Motor Imagery EEG Classification Using Capsule Networks

Ha, Kwon-Woo; Jeong, Jinwoo

doi:10.3390/s19132854

Cited by 108 publications

(67 citation statements)

References 34 publications

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“…Notably, we acknowledged the existence of numerous alternative novel algorithms for decoding neural features of the EEG signal [ 41 , 42 , 43 , 44 , 45 ]. Among them, deep learning and EEG channel optimization methods are the most relevant methods for this study.…”

Section: Discussionmentioning

confidence: 99%

“…Among them, deep learning and EEG channel optimization methods are the most relevant methods for this study. Convolutional Neural Network and its applied algorithms are the prominent and spotlighted algorithm for MI signal toward an image domain analysis through the ERSP or short-time Fourier transform (STFT) [ 43 ]. Additionally, the EEG MI signals present prevailing spatial feature via a multi-electrodes channel.…”

Section: Discussionmentioning

confidence: 99%

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Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

Choi

Kim

Jeong

et al. 2020

Sensors

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This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

Choi

Kim

Jeong

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The outlook of poorly outlined transformed images can be improved by applying the logarithmic transform [37] to the image values using the expression in Eq. (14).…”

Section: Gramian Angular Fieldsmentioning

confidence: 99%

“…where I GASF is the GASF image after the time series polar plot transformation, and Ĩ GASF is the log transformed image. A method of improving the image contrast is suggested in [37].…”

Section: Gramian Angular Fieldsmentioning

confidence: 99%

Image-based mains signal disaggregation and load recognition

Matindife

Sun

Wang

2021

Complex Intell. Syst.

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The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance.

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“…The core of this method is improving the feature extraction method to enhance the classification accuracy of the neural network. However, [14] used the STFT algorithm to transform the EEG signal of a moving image into a two-dimensional image, then used CapsNet to learn all kinds of characteristics of EEG signals; this solution improved the EEG preprocessing method to enhance the detection effect. Reference [15] sought to improve the neural network and integrate the weight splitting technology into the algorithm of the Back-Propagation (BP) neural network for EEG recognition and analysis, with the aim of enhancing the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation

Huang

Xue

et al. 2020

IEEE Access

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As one of the most important research fields in the brain-computer interface (BCI) field, electroencephalogram (EEG) classification has a wide range of application values. However, for the EEG signal, it is difficult for the traditional neural networks to capture the characteristics of the EEG signal more comprehensively from the time and space dimensions, which has a certain effect on the accuracy of EEG classification. To solve this problem, we can improve the accuracy of classification via end-to-end learning of the time and space dimensions of EEG. In this paper, a new type of EEG classification network, the separable EEGNet (S-EEGNet), is proposed based on Hilbert-Huang transform (HHT) and a separable convolutional neural network (CNN) with bilinear interpolation. The EEG signal is transformed into time-frequency representation by HHT, which allows the EEG signal to be better described in the frequency domain. Then, the depthwise and pointwise elements of the network are combined to extract the feature map. The displacement variable is added by the bilinear interpolation method to the convolution layer of the separable CNN, allowing the free deformation of the sampling grid. The deformation depends on the local, dense, and adaptive input characteristics of the EEG data. The network can learn from the time and space dimensions of EEG signals end to end to extract features to improve the accuracy of EEG classification. To show the effectiveness of S-EEGNet, the team used this method to test two different types of EEG public datasets (motor imagery classification and emotion classification). The accuracy of motor imagery classification is 77.9%, and the accuracy of emotion classification is 89.91%, and 88.31%, respectively. The experimental results showed that the classification accuracy of S-EEGNet improved by 3.6%, 1.15%, and 1.33%, respectively.

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Motor Imagery EEG Classification Using Capsule Networks

Cited by 108 publications

References 34 publications

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

Image-based mains signal disaggregation and load recognition

S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation

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