Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Kong, Wanzeng; Lin, Wei-Cheng; Babiloni, Fabio; Hu, Sanqing; Borghini, Gianluca

doi:10.3390/s150819181

Cited by 76 publications

(57 citation statements)

References 47 publications

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“…models robust to temporal variability of neural correlates, increased noise, interaction of neural processes. Some promising approaches include the use of novel features based on the connectivity across brain areas [6], [7], [15], [53]- [55] or the covariance across channels [56], deep learning [10], [57], as well as techniques for robust decoder training using limited samples such as transfer learning or semi-supervised approaches [9], [40], [58]- [61]. A recent review on current trends for EEG decoding in BMI applications can be found in reference [62].…”

Section: Discussionmentioning

confidence: 99%

“…Khushaba and colleagues extracted features using a Fuzzy mutual-information based wavelet packet transform on a combination of EEG, electrooculography (EOG) and electrocardiogram (ECG) signals recorded during simulated driving [4]. Alternatively, other researchers have explored whether different levels of fatigue could be discriminated based on interactions among different brain areas estimated using Granger causality [7], transfer entropy [6], or partial directed coherence [15]. Another approach has looked at the stability of the ICA-estimated neural sources [16].…”

Section: A Drowsiness Workload and Emergencymentioning

confidence: 99%

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Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Chavarriaga

Uscumlic

Zhang

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

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Modern cars can support their drivers by assessing and performing autonomously different driving maneuvers, based on information gathered by in-car sensors. We propose that brain machine interfaces (BMIs) can provide complementary information that can ease the interaction with intelligent cars in order to enhance the driving experience. In our approach, the human remains in control, while a BMI is used to monitor the driver's cognitive state and use that information to modulate the assistance provided by the intelligent car. In this review, we gather our proof-of-concept studies demonstrating the feasibility of decoding electroencephalography (EEG) correlates of upcoming actions and those reflecting whether the decisions of driving assistant systems are in-line with the driver intentions. Experimental results while driving both simulated and real cars consistently showed neural signatures of anticipation, movement preparation and error processing. Remarkably, despite the increased noise inherent to real scenarios, these signals can be decoded on a single-trial basis, reflecting some of the cognitive processes that take place while driving. However, moderate decoding performance compared to the controlled experimental BMI paradigms indicate there exists room for improvement of the machine learning methods typically used in the state-ofthe-art BMIs. We foresee that fusion of neural correlates with information extracted from other physiological measures; e.g. eye movements or electromyography (EMG) as well as contextual information gathered by in-car sensors will allow intelligent cars to provide timely and tailored assistance only if it is required; thus keeping the user in the loop and allowing him to fully enjoy the driving experience.

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Section: Discussionmentioning

confidence: 99%

Section: A Drowsiness Workload and Emergencymentioning

confidence: 99%

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Chavarriaga

Uscumlic

Zhang

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

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“…Kaminski and Blinowska noted that the spread of electrical activity becomes smeared by volume conduction, and they obtained clear and reproducible results in their study51. Similarly to many recent reports using EEG to assess brain functional connectivity183038455253545556, we also assessed functional connectivity patterns in scalp regions instead of in the source domain. We emphasize that both the experimental results and the analyses in the present study were restricted to brain-scalp regions.…”

Section: Discussionmentioning

confidence: 74%

Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

Gao

Yang

Huang

et al. 2016

Sci Rep

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To better characterize the cognitive processes and mechanisms that are associated with deception, wavelet coherence was employed to evaluate functional connectivity between different brain regions. Two groups of subjects were evaluated for this purpose: 32 participants were required to either tell the truth or to lie when facing certain stimuli, and their electroencephalogram signals on 12 electrodes were recorded. The experimental results revealed that deceptive responses elicited greater connectivity strength than truthful responses, particularly in the θ band on specific electrode pairs primarily involving connections between the prefrontal/frontal and central regions and between the prefrontal/frontal and left parietal regions. These results indicate that these brain regions play an important role in executing lying responses. Additionally, three time- and frequency-dependent functional connectivity networks were proposed to thoroughly reflect the functional coupling of brain regions that occurs during lying. Furthermore, the wavelet coherence values for the connections shown in the networks were extracted as features for support vector machine training. High classification accuracy suggested that the proposed network effectively characterized differences in functional connectivity between the two groups of subjects over a specific time-frequency area and hence could be a sensitive measurement for identifying deception.

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“…Fatigue driving data. The DRI data were recorded through a fatigue driving experiment (Kong et al, 2015). This experiment included 12 subjects aged between 23 and 25.…”

Section: 14mentioning

confidence: 99%

Personal Identification Based on Brain Networks of EEG Signals

Kong

Jiang

Fan

et al. 2018

International Journal of Applied Mathematics and Computer Science

Self Cite

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Personal identification is particularly important in information security. There are numerous advantages of using electroencephalogram (EEG) signals for personal identification, such as uniqueness and anti-deceptiveness. Currently, many researchers focus on single-dataset personal identification, instead of the cross-dataset. In this paper, we propose a method for cross-dataset personal identification based on a brain network of EEG signals. First, brain functional networks are constructed from the phase synchronization values between EEG channels. Then, some attributes of the brain networks including the degree of a node, the clustering coefficient and global efficiency are computed to form a new feature vector. Lastly, we utilize linear discriminant analysis (LDA) to classify the extracted features for personal identification. The performance of the method is quantitatively evaluated on four datasets involving different cognitive tasks: (i) a four-class motor imagery task dataset in BCI Competition IV (2008), (ii) a two-class motor imagery dataset in the BNCI Horizon 2020 project, (iii) a neuromarketing dataset recorded by our laboratory, (iv) a fatigue driving dataset recorded by our laboratory. Empirical results of this paper show that the average identification accuracy of each data set was higher than 0.95 and the best one achieved was 0.99, indicating a promising application in personal identification. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivs license (http://creativecommons.org/licenses/by-nc-nd/3.0/). segment) were taken as a sample. The average PLV was computed to get an N × N symmetric matrix V .Step 3. The symmetric matrix V was used as the connectivity matrix of brain network to weigh brain network functional connectivity. The diagonal elements of the matrix V were set to zero to avoid self-connection, which was labeled as V . Then the brain functional network was represented as a weighted undirected graph G by the matrix V .Step 4. The attributes including the degree of nodes, the clustering coefficient and the global efficiency of brain network for each sample were computed. A feature vector combined these attributes was used for classification.Step 5. The LDA projection space was trained by training data. Test data were projected to the LDA projection space, and the category of the test sample was determined by the nearest neighbor rule.

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Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Cited by 76 publications

References 47 publications

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

Personal Identification Based on Brain Networks of EEG Signals

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