Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

Altaheri, Hamdi; Muhammad, Ghulam; Alsulaiman, Mansour; Altuwaijri, Ghadir Ali; Abdul, Wadood; Bencherif, Mohamed A.; Faisal, Mohammed

doi:10.1007/s00521-021-06352-5

Cited by 199 publications

(135 citation statements)

References 150 publications

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“…After the evaluation stage, however, the following points are worth mentioning: Preprocessing and Extraction Topoplots: Preprocessing of EEG signals is commonly implemented by three steps: artifact removal, channel selection, and frequency filtering. Based on the fact that DL can extract useful features from raw and unfiltered data, as a rule, the first two steps are not performed [65,66]. As channel selection may enable lower generalization errors in DL [67], we compute the topoplot representation from the whole set of EEG channels to visualize the spectral power variations on the scalp averaged over each MI interval.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Collazos-Huertas

Álvarez-Meza

Castellanos-Domínguez

2022

Applied Sciences

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Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain–Computer Interfaces (BCIs) technology. However, the substantial inter/intra variability of recorded data significantly influences individual skills on the achieved performance. This study explores the ability to distinguish between MI tasks and the interpretability of the brain’s ability to produce elicited mental responses with improved accuracy. We develop a Deep and Wide Convolutional Neuronal Network fed by a set of topoplots extracted from the multichannel EEG data. Further, we perform a visualization technique based on gradient-based class activation maps (namely, GradCam++) at different intervals along the MI paradigm timeline to account for intra-subject variability in neural responses over time. We also cluster the dynamic spatial representation of the extracted maps across the subject set to come to a deeper understanding of MI-BCI coordination skills. According to the results obtained from the evaluated GigaScience Database of motor-evoked potentials, the developed approach enhances the physiological explanation of motor imagery in aspects such as neural synchronization between rhythms, brain lateralization, and the ability to predict the MI onset responses and their evolution during training sessions.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Collazos-Huertas

Álvarez-Meza

Castellanos-Domínguez

2022

Applied Sciences

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“…The current advancement in deep learning techniques achieves promising performance not only in vision-based sign language recognition but also in many other vision-based fields such as object detection, image classification, and action recognition [6,7].…”

Section: Introductionmentioning

confidence: 99%

Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition

Al-Hammadi

Bencherif

Alsulaiman

et al. 2022

Sensors

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Sign language is the main channel for hearing-impaired people to communicate with others. It is a visual language that conveys highly structured components of manual and non-manual parameters such that it needs a lot of effort to master by hearing people. Sign language recognition aims to facilitate this mastering difficulty and bridge the communication gap between hearing-impaired people and others. This study presents an efficient architecture for sign language recognition based on a convolutional graph neural network (GCN). The presented architecture consists of a few separable 3DGCN layers, which are enhanced by a spatial attention mechanism. The limited number of layers in the proposed architecture enables it to avoid the common over-smoothing problem in deep graph neural networks. Furthermore, the attention mechanism enhances the spatial context representation of the gestures. The proposed architecture is evaluated on different datasets and shows outstanding results.

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“…Although multiple brain waves may happen at the same time, only one brain wave will be dominant. Most research used the range between 0–35 Hz [ 7 ]. This paper focuses on using a raw EEG signal without any preprocessing, and uses the full band for the dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the papers that classify EEG-based motor imagery using deep learning can be divided into four approaches depending on the formulation of input. The input formulation can be extracted features, spectral images, raw signal values, or topological maps [ 7 ]. The architecture of the deep learning model played a big role in deciding which input formulation to use.…”

Section: Introductionmentioning

confidence: 99%

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

Altuwaijri

Muhammad

2022

Biosensors

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Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for convolutional neural network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method’s promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods.

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Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

Cited by 199 publications

References 150 publications

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

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