Rhythm-Dependent Multilayer Brain Network for the Detection of Driving Fatigue

Dang, Weidong; Gao, Zhong-Ke; Lv, Dongmei; Sun, Xinlin; Cheng, Chichao

doi:10.1109/jbhi.2020.3008229

Cited by 26 publications

(13 citation statements)

References 49 publications

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“…The multilayer structure makes it possible to describe complex systems more comprehensively and accurately. Some successful applications of multilayer networks can be found in the elds of chemical systems 24 , EEG signal analysis 25,26 and tra c network analysis 27 .…”

Section: Introductionmentioning

confidence: 99%

Characteristic analysis of epileptic brain network based on attention mechanism

Meng

2022

Preprint

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Constructing an efficient and accurate epilepsy detection system is an urgent research task. In this paper, we developed an EEG-based multi-frequency multilayer brain network (MMBN) and an attentional mechanism based convolutional neural network (AM-CNN) model to study epilepsy detection. Specifically, based on the multi-frequency characteristics and correlation analysis of the brain, we first construct MMBN, where each layer corresponds to a specific frequency band. The time, frequency and channel related information of EEG signals are mapped into the multilayer network topology. On this basis, a multi branch AM-CNN model is designed, which completely matches the multilayer structure of the proposed brain network. The experimental results on public CHB-MIT dataset show that the accuracy of brain state detection is positively correlated with the fineness of frequency band division. When the raw EEG signal is divided into eight frequency bands, this method can accurately detect epilepsy, with an average accuracy of 99.75%, sensitivity of 99.43%, and specificity of 99.83%. All of these provide a reliable technical solution for epilepsy detection.

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Section: Introductionmentioning

confidence: 99%

Characteristic analysis of epileptic brain network based on attention mechanism

Meng

2022

Preprint

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“…For example, Weidong Dang et al. used the Spearman rank correlation to determine the connection among different EEG channels [18]. Wanzeng Kong et al.…”

Section: Introductionmentioning

confidence: 99%

“…However, the aforementioned DL methods do not consider the correlation of multiple channels, and some scholars study the spatial characteristics of multi-channel EEG signals from a network science. For example, Weidong Dang et al used the Spearman rank correlation to determine the connection among different EEG channels [18]. Wanzeng Kong et al developed the functional units (FUs) to show the region of interest for high-density and multidimensional EEG signals [19].…”

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confidence: 99%

Euclidean space data alignment approach for multi‐channel LSTM network in EEG based fatigue driving detection

Tang

Yang

et al. 2021

Electronics Letters

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Fatigue driving is the main reason of traffic accidents. The electroencephalograph (EEG) signal can accurately reflect the changes of human physiological state, so EEG is considered as a reliable method to detect fatigue driving. In this letter, the Euclidean space data alignment approach is adopted to reduce individual differences. Then, to consider the spatial correlation between multiple channels EEG signals and further improve the accuracy, an efficient long short-term memory (LSTM) network structure is designed to detect the fatigue state of drivers. The experimental result shows that the method improves the generalisation capability and achieves the performance with an average accuracy of 95.70% for three categories. Evaluation of the algorithm performance in comparison with other traditional methods indicates that the method has higher accuracy and lower standard deviation. Generally speaking, the proposed method is promising for the application of fatigue driving monitoring.

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“…However, brain is an extremely complex system. Multi-channel EEG signals collected from different brain regions show different features, and are strongly correlated with each other [12][13][14]. The analysis of such correlation between multi-channel EEG signals (i.e., brain-topological characteristics) is helpful for MDD detection, but has been neglected in existing studies.…”

mentioning

confidence: 99%

“…Multilayer structure makes its ability to describe the complex system more comprehensive and accurate. Some successful applications of multilayer network can be found in the fields of chemical system [31], EEG signal analysis [32,33], and social network analysis [34].…”

mentioning

confidence: 99%

Attention-mechanism–based network characteristic analysis for major depressive disorder detection

Yu¹,

Yang²,

Dang³

2022

EPL

Self Cite

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Major depressive disorder (MDD) is a very serious mental illness that spreads all over the world and affects patients of all ages. Constructing an efficient and accurate MDD detection system is an urgent research task. In this paper, we develop an EEG-based multilayer brain network and an attention mechanism-based convolutional neural network (AM-CNN) model to study MDD. In detail, based on mutual information theory, we first construct a multilayer brain network, in which each layer corresponds to a specific frequency band. The experimental results show that such a design can effectively reveal the brain physiological changes of MDD patients, from the perspective of network topology analysis. On this basis, multi-branch AM-CNN model is then designed, which uses multilayer brain network as input and can well achieve feature extraction and detection of MDD. On the publicly available MDD dataset, the proposed method achieves an identification accuracy of 97.22%. Our approach and analysis provide novel insights into the physiological changes of MDD patients and a reliable technical solution for MDD detection.

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Rhythm-Dependent Multilayer Brain Network for the Detection of Driving Fatigue

Cited by 26 publications

References 49 publications

Characteristic analysis of epileptic brain network based on attention mechanism

Characteristic analysis of epileptic brain network based on attention mechanism

Euclidean space data alignment approach for multi‐channel LSTM network in EEG based fatigue driving detection

Attention-mechanism–based network characteristic analysis for major depressive disorder detection

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