EEG microstate correlates of emotion dynamics and stimulation content during video watching.

INTRODUCTION: EEG microstates have been widely adopted to understand the complex and dynamic-changing process in dynamic brain systems, but how microstates are temporally modulated by emotion dynamics is still unclear. An investigation of EEG microstates under video-evoking emotion dynamics modulation would provide a novel insight into the understanding of temporal dynamics of functional brain networks. METHODS: In the present study, we postulate that emotional states dynamically modulate the microstate patterns, and perform an in-depth investigation between EEG microstates and emotion dynamics under a video-watching task. By mapping from subjective-experienced emotion states and objective-presented stimulation content to EEG microstates, we gauge the comprehensive associations among microstates, emotions, and multimedia stimulation. RESULTS: The results show that emotion dynamics could be well revealed by four EEG microstates (MS1, MS2, MS3, and MS4), where MS3 and MS4 are found to be highly correlated to different emotion states (emotion task effect and level effect) and the affective information involved in the multimedia content (visual and audio). CONCLUSION: In this work, we reveal the microstate patterns related to emotion dynamics from sensory and stimulation dimensions, which deepens the understanding of the neural representation under emotion dynamics modulation and will be beneficial for the future study of brain dynamic systems.

Microstate Detection in Naturalistic Electroencephalography Data: A Systematic Comparison of Topographical Clustering Strategies on an Emotional Database.

Electroencephalography (EEG) microstate analysis is a powerful tool to study the spatial and temporal dynamics of human brain activity, through analyzing the quasi-stable states in EEG signals. However, current studies mainly focus on rest-state EEG recordings, microstate analysis for the recording of EEG signals during naturalistic tasks is limited. It remains an open question whether current topographical clustering strategies for rest-state microstate analysis could be directly applied to task-state EEG data under the natural and dynamic conditions and whether stable and reliable results could still be achieved. It is necessary to answer the question and explore whether the topographical clustering strategies would affect the performance of microstate detection in task-state EEG microstate analysis. If it exists differences in microstate detection performance when different topographical clustering strategies are adopted, then we want to know how the alternations of the topographical clustering strategies are associated with the naturalistic task. To answer these questions, we work on a public emotion database using naturalistic and dynamic music videos as the stimulation to evaluate the effects of different topographical clustering strategies for task-state EEG microstate analysis. The performance results are systematically examined and compared in terms of microstate quality, task efficacy, and computational efficiency, and the impact of topographical clustering strategies on microstate analysis for naturalistic task data is discussed. The results reveal that a single-trial-based bottom-up topographical clustering strategy (bottom-up) achieves comparable results with the task-driven-based top-down topographical clustering (top-down). It suggests that, when task information is unknown, the single-trial-based topographical clustering could be a good choice for microstate analysis and neural activity study on naturalistic EEG data.

A Generalized Encoding System for Alpha Oscillations Through Visual Saliency Analysis.

By learning how the brain reacts to external visual stimuli and examining possible triggered brain statuses, we conduct a systematic study on an encoding problem that estimates ongoing EEG dynamics from visual information. A novel generalized system is proposed to encode the alpha oscillations modulated during video viewing by employing the visual saliency involved in the presented natural video stimuli. Focusing on the parietal and occipital lobes, the encoding effects at different alpha frequency bins and brain locations are examined by a real-valued genetic algorithm (GA), and possible links between alpha features and saliency patterns are constructed. The robustness and reliability of the proposed system are demonstrated in a 10-fold cross-validation. The results show that stimuli with different saliency levels can induce significant changes in occipito-parietal alpha oscillations and that alpha at higher frequency bins responded the most in involuntary attention related to bottom-up-based visual processing. This study provides a novel approach to understand the processing of involuntary attention in the brain dynamics and would further be beneficial to the development of brain-computer interfaces and visual design.