Ensemble Regularized Common Spatio-Spectral Pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification

Cherloo, Mohammad Norizadeh; Amiri, Homa Kashefi; Daliri, Mohammad Reza

doi:10.1016/j.compbiomed.2021.104546

Cited by 24 publications

(14 citation statements)

References 30 publications

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“…The proposed method using log-penalized graph learning outperforms the alternative methods, on average across subjects. The method by (Georgiadis et al, 2021) shows the best classification accuracy in subject av, whereas that of (Cherloo et al, 2021) shows the best accuracy in subject aw. In the other three subjects, the proposed method yields higher classification accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, the performance of the proposed method was compared to four recent alternative state-of-the-art methods: a GSPbased method (Georgiadis et al, 2021) that utilizes graph Slepians (Van De Ville et al, 2017), and three methods that utilize different extensions of the FKT (Cherloo et al, 2021; Wang et al, 2020; Hou et al, 2022). The results are presented in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…Secondly, for MI task decoding in particular, given that the frontal and parietal cortices are more involved, by choosing a subset of electrodes—e.g. as in (Georgiadis et al, 2021; Cherloo et al, 2021), graph learning can be localized to these regions of interest. Furthermore, it may be beneficial to extract coarse features at the resolution of spectral graph frequency bands rather than GFT coefficients, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…The training and test EEG signal sets for each subject are initially preprocessed, and then fed into the training and test phases, respectively. As temporal preprocessing, for each trial, we used the time points within the 0.5-2.5 second interval after the visual cue to construct graph signals; this 2-second interval has been previously used in related works (Wang et al, 2020; Cherloo et al, 2021; Georgiadis et al, 2021). Motor activity, be it real or imagined, modulates the sensorimotor mu rhythm (8-13 Hz) and beta rhythm (13-30 Hz), therefore, we filtered the extracted signal with a third-order Butterworth filter with a passband of 8-30 Hz.…”

Section: Methodsmentioning

confidence: 99%

“…Discrimination of mental states from EEG data is a challenging task, for which numerous methods have been proposed (Singh et al, 2021; Mirjalili et al, 2022). One class of methods aims at extracting features from the temporal evolution of the data, in time or frequency domain (Abootalebi et al, 2009; Kim et al, 2018; Lee et al, 2019; Gonzalez-Astudillo et al, 2021; Gwon and Ahn, 2021), whereas an alternative class aims at extracting spatial features from EEG maps (Ramoser et al, 2000; Lotte and Guan, 2010; Wang et al, 2020; Cherloo et al, 2021; Hou et al, 2022). Adaptive classifiers, matrix and tensor classifiers, transfer learning, and deep learning are among other methods that have been more recently proposed (Lotte et al, 2018; Peterson et al, 2021; Dose et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Miri

Abootalebi

Saeedi-Sourck

et al. 2022

Preprint

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Objective: This paper presents a graph signal processing (GSP)-based approach for decoding two-class motor imagery EEG data via deriving task-specific discriminative features. Methods: First, a graph learning (GL) method is used to learn subject-specific graphs from EEG signals. Second, by diagonalizing the normalized Laplacian matrix of each subject graph, an orthonormal basis is obtained using which the graph Fourier transform (GFT) of the EEG signals is computed. Third, the GFT coefficients are mapped into a discriminative subspace for differentiating two class data using a projection matrix obtained by the Fukunaga-Koontz transform (FKT). Finally, an SVM classifier is trained and tested on the variance of the resulting features to differentiate motor imagery classes. Results: The proposed method is evaluated on Dataset IVa of the BCI Competition III and its performance is compared to i) using features extracted on a graph constructed by Pearson correlation coefficients and ii) three state-of-the-art alternative methods. Conclusion: Experimental results indicate the superiority of the proposed method over alternative methods, reflecting the added benefit of integrating elements from GL, GSP and FKT. Significance: The proposed method and results underpin the importance of integrating spatial and temporal characteristics of EEG signals in extracting features that can more powerfully differentiate motor imagery classes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Miri

Abootalebi

Saeedi-Sourck

et al. 2022

Preprint

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Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

Alizadeh

Afrakhteh

Mosavi

2023

Int J Imaging Syst Tech

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Brain–computer interfaces (BCI) have begun to revolutionize many aspects of human life, ranging from health to smart living and communication devices. Therefore, BCI technologies require accurate recognition and classification systems of the brain responses to a variety of motor imagery (MI) movements through electroencephalogram (EEG) signals. Nowadays, deep learning models have received a lot of interest for learning features and classifying numerous kinds of data. However, it has not been thoroughly investigated for EEG signal classification. In this study, to reach an improved performance in MI‐EEG signal classification, we proposed a new strategy based on continuous wavelet transform (CWT)‐based time‐frequency maps to generate two‐dimensional (2D) EEG images of adaptively reconstructed signals from the extracted multivariate mode decomposition (MVMD) modes. In the next step, the extracted signals are projected in the new space using the proposed multiclass common spatial pattern (MCCSP) filtering. The resulting images are then fed to the convolutional neural network (CNN) architectures (AlexNet and LeNet). The proposed framework has the benefit of achieving high classification accuracy to be attained even with a significant amount of input data. LeNet and AlexNet reach the best average accuracy rates of 95.33% and 93.66% on dataset 1 from BCI competition IV and the results on dataset 2a from competition IV are more promising than the current state of the arts. Our results depict that MI‐EEG task recognition using image classification approaches, along with CNNs, is as comparable or even superior to other existing traditional approaches and provides high potential for upcoming research.

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Analysing Big Brain Signal Data for Advanced Brain Computer Interface System

Khanam

Siuly

Wang

2022

Lecture Notes in Computer Science

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Ensemble Regularized Common Spatio-Spectral Pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification

Cited by 24 publications

References 30 publications

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

Analysing Big Brain Signal Data for Advanced Brain Computer Interface System

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