processing Based on Motor Imagery Brain-Computer Interface

Combination of discrete wavelet packet transform with detrended fluctuation analysis using customized mother wavelet with the aim of an imagery-motor control interface for an exoskeleton

A Channel Selection Approach Based on Convolutional Neural Network for Multi-channel EEG Motor Imagery Decoding

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