Research

@article{maksimenko2025video,

title = {Video-Based experiments better unveil societal biases towards ethical decisions of autonomous vehicles},

author = {Vladimir Maksimenko and Xinwei Li and Eui-Jin Kim and Prateek Bansal},

doi = {https://doi.org/10.1016/j.trc.2025.105284},

year  = {2025},

date = {2025-07-26},

urldate = {2025-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {179},

pages = {105284},

publisher = {Elsevier},

abstract = {Autonomous vehicles (AVs) encounter moral dilemmas when determining whom to sacrifice in unavoidable crashes. To increase the trustworthiness of AVs, policymakers need to understand public judgment on how AVs should act in such ethically complex situations. Previous studies have evaluated public perception about these ethical matters using picture-based surveys and reported societal biases, i.e., systematic variations in ethical decisions based on the socioeconomic characteristics (e.g., gender) of the individuals involved. For instance, females may prioritise saving a female pedestrian in AV-pedestrian incidents. We investigate if these biases stem from personal beliefs or emerge during experiment engagement and if the presentation format affects bias manifestation. Analysing neural responses in moral experiments measured using electroencephalography (EEG) and behaviour model parameters, we find that video-based scenes better unveil societal biases than picture-based scenes. These biases emerge when the subject interacts with experimental information rather than being solely dictated by initial preferences. The findings support the use of realistic video-based scenes in moral experiments. These insights can inform data collection standards to shape socially acceptable ethical AI policies.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zazon2025can,

title = {Can your brain signals reveal your romantic emotions?},

author = {Dor Zazon and Nir Nissim},

doi = {https://doi.org/10.1016/j.compbiomed.2025.110754},

year  = {2025},

date = {2025-07-14},

urldate = {2025-01-01},

journal = {Computers in Biology and Medicine},

volume = {196},

pages = {110754},

publisher = {Elsevier},

abstract = {The process of partner selection may result in emotions of romantic attraction when one expresses interest towards a potential partner, and rejection when one receives negative feedback from a potential partner. Previous EEG studies have found distinct neural correlates for both emotions in the context of dating apps. However, to the best of our knowledge, no study has demonstrated the ability to predict the associated intra-subject romantic emotions based on a single-trial analysis of event related potential (ERP). In this study, 61 participants (31 females and 30 males) agreed to use our simulated dating app, and their EEG brain activity was recorded during their engagement with the app. Based on each participant's EEG signals, we induced multiple machine and deep learning models aimed at predicting single-trial romantic attraction and rejection for each participant. Our results show that the best model obtained 71.38 % and 81.31 % average ROC-AUC scores across the participants respectively for romantic attraction and rejection. We also found that our learning models were able to predict romantic emotions more accurately for picky participants than they could for those that were less fussy, which might suggest that picky people have stronger brain activity signals when it comes to romantic preference.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rueda2025test,

title = {Test-Retest Reliability of Kinematic and EEG Low-beta spectral features in a robot-based arm movement task},

author = {Sebastian Rueda Parra and Russell Lee Hardesty and DARREN Ethan GEMOETS and Jeremy Hill and Disha Gupta},

doi = {https://doi.org/10.1088/2057-1976/ade317},

year  = {2025},

date = {2025-06-10},

urldate = {2025-01-01},

journal = {Biomedical Physics & Engineering Express},

abstract = {Objective: Low-beta (Lβ, 13-20 Hz) power plays a key role in upper-limb motor control and afferent processing, making it a strong candidate for a neurophysiological biomarker. We investigate the test-retest reliability of Lβ power and kinematic features from a robotic task over extended intervals between sessions to assess its potential for tracking longitudinal changes in sensorimotor function.



Approach: We designed and optimized a testing protocol to evaluate Lβ power and kinematic features (maximal and mean speed, reaction time, and movement duration) in ten right-handed healthy individuals that performed a planar center-out task using a robotic device and EEG for data collection. The task was performed with both hands, and the experiment was repeated approximately 40 days later under similar conditions, to resemble real-life intervention periods. We first characterized the selected features within the task context for each session, then assessed intersession agreement, the test-retest reliability (Intraclass Correlation Coefficient, ICC), and established threshold values for meaningful changes in Lβ power using Bland-Altman plots and repeatability coefficients.



Main results: Lβ power showed the expected contralateral reduction during movement preparation and onset. Both Lβ power and kinematic features exhibited good to excellent test-retest reliability (ICC > 0.8), displaying no significant intersession differences. Kinematic results align with prior literature, reinforcing the robustness of these measures in tracking motor performance over time. Changes in Lβ power between sessions exceeding 11.4% for right-arm and 16.5% for left-arm movements reflect meaningful intersession differences.



