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The performance of agents is highly influenced by multiple factors, including ability, decision, and states. Trust modeling is widely used to boost the performance of multiagent teaming (MAT). However, most existing trust models rely on statistical methods or preset parameters to assess the trust value in the MAT scenario. In this article, an adaptive trust model is proposed to evaluate comprehensive trust values based on multiple pieces of evidence from variant sources. The proposed trust model leverages information fusion and RL to properly fuse multiple pieces of evidence to generate trust value for every agent in MAT. The trust value is then used in an interaction protocol with MAT to increase the efficiency of cooperation. To verify the performance of the proposed trust model, a ball-collection experiment is designed for MAT to work cooperatively in simulation environments. Two different scenario settings are used to demonstrate the adaptability and robustness of the proposed trust model. The results are further compared with human-designed fusion methods. The comparison shows that the proposed trust model has a better representation of agent performance, namely convergence speed, than human-designed methods in different scenario settings.

The explainability of reinforcement learning (RL) models has received vast amount of interest as its applications have widened. Most existing explainable RL models focus on improving the explainability of an agent's observations instead of the relationships between agent states and actions. This study presents a fuzzy centered explainable network (FCEN) for RL tasks to interpret the relationships between agent states and actions. The proposed FCEN leverages the interpretability of fuzzy neural networks to establish if-then rules and a generative model to visualize learned knowledge. Precisely, the FCEN includes if-then rules that formulate state-action mappings with human-understandable logic, such as the form 'IF Input is A THEN Output is B.' In addition, these rules connect with a generative model that concretizes the states into human-understandable patterns (figures). Our experimental results obtained on 4 Atari games show that the proposed FCEN can achieve a high level of performance in RL tasks and enormously boost the explainability of RL agents both globally and locally. In other words, the FCEN maintains a high-level explanation for the agent decision logic and the possibility of low-level analysis for each given observation sample. The explainability boost does not undermine reward learning performance, humans can even enhance the agent's performance with the provided explainability.

The adoption of automated vehicles will be a positive step towards road safety and environmental benefits. However, one major challenge that still exist is motion sickness. The move from drivers to passengers who will engage in non-driving related tasks as well as the potential change in the layout of the car interior that will come with automated vehicles are expected to result in a worsened experience of motion sickness. The previous workshop [18] highlighted the need for consensus on guidelines regarding study design for motion sickness research. Hence, this workshop will develop a guide for motion sickness research through reflection and discussions on the current methodologies used by experts in the field. Further it will build on the knowledge collected from the previous workshop and will thereby facilitate not only new research ideas and fruitful collaborations but also find a consensus in the field in regard to study design and methodologies.

Electroencephalograms (EEGs) are brain dynamics measured outside of the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition. However, these approaches severely rely on manually designed network structures for different tasks which normally are not sharing the same empirical design cross-task-wise. In this paper, we propose a cross-task neural architecture search (CTNAS-EEG) framework for EEG signal recognition, which can automatically design the network structure across tasks and improve the recognition accuracy of EEG signals. Specifically, a compatible search space for cross-task searching and an efficient constrained searching method is proposed to overcome challenges brought by EEG signals. By unifying structure search on different EEG tasks, this work is the first to explore and analyze the searched structure difference in cross-task-wise. Moreover, by introducing architecture search, this work is the first to analyze model performance by customizing model structure for each human subject. Detailed experimental results suggest that the proposed CTNAS-EEG could reach state-of-the-art performance on different EEG tasks, such as Motor Imagery (MI) and Emotion recognition. Extensive experiments and detailed analysis are provided as a good reference for follow-up researchers.

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs). With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it realizes translation on raw waves without marker by introducing text-EEG contrastive alignment training, and 2) it alleviates the interference caused by individual differences in EEG waves through an invariant discrete codex with or without markers. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. This work is the first to facilitate the translation of entire EEG signal periods without word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset.

