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Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.

With the advancements in the research about the EEG signal processing, there are a large number of different analytical methods include the functional brain network (FBN) methods, most of them are unsupervised and need multi handcrafted steps until classification. It is expected to propose a general-purpose method based on deep learning methods to improve the situation. Therefore, in this paper, we proposed two methods based on the convolutional neural network (CNN). One was analyzing adjacent matrixes computed by the phase locking value (PLV) to generate features based on CNN, which was equivalent to the graph-theoretic (GT) indexes functionally. The other method was a novel end-to-end method, brain connection based on CNN (BCCNN), which used a factorized 1-D CNN to filter the temporal part of the raw EEG, computed the correlation coefficients among the electrodes to build a new kind of FBNs and then extracted features from the FBNs like the last method. Then we performed a working memory (WM) experiment to verify the validity of the two methods. Those methods were used to detect the proficiency of the subjects in a WM task. In the results, the accuracy of the first method was 99.33%, which was as good as that of the GT indexes (99.35%). The accuracy of the second methods was 96.53%, which was lower than the performance of the PLV but higher than that of two conventional CNNs (94.37%, 90.83%).

In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue.

The unsafe behaviors of operators in fatigue state not only lead to declines of work efficiency but also higher error rates and more injuries and even deaths. Automated fatigue detection using Electroencephalogram (EEG) due to helping us decline the occurrence probability of related accidents has gained more and more attention in recent years. Hence, designing a suitable feature extraction approach and choosing an efficient classification methodology are considered as the key to successful implementation. We first propose a new minimum spanning tree (MST) feature extraction approach on the basis of the phase coherence (PC) and a power spectrum density (PSD) method, respectively. Then to further improve the detection performance, we perform feature fusion (FBN-PSD-FF), where the functional brain network (FBN)-based feature characterizing the relationship between brain network organization and fatigue and PSD-based feature characterizing the relationship between power variation and fatigue. Furthermore, an automated fatigue detection system has been developed, which is integrated between the novel fusion feature (FBN-PSD-FF) and extreme learning machine (ELM). Finally, a driving simulation experiment is designed and conducted to demonstrate the proposed detection system, and the Karolinska Sleepiness Scale (KSS) and the Stanford Sleepiness Scale (SSS) are employed to validate the results. Experimental results indicate that the proposed method gives a sensitivity of 95.71%, a specificity of 94.29%, an accuracy of 95.00%, and a highest value of area under the receiver operating curve (AUC-ROC = 0.98). The ELM is employed for fatigue detection, reducing the time consuming greatly. Our proposed system can be a viable solution for detecting operators’ fatigue and has great potential to reduce fatigue-related crashes in many circumstances such as navigation, driving, aviation, construction industry, etc.

A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states. The filtered EEG signals are then decomposed into multiple frequency bands by wavelet packet transform. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase lag index (PLI). Based on this, PLI-weighted networks are subsequently calculated, from which minimum spanning trees are constructed-a graph method that corrects for comparison bias. Statistical analyses are performed on graph-derived metrics as well as on the PLI connectivity values. The major finding is that significant differences in the delta frequency band for three graph metrics and in the theta frequency band for five graph metrics suggesting network integration and communication between network nodes are increased from alertness to drowsiness. Together, our findings also suggest a more line-like configuration in alert states and a more star-like topology in drowsy states. Collectively, our findings point to a more proficient configuration in drowsy state for lower frequency bands. Graph metrics relate to the intrinsic organization of functional brain networks, and these graph metrics may provide additional insights on driver drowsiness detection for reducing and preventing traffic accidents and further understanding the neural mechanisms of driver drowsiness.

Objective. The prevalence of acrophobia is high, especially with the rise of many high-rise buildings. In the recent few years, researchers have begun to analyze acrophobia from the neuroscience perspective, especially to improve the virtual reality exposure therapy (VRET). Electroencephalographic (EEG) is an informative neuroimaging technique, but it is rarely used for acrophobia. The purpose of this study is to evaluate the effectiveness of using EEGs to identify the degree of acrophobia objectively. Approach. EEG data were collected by virtual reality (VR) exposure experiments. We classified all subjects’ degrees of acrophobia into three categories, where their questionnaire scores and behavior data showed significant differences. Using synchronization likelihood, we computed the functional connectivity between each pair of channels and then obtained complex networks named functional brain networks (FBNs). Basic topological features and community structure features were extracted from the FBNs. Statistical results demonstrated that FBN features can be used to distinguish different groups of subjects. We trained machine learning (ML) algorithms with FBN features as inputs and trained convolutional neural networks (CNNs) with FBNs directly as inputs. Main results. It turns out that using FBN to identify the severity of acrophobia is feasible. For ML algorithms, the community structure features of some cerebral cortex regions outperform typical topological features of the whole brain, in terms of classification accuracy. The performances of CNN algorithms are better than ML algorithms. The CNN with ResNet performs the best (accuracy reached 98.46 ± 0.42%). Significance. These observations indicate that community structures of certain cerebral cortex regions could be used to identify the degree of acrophobia. The proposed CNN framework can provide objective feedback, which could help build closed-loop VRET portable systems.

This paper proposes a comprehensive approach to explore whether functional brain network (FBN) changes from the alert state to the drowsy state and to find out ideal neurophysiology indicators able to detect driver drowsiness in terms of FBN. A driving simulation experiment consisting of two driving tasks is designed and conducted using fifteen participant drivers. Collected EEG signals are then decomposed into multiple frequency bands by wavelet packet transform (WPT). Based on this, two novel FBN approaches, synchronization likelihood (SL) and minimum spanning tree (MST) are combined and applied to feature recognition and classification system. Unlike other methods, our approaches focus on the interaction and correlation between different brain regions. Statistical analysis of network features indicates that the difference between alert state and drowsy state are significant and further confirmed that brain network configuration should be related to drowsiness. For classification, these brain network features are selected and then fed into four classifiers considered namely Support Vector Machines (SVM), K Nearest Neighbors classifier (KNN), Logistic Regression (LR) and Decision Trees (DT). It is found that combining MST method and SL method is actually increasing the classification accuracy with all classifiers considered in this work especially the KNN classifier from 95.4% to 98.6%. Moreover, KNN classifier also gives the highest precision of 98.3%, sensitivity of 98.8% and specificity of 98.9%. Thus this kind of methodology might be a useful tool for further understanding the neurophysiology mechanisms of driver drowsiness, and as a reference work for future studies or future ‘systems’.

Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operators during their mineral grinding process controlling based on FBN. To save the search time, a novel semi-data-driven method of computing functional brain connection based on stacked autoencoder (BCSAE) is proposed in this paper. This method uses stacked autoencoder (SAE) to encode the multi-channel EEG data into codes and then computes the dissimilarity between the codes from every pair of electrodes to build FBN. The highlight of this method is that the SAE has a multi-layered structure and is semi-supervised, which means it can dig deeper information and generate better features. Then an experiment was performed, the EEG of the operators were collected while they were operating and analyzed to detect their proficiency. The results show that the BCSAE method generated more number of separable features with less redundancy, and the average accuracy of classification (96.18%) is higher than that of the control methods: PLV (92.19%) and PLI (78.39%).

This paper presents the difference in brain networks between the high-proficiency operators and low-proficiency operators while they did the controlling of mineral grinding process. The brain functional connectivity during the controlling task was investigated by means of Granger causality, like partial direct coherence (PDC) based on EEG. In the experiment, the ocular artifacts were removed using Independent Component Analysis (ICA) and sample entropy. The results show that the brain networks of high-proficiency operators (Hps) are more complex and complete than low-proficiency operators' (Lps) (p