Electroencephalography (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain through electrodes placed on the scalp. EEG can be used for various research applications, including studying brain function and activity, identifying neurological disorders, and investigating the effects of drugs or other interventions on brain activity. EEG is particularly useful for studying brain activity in real-time and identifying the timing and location of brain activity associated with specific cognitive processes or behaviors. It can also be used in clinical settings to diagnose and monitor neurological disorders such as epilepsy, sleep disorders, and traumatic brain injuries. Additionally, EEG can be used to investigate the effects of various interventions, such as cognitive training or neurofeedback, on brain activity and function.
For surfers, catching the perfect wave can induce a state of pure ecstasy known as the “stoke”. But what’s happening in the brain during this ultimate ride? Wearable Sensing created a custom dry EEG system that measures brainwaves during surfing. They partnered with Red Bull to use this technology on professional surfers to uncover the neurophysiological aspects of surfing. The dry EEG system is worn on the head like a swimming cap, and it allows for the measurement of brain activity in real-time during surfing. By studying the brainwaves of surfers during their best rides, researchers hope to understand what goes on in the brain during moments of flow and peak performance, and ultimately unlock the secrets to achieving that elusive state of “stoke”.
In this study, wearable sensors and machine learning-based algorithms were used to predict hypoxia in-flight. The group used Wearable Sensing’s dry-EEG technology to collect sensor data from 85 participants during a two-phase study. Participants wore aviation flight masks, which regulated their oxygen intake while performing cognitive tests and simulated flying tasks. EEG data was collected and analyzed using principal component analysis and machine learning algorithms, including Naïve Bayes, decision tree, random forest, and neural network algorithms, to classify the data as normal or hypoxic. The results showed high sensitivity and specificity, indicating potential for developing a real-time, in-flight hypoxia detection system.
This paper proposes a protocol for assessing stress using wearable sensing technology, including Electroencephalography (EEG), Electrocardiography (ECG), and the Perceived Stress Scale, in combination with a Virtual Reality phobia induction setting. Wearable Sensing’s dry EEG technology is used to measure brain activity and investigate functional brain connectivity associated with stress. The proposed protocol can be expanded with the incorporation of machine learning algorithms for automatic stress level classification.
Maksimenko, Vladimir; Li, Xinwei; Kim, Eui-Jin; Bansal, Prateek
Video-Based experiments better unveil societal biases towards ethical decisions of autonomous vehicles Journal Article
In: Transportation Research Part C: Emerging Technologies, vol. 179, pp. 105284, 2025.
@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}
}
Zazon, Dor; Nissim, Nir
Can your brain signals reveal your romantic emotions? Journal Article
In: Computers in Biology and Medicine, vol. 196, pp. 110754, 2025.
@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}
}
Ahmed, Mohammad Haroon; Panchookian, John; Grillo, Michael; Weerasinghe, Yasith; Taebi, Amirtaha; Qadri, Fadil; Gamage, Peshala; Kaya, Mehmet
Stress Classification Through Simultaneous EEG, Heart Rate Variability, and EMG Monitoring Proceedings Article
In: 2025 IEEE Medical Measurements & Applications (MeMeA), pp. 1–6, IEEE 2025.
@inproceedings{ahmed2025stress,
title = {Stress Classification Through Simultaneous EEG, Heart Rate Variability, and EMG Monitoring},
author = {Mohammad Haroon Ahmed and John Panchookian and Michael Grillo and Yasith Weerasinghe and Amirtaha Taebi and Fadil Qadri and Peshala Gamage and Mehmet Kaya},
doi = {https://doi.org/10.1109/MeMeA65319.2025.11068019},
year = {2025},
date = {2025-07-10},
urldate = {2025-01-01},
booktitle = {2025 IEEE Medical Measurements & Applications (MeMeA)},
pages = {1–6},
organization = {IEEE},
abstract = {Stress has significant effects on health, yet there is limited research on effective methods for quantifying stress detection. Monitoring physiological changes presents a promising approach to stress management. This study compares the effectiveness of electroencephalography (EEG), electrocardiography (ECG)-derived heart rate variability (HRV), and trapezius muscle electromyography (EMG) in stress classification. Sixteen healthy participants (ages 18–46) completed three sessions in a controlled environment. Baseline activity was compared to stress-induced changes during a Stroop color word test and mental arithmetic task. EEG, HRV, and EMG features were analyzed in 30-second intervals to assess their ability to detect stress. EEG features were found to be the most effective, followed by HRV and EMG. Machine learning techniques (SVM, KNN, neural network, and random forest) were applied for subject-specific classification. EEG achieved the highest accuracy (86.45 ± 7.22%), while HRV and EMG yielded similar accuracies (77.36 ± 9.10% and 81.84 ± 6.13%, respectively). When combining HRV and EMG features, an accuracy of 87.51 ± 7.18% was achieved, comparable to EEG. These findings suggest that wearable sensors utilizing EMG and HRV could effectively detect stress without the need for EEG. This approach could open up new avenues for stress management in real-world settings. Future studies with larger sample sizes will work towards developing a universal stress classification model.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Kyrou, Maria; Laskaris, Nikos A; Petrantonakis, Panagiotis C; Kalaganis, Fotis P; Georgiadis, Kostas; Nikolopoulos, Spiros; Kompatsiaris, Ioannis
Decoding Visual Art Preferences from EEG Signals Using Wavelet Scattering Transform Conference
2025 25th International Conference on Digital Signal Processing 2025.