Significance: This study provides evidence that Lβ power remains stable over extended intersession intervals comparable to rehabilitation timelines. The strong reliability of both Lβ power and kinematic features supports their use in monitoring upper-extremity sensorimotor function longitudinally, with Lβ power emerging as a promising biomarker for tracking therapeutic outcomes, postulating it as a reliable feature for long-term applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{wang2025cross,

title = {Cross-frequency coupling between low frequency and gamma oscillations altered in cognitive biotype of depression},

author = {Hongli Wang and Xiaoning Shi and Chenyang Wang and Yongsheng Qi and Michel Gao and Yingying Zhao},

doi = {https://doi.org/10.3389/fpsyt.2025.1596191},

year  = {2025},

date = {2025-06-09},

urldate = {2025-01-01},

journal = {Frontiers in Psychiatry},

volume = {16},

pages = {1596191},

publisher = {Frontiers Media SA},

abstract = {Aim: The cognitive biotype of depression has been conceptualized as a distinct subtype characterized by unique distinct neural correlates and specific clinical features. Abnormal neural oscillations related to cognitive dysfunction have been extensively studied, with particular attention given to gamma oscillations due to their crucial role in neurocircuit operations, emotional processing, and cognitive functions. Nevertheless, cross-frequency coupling between low frequency and gamma oscillations in the cognitive biotype of depression have yet to be fully elucidated.



Method: The study identified the cognitive biotype in depression by MATRICS Consensus Cognitive Battery (MCCB). We enrolled 141 depressed patients in remission, including 56 identified as cognitive biotype and 85 as the non-cognitive impairment subgroup. Cross-frequency coupling between low frequency and gamma oscillations were analyzed using specific computational methods based on the data collected by Electroencephalogram (EEG). Furthermore, we did correlation analysis to explore the relationship between cross-frequency coupling of neural oscillations with cognitive function in depression.



Results: We found that phase-amplitude coupling (PAC) values decreased in cognitive biotype. Specifically, cross-frequency coupling between theta (Pz: t =-3.512, FDR-corrected p = 0.011), alpha (P3: t =-3.377, FDR-corrected p = 0.009; Pz: t =-3.451, FDR-corrected p = 0.009), beta (P3: t =-3.129, FDR-corrected p = 0.020; Pz: t =-3.333, FDR-corrected p = 0.020) with low gamma decreased at eyes-closed state in cognitive biotype. However, cross-frequency coupling between delta with gamma increased in cognitive biotype (P4: t = 3.314, FDR-corrected p = 0.022) While cross-frequency coupling exhibited no significant differences at eyes-opened state in two subgroups (FDR-corrected p > 0.05). Furthermore, significant correlations between cognitive function and cross-frequency coupling at eyes-closed state were observed.



Conclusion: These results indicated that the cross-frequency coupling between low frequency and gamma occurred in the parietal lobe in cognitive biotype of depression. These results advance the understanding of neurophysiological mechanisms underlying cognitive deficits and highlight potential biomarkers for precision depression.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nyffenegger2025cortical,

title = {Cortical activity during the first 4 months after anterior cruciate ligament reconstruction while performing an active knee joint position sense test: A pilot study},

author = {Daniela Nyffenegger and Heiner Baur and Philipp Henle and Aglaja Busch},

doi = {https://doi.org/10.1016/j.knee.2025.04.017},

year  = {2025},

date = {2025-05-07},

urldate = {2025-01-01},

journal = {The Knee},

volume = {55},

pages = {168–178},

publisher = {Elsevier},

abstract = {Background

Anterior cruciate ligament (ACL) rupture is thought to alter the way in which the brain receives and processes information, affecting body movements. Although alterations in brain activity after ACL rupture have been described, these are limited to time points more than 6 months after rupture. Therefore, this pilot study aims to investigate cortical activity during an active knee joint position sense (JPS) test within the first 4 months after ACL reconstruction.



Methods

Twelve participants with ACL reconstruction (nine males; age 25.3 ± 6.4 years; height 173.6 ± 8.0 cm; mass 71.1 ± 9.1 kg) and 12 matched healthy controls (nine males; age 28.8 ± 9.7 years; height 174.5 ± 9.7 cm; mass 72.7 ± 12.7 kg) performed an active knee JPS test in an open kinetic chain with a starting angle of 90° knee flexion and a target angle of 50°. Absolute angular error was measured with an electrogoniometer. Cortical activity was simultaneously recorded with dry electroencephalography. Participants with ACL reconstruction were measured at 5–8 weeks postoperative (M1) and 12–16 weeks postoperative (M2), the control group once. Power spectra for the frequencies, theta (4.75–6.75 Hz), alpha-1 (7.0–9.5 Hz) and alpha-2 (9.75–12.5 Hz) for frontal, central and parietal regions of interest were calculated.



Results

Participants with ACL reconstruction exhibited significantly higher central theta power during JPS testing with their uninvolved leg at M1 compared with M2 (adjusted P = 0.01; rank epsilon squared = 0.39). No other comparisons yielded statistically significant differences.



Conclusions

The results cautiously support current evidence on cortical alterations following ACL reconstruction. A larger sample size and more measurement time points may provide further insight into possible alterations in the early postoperative period.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}