EEG Classification Based on Sparse Representation and Deep Learning

Gao, Guangchun; Shang, Lei; Xiong, Kai; Fang, Jian; Zhang, Cui; Gu, Xuejun

doi:10.14704/nq.2018.16.6.1666

Cited by 10 publications

(23 citation statements)

References 25 publications

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“…It is The copyright holder for this preprint this version posted June 18, 2021. ; https://doi.org/10.1101/2021.06.18.448960 doi: bioRxiv preprint CNN Classification for Motor Imagery BCIs 23based Classification (SRC) algorithm for binary classification of the MI task. The dataset adopted by Gao et al (2018) was BCI competition III (Dataset IVa). Here the authors showed that their SRC+CNN model achieved mean accuracy of 80% (Gao et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…CC-BY-NC-ND 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is (Tang et al, 2017;Gao et al, 2018;Sakhavi et al, 2015;Li et al, 2020;Dai et al, 2019;Tayeb et al, 2019;Stieger et al, 2020;Zhang et al, 2021;Ko et al, 2020;Mane et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

Leeuwis

Alimardani

2021

Preprint

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Motor Imagery (MI) is a mental process by which an individual rehearses body movements without actually performing physical actions. Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with this mental process and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). However, in recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. In this study, EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp was employed to compare the performance of two MI-BCI classifiers; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. The mean classification accuracies achieved by the CNN and CSP+LDA models were 69.42% and 52.56%, respectively. Further analysis showed that the DL approach improved the classification accuracy for all subjects within the range of 2.37 to 28.28% and that the improvement was significantly stronger for low performers. Our findings show promise for employment of DL models in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

Leeuwis

Alimardani

2021

Preprint

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Section: Methodsmentioning

confidence: 99%

“…First, EEG waveforms need to be segmented to the 3-s time samples, and140 experimental samples can be achieved for each type of EEG signal. To reduce the interference from other sources such as electrooculogramsand electromyograms, 8-to 15-Hz bandpass filters were applied in this article (Gao et al, 2018).The CSP method is an effective method in the feature extraction problem of motion imaging signals.It is suitable for two classes (conditions) of multichannel EEG-baBCIs, so this article adopts the CSP method to filter the EEG signals and extract energy features. When the number of CSP filters is set as 32, after filtering operation, training and testing EEG samples can be converted to 32 CSP eigenvalues, which can be used for data classification.…”

Section: Data Preprocessingmentioning

confidence: 99%

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An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

Huang

Chen

et al. 2020

Front. Neurosci.

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The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of EEG types with high accuracy, a classification methodology using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNNs) is proposed. In the proposed methodology, EEG waveforms of classes 1 and 2 are segmented into subsignals,and 140 experimental samples were achieved for each type of EEG signal. The common spatial patterns algorithm is used to obtain the features of the EEG signal. Subsequently, the redundant dictionary with sparse representation is constructed based on these features. Finally, the samples of the EEG types were imported into the FCRes-CNN model having fast down-sampling module and residual block structural units to be identified and classified. The datasets from BCI Competition 2005 (dataset IVa) and BCI Competition 2003 (dataset III) were used to test the performance of the proposed deep learning classifier. The classification experiments show that the recognition averaged accuracy of the proposed method is 98.82%. The experimental results show that the classification method provides better classification performance compared with sparse representation classification (SRC) method. The method can be applied successfully to BCI systems where the amount of data is large due to daily recording.

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Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network

Liu¹,

Raza²,

Bhattacharyya³

2023

Advanced Methods in Biomedical Signal Processing and Analysis

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EEG Classification Based on Sparse Representation and Deep Learning

Cited by 10 publications

References 25 publications

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network

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