Natural sunlight can reduce the chemicals of emerging concern (CECs) and biological effects from the discharged domestic wastewater. But the aquatic photolysis and biotoxic variations of specific CECs detected in secondary effluent (SE) were not clear. In this study, 29 CECs were detected in the SE, and 13 medium- and high-risk CECs were identified as target chemicals based on their ecological risk assessment. To comprehensively explore the photolysis properties of the identified target chemicals, the direct and self-sensitized photodegradation of the target chemicals, even the indirect photodegradation in the mixture, were investigated and compared with these photodegradation in the SE. Of the 13 target chemicals, only five chemicals (including dichlorvos (DDVP), mefenamic acid (MEF), diphenhydramine hydrochloride (DPH), chlorpyrifos (CPF), and imidacloprid (IMI)) underwent direct and self-sensitized photodegradation processes. The removal of DDVP, MEF, and DPH was attributed to self-sensitized photodegradation, which was mainly mediated by •OH; CPF and IMI primarily relied on direct photodegradation. Synergistic and/or antagonistic actions that occurred in the mixture improved/decreased the rate constants of five photodegradable target chemicals. Meanwhile, the biotoxicities (acute toxicity and genotoxicity) of the target chemicals (including individual chemicals and the mixture) were significantly reduced, which can explain the reduction of biotoxicities from SE. For the two refractory high-risk chemicals, atrazine (ATZ) and carbendazim (MBC), algae-derived intracellular dissolved organic matter (IOM) on ATZ, and IOM and extracellular dissolved organic matter (EOM) on MBC had slightly promotion for their photodegradation; while peroxysulfate, and peroxymonosulfate served as sensitizers were activated by natural sunlight and effectively improved their photodegradation rate, and then reduced their biotoxicities. These findings will promote the development of CECs t

With the development of intelligent technology, autonomous agents are no longer just simple tools; they have gradually become our partners. This paper presents a trust-based human-autonomous teaming (HAT) framework to realize tactical coordination between human and autonomous agents. The proposed trust-based HAT framework consists of human and autonomous trust models, which leverage a fusion mechanism to fuse multiple performance metrics to generate trust values in real-time. To obtain adaptive trust models for a particular task, a reinforcement learning algorithm is used to learn the fusion weights of each performance metric from human and autonomous agents. The adaptive trust models enable the proposed trust-based HAT framework to coordinate actions or decisions of human and autonomous agents based on their trust values. We used a ball-collection task to demonstrate the coordination ability of the proposed framework. Our experimental results show that the proposed framework can improve work efficiency.

Failure to notice salient objects even looking directly at them happens when individuals' attention is preoccupied, known as inattentional blindness (IB). As a form of attentional loss, IB occurrence might cause severe outcomes due to limited cognitive resources. Varied methods have been explored to reduce the IB effect and avoid neglect of critical information. Attenuating attentional loss via aided guidance with different sensory modalities intervention could be a possible way to address this issue. This study investigates how different sensory modalities affect the cognitive performance and IB effect from behaviour and neural changes in the human brain and how could we apply this in attention training for attentional loss improvement. Two experimental sessions were conducted, with a multisensory oddball task designed in virtual reality (VR) as the main task to attract individuals' attention. In session 1, participants responded to the main task without being informed of the unexpected task-irrelevant patterns in the background, while in session 2, they were informed of the unexpected patterns but still attended to the main task. Thus, participants were divided into IB (unaware of the pattern) and Aware (aware of the pattern) groups based on their awareness of patterns in the first session. Our results revealed that this VR-based design successfully induced the IB occurrence, with four out of nine participants reporting being unaware of the unexpected patterns. Further, the multisensory oddball task showed better performance in cross-modal stimuli (visual-auditory, VA) with higher accuracy and shorter reaction time than in uni-modal (A or V) conditions. Interestingly, in session 1, the IB group showed better performance than the Aware group, indicating that the IB group was not distracted during the task since they were unaware of the patterns. These findings supported our aims to explore the impact of different sensory modalities on cognitive performance and prov

This paper investigates the application of using error-related potential (ErrP) based brain-computer interface (BCI) paradigm to control robot movements with implicit commands. ErrP is a neural signal that is automatically evoked when the machine’s behavior deviates from the observer’s expectations. By continuously monitoring the presence of ErrP, the system infers the observer’s reaction toward robot movements and automatically translates them into control commands, allowing the implicit control of robot movements without interfering the observer’s other tasks. However, ErrP-based BCI has a major limitation: the ErrP is evoked after the robot has committed an error, which might be costly or dangerous in contexts such as assembly line or autonomous driving. To address these limitations, we propose a novel robotic design for ErrP-based BCI that allows humans to continuously evaluate the robot’s intentions and intervene earlier, if necessary before the robot commits an error. We evaluate the proposed robotic design and BCI system via an experiment where a ground robot performs a binary target-reaching task. The high classification accuracy (77.57%) demonstrated that the proposed ErrP-based BCI is feasible for human-robot intention communication before the robot commits an error and has the potential to broaden the range of applications for ErrP-based BCIs.

