EEG-Based Seizure detection using linear graph convolution network with focal loss

Zhao, Yanna; Dong, Changxu; Zhang, Gaobo; Wang, Yaru; Chen, Xin; Jia, Weikuan; Yuan, Qi; Xu, Fangzhou; Zheng, Yuanjie

doi:10.1016/j.cmpb.2021.106277

Cited by 62 publications

(20 citation statements)

References 22 publications

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“…Similarly, focal loss is an objective (loss) function that allows the training model to alleviate the impact of data imbalance by focusing on the minority class while reducing the weight of the majority class. Using focal loss has resulted in higher seizure detection [ 135 ] and prediction [ 61 ] performance. Another approach has included signal segmentation and recombination in different domains [ 15 , 136 ].…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.

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Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Nafea

Ismail

2022

Bioengineering

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“…The adjacency matrix W

\in R^{n \times n}

of each mini‐epoch, representing the spatial correlation along with the channel signals

X \in R^{n \times M}

is one of the inputs of the ST‐GCN model shown in Figure 1a. In some studies on brain disorders based on GCN (Chen et al, 2020; Li, Liu, et al, 2021; Zeng et al, 2020; Zhang et al, 2021; Zhao et al, 2021), the relationship of different channels of EEG is short of effective prior guidance and the adjacency matrix cannot ensure the utilisation of the coupling information between each channel. To address these issues, we first apply functional connectivity, which has been proven to be useful in AD classification, to construct the adaptive adjacency matrix to extract spatial coupling features.…”

Section: Methodsmentioning

confidence: 99%

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

Shan

Cao

Huo

et al. 2022

Human Brain Mapping

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Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial-temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG.

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“…This loss function is optimized based on the standard cross-entropy loss function. For the problem of unbalanced samples, the focal loss function can reduce the weight of non-target samples to make the model focus more on the classification of target samples during training [33]. The formula of the focal loss function is as follows：…”

Section: Loss Functionmentioning

confidence: 99%

An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

Zhang

Wang

et al. 2022

Brain Science Advances

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As a new type of brain–computer interface (BCI), the rapid serial visual presentation (RSVP) paradigm has attracted significant attention. The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition. This paper proposed an improved EEGNet model to achieve good performance in offline and online data. Specifically, the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram (EEG) signals. The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples. Additionally, the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model. We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place. The average recall rate of the four participants reached 51.56% in triple classification. In the offline data benchmark dataset (64 subjects-RSVP tasks), the average recall rates of groups A and B reached 76.07% and 78.11%, respectively. We provided an alternative method to identify targets based on the RSVP paradigm.

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EEG-Based Seizure detection using linear graph convolution network with focal loss

Cited by 62 publications

References 22 publications

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

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