Euler common spatial patterns for EEG classification

Sun, Jun; Wei, Mengting; Luo, Ning; Li, Zhanli; Wang, Haixian

doi:10.1007/s11517-021-02488-7

Cited by 10 publications

(2 citation statements)

References 40 publications

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“…Common spatial pattern is a commonly employed feature extraction method in the classification of MI EEG signals (Sun et al, 2022). Its fundamental concept involves projecting the data sequence onto a specific surface through the computation of a set of spatial filters.…”

Section: Cspmentioning

confidence: 99%

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks

Ma,

Dong,

Lin

et al. 2024

Front. Neurosci.

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BackgroundThe development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals.ObjectiveThis paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task.MethodsThe signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL.ResultsFor Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm’s performance was found to be optimal.ConclusionThe feature fusion of DTF network and graph theory features enhanced CSP algorithm’s performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model’s sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.

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Section: Cspmentioning

confidence: 99%

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks

Ma,

Dong,

Lin

et al. 2024

Front. Neurosci.

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“…At present, research on EEG analysis mainly includes three aspects: signal filtering, feature extraction, and recognition. In EEG feature extraction, the mainstream methods include differential entropy, the Fourier transform [9][10][11], power spectral density-based methods [12], autoregressive models [13], common spatial patterns [14], etc. For EEG recognition and classification, studies mainly adopt stochastic models [15], machine learning [16][17][18][19] and deep learning [20][21][22][23] methods.…”

Section: Introductionmentioning

confidence: 99%

Eye state recognition based on continuous wavelet transform and improved convolutional neural network

Xiao

Yang

Yuan

2023

Preprint

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EEG signals are multi-dimensional and non-stationary time series signals, and their features are not easy to extract, so large deviations are often encountered in the classification and prediction tasks. Therefore, in this paper, an EEG eye state (open or close) recognition method is proposed based on an improved convolutional neural network and the continuous wavelet transform. First, for the high noise levels encountered during EEG signal acquisition, variational mode decomposition (VMD) is used to decompose the EEG signal into several modal components, which are then subsequently used to reconstruct the signal according to the correlation principle to obtain a noiseless signal. Secondly, the continuous wavelet transform (CWT) method is used to extract the features of the EEG signals in the time and frequency domains, and the one-dimensional signal is converted into two-dimensional images to enhance the feature-learning ability convolutional networks. Finally, a residual term is introduced on the basis of the traditional convolutional neural network, and a recognition model is constructed by combining a dual channel and spatial attention mechanism. Experimental results show that the proposed model has an accuracy rate higher than 99\%, and is more effective and with better generalizability than previous studies.

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EEG Features Selection by Using Tasmanian Devil Optimization Algorithm for Stress Detection

Dhake,

Angal

2024

Algorithms for Intelligent Systems

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Euler common spatial patterns for EEG classification

Cited by 10 publications

References 40 publications

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks

Eye state recognition based on continuous wavelet transform and improved convolutional neural network

EEG Features Selection by Using Tasmanian Devil Optimization Algorithm for Stress Detection

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