Image caption aims to generate a language description of a given image. The problem can be solved by learning semantic information of visual objects and generating descriptions based on extracted embedding. However, the spatial relationship between visual objects and their static position is not fully explored by existing methods. In this work, we propose a Position-Aware Transformer (PAT) model that extracts both regional and static global visual features and unify both the regional and global by incorporating spatial information aligned to each visual feature. To make a better representation of spatial information and correlation between extracted visual features, we propose and compare three subtle approaches to explore position embedding with spatial relation information explicitly. Moreover, we jointly consider the static global and regional embedding for spatial modeling. Experimental results illustrate that our proposed model achieves competitive performance on the COCO image captioning dataset, where the PAT model could respectively reach 38.7, 28.6, and 58.6 on BLEU-4, METEOR, and ROUGE-L respectively. Extensive experiments suggest that the proposed PAT model could also reach competitive performance on related visual-language tasks including visual question answering (VQA) and multi-modal retrieval. Detailed ablation studies are conducted to report how each part would contribute to the final performance, which could be a good reference for follow-up spatial information representation works.

The global teleoperation and telerobotics market is projected to reach 98.3billion (USD) by 2027. With the benefits of 5G coverage almost anywhere in the world will allow teleoperated robots to be implemented globally via the cloud. Remote Guided Robotics has excellent potential in existing markets, attempting to remove operators from dangerous tasks and providing access to labour in remote areas, where labour is scarce. This research introduces a configuration of technology that reduces the knowledge gap of what is required to successfully achieve high accuracy and realistic control of a robot in real-time. The system implemented in this paper utilises an infrared camera from Leap Motion to capture and track hand gestures and movements, a Kuka 6 DOF robot and software packages to follow the hand movements from the UltraLeap Leap Motion Controller (LMC) in real-time, and an HTC VR headset for a mixed reality experience where two cameras are placed in the robot work-cell to send back a 3D stereoscopic image to allow the operator to have an immersed experience when remotely controlling the robot. The system proved to have a repeatability of approx. ± 10 mm, and an accuracy of 0.1mm. The gripper was able to successfully pick up 86% of the time, with a final test of gripping an item and guiding it through a bandsaw at an average rate of 24.2 seconds. Testings and simulations completed in this paper outline the level of accuracy and repeatability of the remote guide robotic system. This teleoperated robotic system has the ability to protect operators' safety and meet the explosive growth of needs in the various industries.

Robots for physical Human-Robot Collaboration (pHRC) often need to adapt their admittance and how they operate due to several factors. As the admittance of the system becomes variable throughout the workspace, it is not always straightforward for the operator to predict the robot’s behaviour. Previous work demonstrated that cognitive conflicts can be detected during one-dimensional tasks. This work assesses whether cognitive conflicts can also be detected during 2D tasks in pHRC and a classification problem is formulated. Different robot admittance profiles anticipating the stimulus translated into different levels of cognitive conflict. Several commonly used classification algorithms for EEG signals were evaluated to classify different levels of cognitive conflict. Results demonstrate that cognitive conflict level is lower when the admittance smoothly decreases before unexpected events when compared to conditions in which the admittance abruptly decreases before the stimulus. Among the classification algorithms, Convolutional Neural Network has shown the best results to classify different levels of cognitive conflict. Results suggest the feasibility of adaptive approaches for future pHRC control systems that close the loop on users through EEG signals. The detected human cognitive state can also be used to assess and improve the predictability of Human-Robot teams in various pHRC applications.

We demonstrate a two-dimensional monolayer WS efficiently coupled to a dielectric circular Bragg resonator (CBR). The coupling of the WS and CBR leads to the pronounced enhancements in both photoluminescence and second harmonic generation. 2 2

Nature-based solutions of polishing treated effluent are drawing increased attention because of their potential to mitigate ecological risks, and the shallow open water unit (SOWU) process is one of the most promising options. In this study, a pilot experiment was conducted to assess the efficiency of SOWU for effluent quality polishing in a municipal wastewater treatment plant targeting 33 residual emerging contaminants (ECs) that were categorized into six groups, namely pharmaceuticals, sunscreens, bactericides, pesticides, flame retardants and phenols. In the 14-day monitoring period, each of the ECs decayed following a first order reaction, and about 61 % total ECs removal was achieved, indicating the remarkable photolysis effect of the SOWU under natural conditions. The significant removal of ECs, especially the very high removals of pharmaceuticals (76 %) and sunscreens (77 %), was associated with the attenuation of both the acute luminescent bacterial toxicity and genotoxicity. From the linear relationship between the acute toxicity and chromophoric dissolved organic matter and that between genotoxicity and UV absorbance at 254 nm, it was likely that the attenuation of biotoxicities resulted from the decay of chromophoric and aromatic compounds. By analyzing several reactive species, the excellent photoreactivity of the SOWU was further identified.

