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Neurocinematics is an emerging discipline in neuroscience, which aims to provide new filmmaking techniques by analyzing the brain activities of a group of audiences. Several neurocinematics studies attempted to track temporal changes in mental states during movie screening; however, it is still needed to develop efficient and robust electroencephalography (EEG) features for tracking brain states precisely over a long period. This study proposes a novel method for estimating emotional arousal changes in a group of individuals during movie screening by employing steady-state visual evoked potential (SSVEP), which is a widely used EEG response elicited by the presentation of periodic visual stimuli. Previous studies have reported that the emotional arousal of each individual modulates the strength of SSVEP responses. Based on this phenomenon, movie clips were superimposed on a background, eliciting an SSVEP response with a specific frequency. Two emotionally arousing movie clips were presented to six healthy male participants, while EEG signals were recorded from the occipital channels. We then investigated whether the movie scenes that elicited higher SSVEP responses coincided well with those rated as the most impressive scenes by 37 viewers in a separate experimental session. Our results showed that the SSVEP response averaged across six participants could accurately predict the overall impressiveness of each movie, evaluated with a much larger group of individuals.
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http://dx.doi.org/10.3389/fninf.2021.731236 | DOI Listing |
Traffic Inj Prev
September 2025
Chongqing Jianzhu College, Chongqing, P.R. China.
Purpose: The monotonous lighting environment in extra-long tunnels often induces mind-wandering in drivers. To address this issue, this study explores effective strategies to optimize tunnel lighting environments by configuring various background colors and special lighting zones to enhance the alertness of young drivers and ensure driving safety.
Methods: A virtual driving simulator was utilized to carry out the experiment.
Front Neurosci
August 2025
Beijing Life Science Academy, Beijing, China.
Hypocretin, also known as orexin, is a hypothalamic neuropeptide that regulates essential physiological processes including arousal, energy metabolism, feeding behavior, and emotional states. Through widespread projections and two G-protein-coupled receptors-HCRT-1R and HCRT-2R-the hypocretin system exerts diverse modulatory effects across the central nervous system. The role of hypocretin in maintaining wakefulness is well established, particularly in narcolepsy type 1 (NT1), where loss of hypocretin neurons leads to excessive daytime sleepiness and cataplexy.
View Article and Find Full Text PDFPsychophysiology
September 2025
Department of Psychology, Clinical Psychology and Psychotherapy, University of Regensburg, Regensburg, Germany.
Facial emotional expressions are interactive signals that communicate intentions. Previous research has shown that sending a facial emotional expression influences the evaluation of response expressions, but the mechanisms behind this effect remain unclear. In a preregistered experiment, 68 participants were asked to send an emoji (happy, neutral, and angry) to a virtual agent in front of them, whereupon the agent reacted with either a smiling or frowning facial expression.
View Article and Find Full Text PDFJ Neurosci
September 2025
Lendület Laboratory of Thalamus Research, HUN-REN Institute of Experimental Medicine; Budapest, Hungary
The paraventricular thalamic nucleus (PVT) integrates subcortical signals related to arousal, stress, addiction, and anxiety with top-down cortical influences. Increases or decreases in PVT activity exert profound, long-lasting effects on behavior related to motivation, addiction and homeostasis. Yet the sources of its subcortical excitatory and inhibitory afferents, their distribution within the PVT, and their integration with layer-specific cortical inputs remain unclear.
View Article and Find Full Text PDFJ Integr Neurosci
August 2025
School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, Guangdong, China.
Background: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition.
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