Directed EEG neural network analysis by LAPPS (p<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1284" altimg="si3.svg"><mml:mo>≤</mml:mo></mml:math>1) Penalized sparse Granger approach

Bore, Joyce Chelangat; Li, Peiyang; Harmah, Dennis Joe; Li, Fali; Yao, Dezhong; Xu, Peng

doi:10.1016/j.neunet.2020.01.022

Cited by 11 publications

(7 citation statements)

References 56 publications

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“…Adding sparse regularizers effectively extracts the important features of the network and reduces the time complexity of the network. In this way, the goal of improving accuracy while reducing the operational requirements can be achieved (Bore et al, 2018(Bore et al, , 2020.…”

Section: Methodsmentioning

confidence: 99%

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Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography

Dongwei

Miao

Deng

et al. 2021

Front. Comput. Neurosci.

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In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L1/2 norm framework for feature extraction, and uses L2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46–21.81%.

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Section: Methodsmentioning

confidence: 99%

“…However, the L 1/2 regularizer is more sparse and robust than LASSO (Xu et al, 2010b;Zong-Ben et al, 2012;Li et al, 2017). Thus, Granger causality analysis based on the L 1/2 norm has been developed, and experiments have proved that this obtains better solutions (Bore et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography

Dongwei

Miao

Deng

et al. 2021

Front. Comput. Neurosci.

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“…EEG recordings are usually fatally contaminated by artifacts or outliers (e.g., eye movement (Brunner et al 2016)) which may lead to network distortion. These algorithms with EEG network analysis can suppress the influence of outliers and capture reliable causal relationship by Lp (0 \ p \ 1)-norm Granger Causality (Lp-GA) (Li et al 2017), least absolute Lp (0 \ p \ 1) penalized sparse Granger (Bore et al 2020), Lp-norm PDC (Li et al 2018c) given the temporal and frequency domain respectively.…”

Section: Granger Causality and Partial Directed Coherencementioning

confidence: 99%

“…3. The methods in these publications ( Bore et al 2020;Li et al 2018c) are derivatives from Lp (Li et al 2017). Hence, we give some technical details here.…”

Section: Granger Causality and Partial Directed Coherencementioning

confidence: 99%

A survey of brain network analysis by electroencephalographic signals

Luo

et al. 2021

Cogn Neurodyn

Self Cite

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Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

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“…Besides, combining EEGs with complex networks to study changes in the brain functions of drivers may provide some objective and effective characteristic indicators and mechanism explanations for driving fatigue. Among them, Granger causality analysis of multivariate time series is widely used in the construction of EEG-based directed networks [ 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals

Qin

Chen

et al. 2022

Entropy

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Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver’s attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain’s information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain’s local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

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Directed EEG neural network analysis by LAPPS (p≤1) Penalized sparse Granger approach

Cited by 11 publications

References 56 publications

Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography

Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography

A survey of brain network analysis by electroencephalographic signals

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals

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