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Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems. [ABSTRACT FROM AUTHOR]

• Developed a novel Granger causality inference based on Student's t -distribution. • Quantitatively verified its robustness through both simulation study and real EEG application. • Significantly improved the performance of EEG-based directed brain networks for the recognition of emotions. • Revealed the brain-network-topology differences between various emotional states. • Discovered the lateralization differences of brain networks in emotion processing between genders. Granger-causality-based brain network analysis has been widely applied in EEG-based neuroscience researches and clinical diagnoses, such as motor imagery emotion analysis and seizure prediction. However, how to accurately estimate the causal interactions among multiple brain regions and reveal potential neural mechanisms in a reliable way is still a great challenge, due to the influence of inevitable outliers such as ocular artifacts, which may lead to the deviation of network estimation and the decoding failure of the inherent cognitive states. In this work, by introducing Student's t -distribution into multivariate autoregressive (MVAR) model, we proposed a novel Granger causality analysis to suppress the outliers influence in directed brain network analysis. To quantitatively evaluate the performance of our proposed method, both simulation study and motor imagery EEG experiment were conducted. Through these two quantitative experiments, we verified the robustness of our proposed method to outlier influence when applying it to capture the inherent network patterns. Based on its robustness, we applied it for EEG analysis of emotions and assessed its efficiency in offering discriminative network structures for emotion recognition and discovered the biomarkers for different emotional states. These biomarkers further revealed the network-topology differences between male and female subjects when they experienced different emotional states. In general, our conducted experimental results consistently proved the robustness and efficiency of our proposed method for directed brain network analysis under complex artifact conditions, which may offer reliable evidence for network-based neurocognitive research. [ABSTRACT FROM AUTHOR]

A large-scale network provides a high hierarchical level for understanding the adaptive adjustment of the human brain during cognition processes. Since high spatial resolution is required, most of the related works are based on functional magnetic resonance imaging (fMRI); however, fMRI lacks the temporal information that is important in investigating the high cognition processes. Although combining electroencephalography (EEG) inverse solution and independent component analysis (ICA), researchers detected large-scale functional subnetworks recently, few researchers focus on the unreasonable negative activation, which is biased from the nonnegative electrical source activations in the brain. In this study, considering the favorable nonnegative property of Bayesian nonnegative matrix factorization (Bayesian NMF) and combining EEG source imaging, we developed a robust approach for EEG large-scale network construction and applied it to two independent real EEG datasets (i.e., decision-making and P300). Eight and nine best-fit networks, including such important subnetworks as the somatosensory-motor network (SMN), the default mode network (DMN), etc., were successfully identified for decision-making and P300, respectively. Compared to the networks acquired with ICA, these networks not only lacked confusing negative activations but also showed clear spatial distributions that are compatible with specific brain function. Based on the constructed large-scale network, we further probed that the self-referential network (SRN), the primary visual network (PVN), and the visual network (VN) demonstrated different interaction patterns with other networks between different responses in decision-making. Our results confirm the possibility of probing the neural mechanisms of high cognition processes at a very high temporal and spatial resolution level. • A robust EEG large-scale network construction approach is proposed by Bayesian NMF. • The networks with positive activations and clear spatial distributions are detected. • The different network patterns between the two decision-making responses are revealed. [ABSTRACT FROM AUTHOR]

• A new character-speech materials is proposed to study the audio-visual competition. • Validating Mandarin has a modality advantages in processing audio-visual information. • Clarifying the dominance of auditory processing in Chinese audio-visual competition. Language is a remarkable cognitive ability that can be expressed through visual (written language) or auditory (spoken language) modalities. When visual characters and auditory speech convey conflicting information, individuals may selectively attend to either one of them. However, the dominant modality in such a competing situation and the neural mechanism underlying it are still unclear. Here, we presented participants with Chinese sentences in which the visual characters and auditory speech convey conflicting information, while behavioral and electroencephalographic (EEG) responses were recorded. Results showed a prominent auditory dominance when audio-visual competition occurred. Specifically, higher accuracy (ACC), larger N400 amplitudes and more linkages in the posterior occipital-parietal areas were demonstrated in the auditory mismatch condition compared to that in the visual mismatch condition. Our research illustrates the superiority of the auditory speech over the visual characters, extending our understanding of the neural mechanisms of audio-visual competition in Chinese. [ABSTRACT FROM AUTHOR]