An Overview of EEG-based Machine Learning Methods in Seizure Prediction and Opportunities for Neurologists in this Field

Maimaiti, Buajieerguli; Meng, Hongmei; Y, Lv; Qiu, Jiqing; Zhu, Zhanpeng; Xie, Yinyin; Li, Yue; Yucheng,; Zhao, Weixuan; Liu, Jiayu; Li, Mingyang

doi:10.1016/j.neuroscience.2021.11.017

Cited by 30 publications

(13 citation statements)

References 144 publications

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“…The feature extraction methods mainly include time domain analysis, frequency domain analysis, time frequency domain analysis, multivariate statistical analysis, and nonlinear dynamic analysis (Donos et al, 2018;Rasheed et al, 2020). Principal component analysis, linear discriminant analysis (LDA; Alotaiby et al, 2017), and independent component analysis (Ur et al, 2013;Acharya et al, 2018a;Maimaiti et al, 2021) are widely used unsupervised time-domain methods to summarize EEG data. Frequency domain features include spectral center, coefficient of variation, power spectral density, signal energy, spectral moment, and spectral skewness, which can provide key information about data changes (Yuan et al, 2018;Acharya et al, 2018a).…”

Section: Review Of Eeg Emotion Recognition Techniques 41 Shallow Mach...mentioning

confidence: 99%

Deep learning-based EEG emotion recognition: Current trends and future perspectives

et al. 2023

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Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human–computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions.

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Section: Review Of Eeg Emotion Recognition Techniques 41 Shallow Mach...mentioning

confidence: 99%

Deep learning-based EEG emotion recognition: Current trends and future perspectives

et al. 2023

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“…PILEPSY is a chronic brain disease caused by the sudden abnormal discharge of neurons in the brain resulting in temporary impairment of brain function [1], [2]. The disease affects the normal life of approximately 1% of the world's population, where about 20-30% of patients are drugresistant, known as intractable patients [3], [4]. For these people, it is a feasible scheme to alert them before a coming seizure, which will take care of the self-esteem of patients and avoid the serious consequences caused by a sudden seizure when they go out for activities [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Applicability of Transfer Learning and Feature Engineering in Epilepsy Prediction Using Hybrid Transformer Model

Liu

Yang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…There are four main stages of an epileptic seizure, including preictal, ictal, postictal, and interictal period. Normally, patients are in the interictal state, from which if preictal phase could be identified, an imminent seizure may be avoided by medical treatment ( Usman et al, 2019 ; Maimaiti et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

An end-to-end seizure prediction approach using long short-term memory network

Xiao

Zhou

Zhang

et al. 2023

Front. Hum. Neurosci.

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There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily depends on the artificial experience (or domain knowledge) and is highly subjective. To address these issues, in this paper we propose an end-to-end epileptic seizure prediction approach based on the long short-term memory network (LSTM). In the new method, only the gamma band of raw electroencephalography (EEG) signals is extracted as network input directly for seizure prediction, thus avoiding subjective and expensive feature design process. Despite its simplicity, the proposed method achieves the mean sensitivity of 91.76% and false prediction rate (FPR) of 0.29/h on Children’s Hospital Boston-MIT (CHB-MIT) scalp EEG Database, respectively, when identifying the preictal stage from the EEG signals. Furthermore, different from traditional methods that only consider the classification of preictal and interictal EEG, we introduce the postictal stage as an extra class in the proposed method. As a result, the performance of seizure prediction is further improved, obtaining a higher sensitivity of 92.17% and a low FPR of 0.27/h. The mean warning time is 44.46 min, which suggests that sufficient time is reserved for patients to take intervention measures by this prediction method.

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An Overview of EEG-based Machine Learning Methods in Seizure Prediction and Opportunities for Neurologists in this Field

Cited by 30 publications

References 144 publications

Deep learning-based EEG emotion recognition: Current trends and future perspectives

Deep learning-based EEG emotion recognition: Current trends and future perspectives

Exploring the Applicability of Transfer Learning and Feature Engineering in Epilepsy Prediction Using Hybrid Transformer Model

An end-to-end seizure prediction approach using long short-term memory network

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