A novel approach of decoding EEG four-class motor imagery tasks via scout ESI and CNN

Hou, Yimin; Lu, Zhou; Jia, Shuyue; Lun, Xiangmin

doi:10.1088/1741-2552/ab4af6

Cited by 95 publications

(58 citation statements)

References 58 publications

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“…Although, Hou et al (2019) used multiple electrodes whereas this work used only two electrodes. Hou et al used the Colin27 template brain for Physionet database, the boundary element method (BEM) implemented in the OpenMEEG toolbox for a realistic-geometry head model, and a Morlet wavelet approach utilized for feature extraction.…”

Section: Classification Comparisonmentioning

confidence: 99%

“…Convolutional neural networks (CNNs) have been widely used in MI-EEG classification on account of their ability to learn features from local receptive fields. Because the trained detector can be used to detect abstract features by convolutional layer repetition, CNNs are suitable for complex EEG recognition tasks, and have achieved good results and been widely used by many scholars (Amin et al, 2019 ; Hou et al, 2019 ; Jaoude et al, 2020 ; Zhang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

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A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

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Section: Classification Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

Self Cite

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“…Stochastic gradient descent is optimized by Adam to find optimal parameters of the LSTM-based model. And the L2 norm applied to the Euclidean distance serves as the loss function of the model [18]. Based on the loss function, the network parameters of LSTM are continuously fine-tuning during the iteration process [19].…”

Section: B Offline Training Stagementioning

confidence: 99%

“…The value of the F1-score is within the interval [0, 1]. The higher value of the F1-score, the better performance of the classifier F1-score 2 TPR FPR TPR FPR (18)…”

Section: Tp+tn Tp Fp Fn Tnmentioning

confidence: 99%

Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

Zhang

et al. 2021

IEEE Access

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To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to the unavailability of reliable quantitative criteria. Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics. Finally, the trained assessment model is employed to determine the systems stability status in real time. The test results on the IEEE 39-bus system suggest that the proposed approach manages to assess the stability status of the system accurately and timely. Furthermore, the superiority of the proposed method over traditional shallow learning-based assessment methods has also been proved.

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“…In addition, considering the prevalent imbalance of data distribution in the experimental data, the macro-average F-measure (Macro_F) was also employed to describe the classification effects. Macro_F is more sensitive to the classification quality, and it is defined as follows [50]:…”

Section: Training and Testingmentioning

confidence: 99%

An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks

Wang

Zhang

et al. 2020

Brain Sciences

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In the fields of neuroscience and biomedical signal processing, spike sorting is a crucial step to extract the information of single neurons from extracellular recordings. In this paper, we propose a novel deep learning approach based on one-dimensional convolutional neural networks (1D-CNNs) to implement accurate and robust spike sorting. The results of the simulated data demonstrated that the clustering accuracy in most datasets was greater than 99%, despite the multiple levels of noise and various degrees of overlapped spikes. Moreover, the proposed method performed significantly better than the state-of-the-art method named “WMsorting” and a deep-learning-based multilayer perceptron (MLP) model. In addition, the experimental data recorded from the primary visual cortex of a macaque monkey were used to evaluate the proposed method in a practical application. It was shown that the method could successfully isolate most spikes of different neurons (ranging from two to five) by training the 1D-CNN model with a small number of manually labeled spikes. Considering the above, the deep learning method proposed in this paper is of great advantage for spike sorting with high accuracy and strong robustness. It lays the foundation for application in more challenging works, such as distinguishing overlapped spikes and the simultaneous sorting of multichannel recordings.

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A novel approach of decoding EEG four-class motor imagery tasks via scout ESI and CNN

Cited by 95 publications

References 58 publications

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks

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