@article{carter2025eeg,

title = {EEG Functional Connectivity, Heartrate Synchrony, and Eye Movements Reveal Distinct Components within Narrative Engagement and Immersion},

author = {Felix Carter and Iain D Gilchrist and Danae Stanton-Fraser},

doi = {https://doi.org/10.1162/jocn_a_02338},

year  = {2025},

date = {2025-04-28},

urldate = {2025-01-01},

journal = {Journal of Cognitive Neuroscience},

pages = {1–22},

publisher = {MIT Press 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA~…},

abstract = {Storytelling is a fundamental and universal human behavior, representing a vehicle for cultural information exchange throughout human history. In the present day, consumption of narrative audiovisual media is one of the most common recreational activities worldwide. Despite the importance and ubiquity of storytelling, relatively little is known about the neurocognitive mechanisms by which narrative media capture and sustain our attention. In this study, 40 participants watched 10 short clips from television shows of various genres while electroencephalography, eye tracking, heart rate, and self-report data were recorded. Self-reported immersion and three of the four components of narrative engagement that we examined—attentional focus, emotional engagement, and narrative presence—were associated with interindividual synchrony in heart rate and gaze behavior, but were associated with relatively distinct patterns of neural activity (electroencephalography power amplitude and functional connectivity). Narrative understanding, on the other hand, was not associated with heart rate or gaze synchrony. Furthermore, structural equation modeling revealed directionally opposing relationships between overall alpha-band connectivity and narrative presence on the one hand (positive), and narrative understanding (negative) on the other. These results suggest narrative understanding may be associated with a different set of neurocognitive processes to the other dimensions of narrative engagement. These findings point toward a bifurcated model of narrative engagement and raise interesting theoretical questions about the role of narrative comprehension in this process.



},

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pubstate = {published},

tppubtype = {article}

}

@article{wang2025study,

title = {Study on cognitive impairment evaluation based on photoelectric neural information},

author = {Zehua Wang and Ye Zhang and Ning Ma and Huiting Qiao and Meiyun Xia and Deyu Li},

doi = {https://doi.org/10.1177/25424823251325537},

year  = {2025},

date = {2025-03-21},

urldate = {2025-01-01},

journal = {Journal of Alzheimer's Disease Reports},

volume = {9},

pages = {25424823251325537},

publisher = {SAGE Publications Sage UK: London, England},

abstract = {Background

Whether there is a cognitive load-dependent brain activation pattern in the pre-Alzheimer's disease phase is unknown. Multimodal system provides a powerful technical tool.

Objective

We evaluated brain activity patterns under different cognitive loads in patients with mild cognitive impairment.

Methods

Functional near-infrared spectroscopy signals and electroencephalography signals were acquired from the mild cognitive impairment group (MCI, n = 20) and the healthy control group (HC, n = 24) under four cognitive loads. We analyzed the respective brain activity features and performed correlation analyses.

Results

(1) During the encoding phase, both the left occipital (pcond = 0.05, pgroup < 0.01) and left temporal (pcond = 0.02, pgroup = 0.03) skewness condition effects and between-group effects were significant. (2) As the cognitive load increased, the clustering coefficients and local efficiencies were significantly lower for the HC group. (3) The left occipital and left temporal activation skewness in the MCI group were significantly correlated with left occipital electrical features, whereas the left occipital activation intensity and skewness were significantly correlated with left occipital electrical features in HC group.

Conclusions

The pattern of brain activity in MCI depends on cognitive load. Left occipital and left temporal may be important brain regions for evaluating MCI and need to be focused on in the future.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{calvo2025deep,

title = {Deep Learning-Driven EEG Analysis for Personalized Deep Brain Stimulation Programming in Parkinson's Disease},

author = {Nicolas Calvo Peiro and Mathias Ramm Haugland and Alena Kutuzova and Cosima Graef and Aminata Bocum and Yen Foung Tai and Anastasia Borovykh and Shlomi Haar},

doi = {https://doi.org/10.1101/2025.02.11.25321886},

year  = {2025},

date = {2025-02-13},

urldate = {2025-01-01},

journal = {medRxiv},

pages = {2025–02},

publisher = {Cold Spring Harbor Laboratory Press},

abstract = {Deep Brain Stimulation (DBS) is an invasive procedure used to alleviate motor symptoms in Parkinson’s Disease (PD) patients. While brain activity can be used to optimise DBS parameters, the impact of DBS parameters on brain activity remains unclear. We aimed to identify the cortical neural response to changes in DBS parameters, which are sensitive to the effect of small changes in the stimulation parameters and could be used as neural biomarkers. We recorded in-clinic EEG data from seven hemispheres of PD patients during DBS programming sessions.



