A Novel Classification Algorithm for MI-EEG based on Deep Learning

Tang, Xiuzhen; Zhao, Jing; Fu, Wenli; Pan, Jiangfeng; Zhou, Huanyu

doi:10.1109/itaic.2019.8785541

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…Some research works combine the preprocessing steps in their deep learning pipeline and call it as end-to-end framework (Antoniades et al, 2018 ; Aznan et al, 2018 ; Zhang et al, 2021 ). Moreover, an additional CNN layer has been used for the preprocessing in some cases (Amin et al, 2019a ; Tang et al, 2019 ).…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

“…The dataset used to validate the classification method and signal processing for brain–computer interfaces was obtained from the BCI competition (Tabar and Halici, 2016 ; Amin et al, 2019b ; Dai et al, 2019 ; Olivas-Padilla and Chacon-Murguia, 2019 ; Qiao and Bi, 2019 ; Roy et al, 2019 ; Song et al, 2019 ; Tang et al, 2019 ; Tayeb et al, 2019 ; Zhao et al, 2019 ; Li Y. et al, 2020 ; Miao et al, 2020 ; Polat and Özerdem, 2020 ; Rammy et al, 2020 ; Yang et al, 2020 ; Deng et al, 2021 ; Huang et al, 2021 , 2022 ; Tiwari et al, 2021 ; Zhang et al, 2021 ). This dataset comprises EEG data obtained from participants.…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

“…Autoencoders (AE) and variational AE (VAE) (Tabar and Halici, 2016 ; Dai et al, 2019 ; Tang et al, 2019 ).…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

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Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

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In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

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Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

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Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

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“…The temporal feature extraction process is expressed as Eq. ( 12 A fully connected layer with 256 units is adopted to enhance the representation ability of temporal-spectral-spatial features, and it is followed by SoftMax as the last layer to obtain the prediction result, as shown in equations ( 13) and (14). ' 1 256 1 ()…”

Section: ) Detailed Information About Pmmclmentioning

confidence: 99%

Key Band Image Sequences and A Hybrid Deep Neural Network for Recognition of Motor Imagery EEG

Peng

Yang

2021

IEEE Access

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Deep neural network is a promising method to recognize motor imagery electroencephalography (MI-EEG), which is often used as the source signal of a rehabilitation system; and the core issues are the data representation and the matched neural networks. MI-EEG images is one of the main expressions, however, all the measured data of a trial are usually integrated into one image, causing information loss, especially in the time dimension; and the neural network architecture might not fully extract the features over the α and β frequency bands, which are closely related to MI. In this paper, we propose a key band imaging method (KBIM). A short time Fourier transform is applied to each electrode of the MI-EEG signal to generate a time-frequency image, and the parts corresponding to the α and β bands are intercepted, fused, and further arranged into the EEG electrode map by the nearest neighbor interpolation method, forming two key band image sequences. In addition, a hybrid deep neural network named the parallel multimodule convolutional neural network and long short-term memory network (PMMCL) is designed for the extraction and fusion of the spatial-spectral and temporal features of two key band image sequences to realize automatic classification of MI-EEG signals. Extensive experiments are conducted on two public datasets, and the accuracies after 10-fold cross-validation are 97.42% and 77.33%, respectively. Statistical analysis shows the superb discrimination ability for multiclass MI-EEG too. The results demonstrate that KBIM can preserve the integrity of the feature information, and they well match with PMMCL.

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“…Deep neural networks (DNNs) have been widely for various tasks: classification [1], [2], segmentation [3]- [5], recognition [6], [7], caption-generation, [8]- [10] and translation [11], [12]. However, DNNs are commonly known to have heavy computational loads and require large amounts of memory to store billions of parameters.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Neural Network for Reducing Expected Inference Time

Putra

Leu

2019

IEEE Access

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It is widely known that deep neural networks (DNNs) can perform well in many applications, and can sometimes exceed human ability. However, their cost limits their impact in a variety of realworld applications, such as IoT and mobile computing. Recently, many DNN compression and acceleration methods have been employed to overcome this problem. Most methods succeed in reducing the number of parameters and FLOPs, but only a few can speed up expected inference times because of either the overhead generated from using such methods or DNN framework deficiencies. Edge-cloud computing has recently emerged and presents an opportunity for new model acceleration and compression techniques. To address the aforementioned problem, we propose a novel technique to speed up expected inference times by using several networks that perform the exact same task with different strengths. Although our method is based on edge-cloud computing, it is suitable for any other hierarchical computing paradigm. Using a simple yet strong enough estimator, the system predicts whether the data should be passed to a larger network or not. Extensive experimental results demonstrate that the proposed technique can speed up expected inference times and beat almost all state-of-the-art compression techniques, including pruning, low-rank approximation, knowledge distillation, and branchy-type networks, on both CPUs and GPUs. INDEX TERMS Edge computing, mobile computing, network compression and acceleration.

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A Novel Classification Algorithm for MI-EEG based on Deep Learning

Cited by 9 publications

References 9 publications

Status of deep learning for EEG-based brain–computer interface applications

Status of deep learning for EEG-based brain–computer interface applications

Key Band Image Sequences and A Hybrid Deep Neural Network for Recognition of Motor Imagery EEG

Multilevel Neural Network for Reducing Expected Inference Time

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