The long short-term memory (LSTM) network underpins many achievements and breakthroughs especially in natural language processing fields. Essentially, it is endowed with certain memory capabilities to boost its performance. Currently, the volume and speed of big data generation are increasing exponentially, and such data require efficient models to acquire memory augmented knowledge. In this paper, we propose a memory augmented convolutional neural network (MACNN) with utilizing self-organizing maps (SOM) as the memory module. First, we depict the potential challenge about just applying solely a convolutional neural network (CNN) so as to highlight the advantage of augmenting SOM memory for better network generalization. Then, we dissert a corresponding network architecture incorporating memory to instantiate the distributed knowledge representation machanism, which tactically combines both SOM and CNN. Each component of the input vector is connected with a neuron in a two-dimensional lattice. Finally, we test the proposed network on various datasets and the experimental results reveal that MACNN can achieve competitive performance, especially for bioimages datasets. Meanwhile, we further illustrate the learned representations to interpret the SOM behavior and to comprehend the achieved results, which indicates that the proposed memory-incorporating model can exhibit the better performance.

IEEE Multitasking has become omnipresent in daily activities, and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this study, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) datasets collected from sixteen participants over a five-month study in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload condition. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states.

© 2020 IEEE. Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph. We propose a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discriminative sparse PCA and projected clustering with adaptive neighbors. As a result, both global and local data structures, as well as the label information, are used for better dimensionality reduction. Classification experiments on nine high-dimensional datasets validated the effectiveness and robustness of our proposed SDSPCAAN.

Cancer treatment is extremely aggressive and, in addition to causing considerable discomfort, can lead to death. Therefore, identifying aspects related to treatment assertiveness may be efficient for reducing the mortality rate of cancer patients. This paper seeks to identify the prognosis of cancer treatment survival through hybrid techniques based on the autonomous fuzzification process and artificial neural networks. The public dataset on cancer mortality is the source for conducting treatment assertiveness rating tests. The hybrid model had its results compared to other models present in the pattern classification literature with superior accuracy and identification of people likely to survive treatment (90.46%), and the fuzzy rules obtained with the execution of the model corroborate the high assertiveness of the model, even surpassing state of the art for the theme.

© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.

© 2019 Elsevier B.V. Biosignals tend to display manifest intra- and cross-subject variance, which generates numerous challenges for electroencephalograph (EEG) data analysis. For instance, in the context of classification, the discrepancy between EEG data can make the trained model generalising poorly for new test subjects. In this paper, a subject adaptation network (SAN) inspired by the generative adversarial network (GAN) to mitigate different variances is proposed for analysing EEG data. First the challenges faced by traditional approaches employed for EEG signal processing are emphasised. Then the problem is formulated from mathematical perspective to highlight the key points in resolving such discrepancies. Third, the motivation behind and design principle of the SAN are described in an intuitive manner to reflect its suitability for analysing EEG data. Then after depicting the overall architecture of the SAN, several experiments are used to justify the practicality and efficiency of using the proposed model from different perspectives. For instance, an EEG dataset captured during a stereotypical neurophysiological experiment called the VEP oddball task is utilised to demonstrate the performance improvement achieved by running the SAN.

© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. Background: In the past decade, fatigue has been regarded as one of the main factors impairing task performance and increasing behavioral lapses during driving, even leading to fatal car crashes. Although previous studies have explored the impact of acute fatigue through electroencephalography (EEG) signals, it is still unclear how different fatigue levels affect brain–behavior relationships. Methods: A longitudinal study was performed to investigate the brain dynamics and behavioral changes in individuals under different fatigue levels by a sustained attention task. This study used questionnaires in combination with actigraphy, a noninvasive means of monitoring human physiological activity cycles, to conduct longitudinal assessment and tracking of the objective and subjective fatigue levels of recruited participants. In this study, degrees of effectiveness score (fatigue rating) are divided into three levels (normal, reduced, and high risk) by the SAFTE fatigue model. Results: Results showed that those objective and subjective indicators were negatively correlated to behavioral performance. In addition, increased response times were accompanied by increased alpha and theta power in most brain regions, especially the posterior regions. In particular, the theta and alpha power dramatically increased in the high-fatigue (high-risk) group. Additionally, the alpha power of the occipital regions showed an inverted U-shaped change. Conclusion: Our results help to explain the inconsistent findings among existing studies, which considered the effects of only acute fatigue on driving performance while ignoring different levels of resident fatigue, and potentially lead to practical and precise biomathematical models to better predict the performance of human operators.