Here we developed a siamese adaptation of the EEGNet deep learning architecture and trained it to distinguish whether two short (1-sec-long) segments of brain activity were taken with the same stimulation parameters, or if either the strength or location of the stimulation had changed. 13 independent models were trained independently in each hemisphere for stimulation amplitude or contact, and all achieved high accuracy with an average of 78%. Our models are sensitive to changes in brain activity recorded at the scalp of the patients following changes as small as 0.3mA in the DBS parameters.



Next, we interpreted what our black-box AI models learned with an ablation-based explainability method, that extracts frequency bands learned by the models through a perturbation of the input’s frequency spectrum. We found that fast Narrow-Band Gamma oscillations (60-90Hz), contributed most to the models across all 7 hemispheres.



This work, using a data-driven approach, joins a recent body of evidence suggesting cortical Narrow-Band Gamma activity as the potential range for digital biomarkers for DBS optimization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jang2024enhancing,

title = {Enhancing Student Learning in Virtual Classrooms: Effects of Window View Content and Time of Day},

author = {Dajeong Jang and Han-Jong Kim and Kyungah Choi},

doi = {10.1109/ACCESS.2024.3476982},

year  = {2024},

date = {2024-10-09},

urldate = {2024-01-01},

journal = {IEEE Access},

publisher = {IEEE},

abstract = {As virtual classrooms, traditional physical classroom environments are transformed into flexible virtual environments, allowing customization of environmental elements to enhance student learning. This study explored the effects of window settings in virtual classrooms on learning experiences of students. Utilizing a within-subjects design, we simulated a virtual classroom environment with seven unique window settings and varied its view content (nature vs. urban) and time of day (daytime, sunset, and night). We also simulated a windowless condition. Thirty-five university students participated in the study and performed subjective evaluations and cognitive tasks. Moreover, their physiological responses were recorded using electroencephalogram measurements. The results indicated that environments with windows increased the perception of spaciousness and promoted a state of relaxed alertness, as evidenced by increased fast alpha brainwave activity. In contrast, settings without windows or with urban views increased the sense of presence. Daytime views positively affected valence, motivation, spaciousness, and concentration, whereas nighttime views were the least preferred. No significant differences were observed in cognitive task performance across the different conditions. These findings underscore the necessity of customizing virtual learning environments to meet individual user needs. By allowing students to adjust their virtual environments, educators and space designers can create more flexible and personalized virtual-reality educational spaces, ultimately improving learning outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kamti2023eeg,

title = {Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions},

author = {Mukesh Kumar Kamti and Rauf Iqbal and Pallabjyoti Kakoti},

doi = {https://doi.org/10.1016/j.trf.2023.10.011},

year  = {2023},

date = {2023-10-19},

urldate = {2023-01-01},

journal = {Transportation Research Part F: Traffic Psychology and Behaviour},

volume = {99},

pages = {98–112},

publisher = {Elsevier},

abstract = {Driving in diverse and challenging conditions, including inclement weather, poses potential risks to road safety. While previous studies have primarily focused on examining driver behavior and reactions in different weather and road conditions, there is a lack of research on assessing drivers' mental states during such situations, particularly considering the influence of factors such as road complexity, traffic, demographics, and adverse environmental conditions. This paper aims to address this research gap by evaluating the mental states of drivers across different age groups and driving experience levels through simulated driving scenarios encompassing various environments and traffic conditions. A three-wheeler driving simulator was employed, along with the DSI 7 EEG headset and Q states software, to classify and analyze the drivers' mental states. The findings of this study highlight that young novice drivers exhibit higher fluctuations in mental state compared to their mid and high-experienced counterparts. Furthermore, mid-age drivers face an elevated risk of collision due to frequent changes in mental state and attention. Additionally, it was observed that highly skilled drivers display a transition in attention level and mental state between sessions, shifting from a focused to a relaxed state—an aspect absent in inexperienced drivers. These findings enhance our comprehension of the intricate interaction among drivers' emotional states, age, experience, and driving abilities, consequently opening avenues for tailored interventions and training initiatives focused on improving road safety.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{swerdloffevaluating,

title = {Evaluating the partial contribution of the P3 event-related potential elicited by auditory oddball stimuli during the Stroop task},

author = {Margaret M Swerdloff and Levi J Hargrove},

url = {https://arinex.com.au/EMBC/pdf/full-paper_1084.pdf},

year  = {2023},

date = {2023-07-01},

abstract = {The cognitive load of a precisely timed task, such as the Stroop task, may be measured through the use of eventrelated potentials (ERPs). To determine the time at which cognitive load is at its peak, oddball tones may be applied at various times surrounding a cognitive task. However, we need to determine whether the simultaneous presentation of auditory and visual stimuli would mask a potential change in P3 in an ERP-producing task. If the contribution of the Stroop stimulus is too large, then Stroop ERP with oddball stimuli occurring at different timepoints may not be directly comparable across the various timepoints due to the contribution of the Stroop ERP. The aim of this study was to measure the magnitude of the difference wave between that of simultaneously presented stimuli and that of linearly added stimuli of separate responses. Participants were fitted with a dry-sensor EEG cap and were presented with a series of Stroop and auditory stimuli. For some Stroop stimuli, auditory stimuli occurred simultaneously or in a close time proximity to the Stroop stimuli. We sought to estimate the linear contribution of the ERP from Stroop and oddball stimuli. We found that the magnitude of the difference waves were 3.07 ± 1.65 µV and 2.82 ± 1.34 µV for congruent and incongruent stimuli, respectively. As the average amplitude in the P3 region for both the congruent and incongruent difference waves was lower than the magnitude of the auditory oddball presented simultaneously with Stroop stimuli (12.13 ± 1.00 µV for congruent and 11.78 ± 1.05 µV for incongruent Stroop), we expect that the contribution of P3 auditory oddball would not mask a potential Stroop effect even if the timing of the auditory oddball stimuli were experimentally manipulated, a direction that we hope to explore in future work. In conclusion, we determine this paradigm is suitable for measuring cognitive load in precisely timed tasks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chiossi2023exploring,

title = {Exploring Physiological Correlates of Visual Complexity Adaptation: Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR Adaptive Systems},

author = {Francesco Chiossi and Changkun Ou and Sven Mayer},

url = {https://www.researchgate.net/profile/Francesco-Chiossi/publication/369033584_Exploring_Physiological_Correlates_of_Visual_Complexity_Adaptation_Insights_from_EDA_ECG_and_EEG_Data_for_Adaptation_Evaluation_in_VR_Adaptive_Systems/links/64064d5d0cf1030a567a05fb/Exploring-Physiological-Correlates-of-Visual-Complexity-Adaptation-Insights-from-EDA-ECG-and-EEG-Data-for-Adaptation-Evaluation-in-VR-Adaptive-Systems.pdf},

year  = {2023},

date = {2023-04-23},

urldate = {2023-04-23},

abstract = {Physiologically-adaptive Virtual Reality can drive interactions and adjust virtual content to better fit users’ needs and support specific goals. However, the complexity of psychophysiological inference hinders efficient adaptation as the relationship between cognitive and physiological features rarely show one-to-one correspondence. Therefore, it is necessary to employ multimodal approaches to evaluate the effect of adaptations. In this work, we analyzed a multimodal dataset (EEG, ECG, and EDA) acquired during interaction with a VR-adaptive system that employed EDA as input for adaptation of secondary task difficulty. We evaluated the effect of dynamic adjustments on different physiological features and their correlation. Our results show that when the adaptive system increased the secondary task difficulty, theta, beta, and phasic EDA features increased. Moreover, we found a high correlation between theta, alpha, and beta oscillations during difficulty adjustments. Our results show how specific EEG and EDA features can be employed for evaluating VR adaptive systems.

},

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pubstate = {published},

tppubtype = {article}

}