© 2005-2012 IEEE. Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands.

© 2018, Springer-Verlag London Ltd., part of Springer Nature. Differentiable neural computer (DNC) has demonstrated remarkable capabilities in solving complex problems. In this paper, we propose to stack an enhanced version of differentiable neural computer together to extend its learning capabilities. Firstly, we give an intuitive interpretation of DNC to explain the architectural essence and demonstrate the stacking feasibility by contrasting it with the conventional recurrent neural network. Secondly, the architecture of stacked DNCs is proposed and modified for electroencephalogram (EEG) data analysis. We substitute the original Long Short-Term Memory network controller by a recurrent convolutional network controller and adjust the memory accessing structures for processing EEG topographic data. Thirdly, the practicability of our proposed model is verified by an open-sourced EEG dataset with the highest average accuracy achieved; then after fine-tuning the parameters, we show the minimal mean error obtained on a proprietary EEG dataset. Finally, by analyzing the behavioral characteristics of the trained stacked DNCs model, we highlight the suitableness and potential of utilizing stacked DNCs in EEG signal processing.

© 2018 IEEE. This paper proposes applying recurrent residual network (RRN) for analyzing electroencephalogram (EEG) data captured during a simulated sustained attention driving task. We first address the suitableness of utilizing residual structure as well as adopting recurrent structure for EEG signal processing. Then based on these descriptions a recurrent residual network is tailored and depicted in detail. Thirdly we use an EEG dataset obtained from a sustained-attention experiment for our model justification. By applying the RRN model to the experimental data and via the competitive result achieved, we demonstrate the elegance of the proposed model. At last, we discuss the characteristics of the learned filters and their interpretations from EEG frequency band perspectives.

© 2018 Wang, Jung and Lin. Performing multiple tasks simultaneously usually affects the behavioral performance as compared with executing the single task. Moreover, processing multiple tasks simultaneously often involve more cognitive demands. Two visual tasks, lane-keeping task and mental calculation, were utilized to assess the brain dynamics through 32-channel electroencephalogram (EEG) recorded from 14 participants. A 400-ms stimulus onset asynchrony (SOA) factor was used to induce distinct levels of attentional requirements. In the dual-task conditions, the deteriorated behavior reflected the divided attention and the overlapping brain resources used. The frontal, parietal and occipital components were decomposed by independent component analysis (ICA) algorithm. The event- and response-related theta and alpha oscillations in selected brain regions were investigated first. The increased theta oscillation in frontal component and decreased alpha oscillations in parietal and occipital components reflect the cognitive demands and attentional requirements as executing the designed tasks. Furthermore, time-varying interactive over-additive (O-Add), additive (Add) and under-additive (U-Add) activations were explored and summarized through the comparison between the summation of the elicited spectral perturbations in two single-task conditions and the spectral perturbations in the dual task. Add and U-Add activations were observed while executing the dual tasks. U-Add theta and alpha activations dominated the posterior region in dual-task situations. Our results show that both deteriorated behaviors and interactive brain activations should be comprehensively considered for evaluating workload or attentional interaction precisely.

© 2017 Elsevier B.V. Studies on spatial navigation demonstrate a significant role of the retrosplenial complex (RSC) in the transformation of egocentric and allocentric information into complementary spatial reference frames (SRFs). The tight anatomical connections of the RSC with a wide range of other cortical regions processing spatial information support its vital role within the human navigation network. To better understand how different areas of the navigational network interact, we investigated the dynamic causal interactions of brain regions involved in solving a virtual navigation task. EEG signals were decomposed by independent component analysis (ICA) and subsequently examined for information flow between clusters of independent components (ICs) using direct short-time directed transfer function (sdDTF). The results revealed information flow between the anterior cingulate cortex and the left prefrontal cortex in the theta (4–7 Hz) frequency band and between the prefrontal, motor, parietal, and occipital cortices as well as the RSC in the alpha (8–13 Hz) frequency band. When participants prefered to use distinct reference frames (egocentric vs. allocentric) during navigation was considered, a dominant occipito-parieto–RSC network was identified in allocentric navigators. These results are in line with the assumption that the RSC, parietal, and occipital cortices are involved in transforming egocentric visual-spatial information into an allocentric reference frame. Moreover, the RSC demonstrated the strongest causal flow during changes in orientation, suggesting that this structure directly provides information on heading changes in humans.