@conference{kyroudecoding,
title = {Decoding Visual Art Preferences from EEG Signals Using Wavelet Scattering Transform},
author = {Maria Kyrou and Nikos A Laskaris and Panagiotis C Petrantonakis and Fotis P Kalaganis and Kostas Georgiadis and Spiros Nikolopoulos and Ioannis Kompatsiaris},
url = {https://2025.ic-dsp.org/wp-content/uploads/2025/05/Decoding-Visual-Art-Preferences-from-EEG-signals-Using-Wavelet-Scattering-Transform.pdf},
year = {2025},
date = {2025-06-25},
organization = {2025 25th International Conference on Digital Signal Processing},
abstract = {Understanding art preferences through neural signals can enhance artistic experiences and provide valuable insights into aesthetic perception. In this study, we propose a novel EEG-based framework for visual art preferences classification, leveraging Wavelet Scattering Transform (WST) for feature extraction and Support Vector Machines (SVM) for classification. Unlike deep learning approaches that require large-scale datasets and extensive training, a wavelet scattering network provides low-variance, translation-invariant features without the need for learnable parameters, making it well-suited for regular size EEG datasets. Experimental results demonstrate that the proposed method effectively differentiates between ”like” and ”dislike” ratings based on EEG responses to visual art stimuli. The findings highlight the potential of wavelet scattering-based feature extraction in decoding aesthetic preferences.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Kalaganis, Fotis P; Georgiadis, Kostas; Nousias, Georgios; Oikonomou, Vangelis P; Laskaris, Nikos A; Nikolopoulos, Spiros; Kompatsiaris, Ioannis
Enhancing EEG-Based Neuromarketing with Attention mechanism and Riemannian Features Conference
2025 25th International Conference on Digital Signal Processing 2025.
@conference{kalaganisenhancing,
title = {Enhancing EEG-Based Neuromarketing with Attention mechanism and Riemannian Features},
author = {Fotis P Kalaganis and Kostas Georgiadis and Georgios Nousias and Vangelis P Oikonomou and Nikos A Laskaris and Spiros Nikolopoulos and Ioannis Kompatsiaris},
url = {https://2025.ic-dsp.org/wp-content/uploads/2025/05/IEEE_DSP_Attention_and_SCMs_for_Neuromarketing_final.pdf},
year = {2025},
date = {2025-06-25},
organization = {2025 25th International Conference on Digital Signal Processing},
abstract = {This paper presents a novel EEG-based decoding framework that integrates Riemannian Geometry features with Deep Learning to enhance neuromarketing classification tasks. The proposed approach leverages Spatial Covariance Matrices (SCMs) computed across multiple frequency bands and employs a self-attention mechanism to improve feature selection and classification performance. To address the challenges of class imbalance and inter-subject variability, Riemannian alignment and data augmentation techniques are incorporated, ensuring robust feature representations. The framework is evaluated on the NeuMa dataset, where participants engaged in a realistic shopping scenario. Experimental results demonstrate that the proposed method achieves a balanced accuracy of 77.80%, outperforming traditional classifiers, including Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Riemannian-based models, and SPDNet-based approaches. These findings highlight the effectiveness of combining functional covariation with deep learning architectures, paving the way for more advanced EEGbased consumer behavior analysis in neuromarketing applications.
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Parra, Sebastian Rueda; Hardesty, Russell Lee; GEMOETS, DARREN Ethan; Hill, Jeremy; Gupta, Disha
Test-Retest Reliability of Kinematic and EEG Low-beta spectral features in a robot-based arm movement task Journal Article
In: Biomedical Physics & Engineering Express, 2025.
@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}
}
Wang, Hongli; Shi, Xiaoning; Wang, Chenyang; Qi, Yongsheng; Gao, Michel; Zhao, Yingying
Cross-frequency coupling between low frequency and gamma oscillations altered in cognitive biotype of depression Journal Article
In: Frontiers in Psychiatry, vol. 16, pp. 1596191, 2025.
@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}
}
Celik, Basak; Memmott, Tab; Stratis, Georgios; Lawhead, Matthew; Peters, Betts; Klee, Daniel; Fried-Oken, Melanie; Erdogmus, Deniz
Multimodal Sensor Fusion for EEG-Based BCI Typing Systems Conference
International Brain-Computer Interface Meeting 2025 2025.
@conference{inproceedings,
title = {Multimodal Sensor Fusion for EEG-Based BCI Typing Systems},
author = {Basak Celik and Tab Memmott and Georgios Stratis and Matthew Lawhead and Betts Peters and Daniel Klee and Melanie Fried-Oken and Deniz Erdogmus},
url = {https://www.researchgate.net/publication/393680550_Multimodal_Sensor_Fusion_for_EEG-Based_BCI_Typing_Systems},
doi = {10.3217/978-3-99161-050-2-196},
year = {2025},
date = {2025-06-02},
urldate = {2025-01-01},
organization = {International Brain-Computer Interface Meeting 2025},
abstract = {For people with severe speech and physical impairments (SSPI), a robust communication interface is often a necessity to improve quality of life. Non-implantable electroencephalography (EEG)-based BCI typing systems are one option in the field to restore communication. In an EEG-based typing interface, a sequence of symbols are presented consecutively on a screen, and the intended symbol is probabilistically inferred by the resulting event-related potentials (ERPs) [1]. Selecting the intended symbol often takes multiple attempts due to a subset of all symbols being presented in each sequence, and a decision cannot be made if the EEG evidence does not strongly support the intended symbol. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Kim, Heegyu; Jun, Sung Chan; Nam, Chang S
Neural Dynamics of Group Interaction in the Iterate Multi-player Prisoner’s Dilemma Game: Multilayer Network Approach Proceedings Article
In: International Conference on Human-Computer Interaction, pp. 18–31, Springer 2025.
@inproceedings{kim2025neural,
title = {Neural Dynamics of Group Interaction in the Iterate Multi-player Prisoner’s Dilemma Game: Multilayer Network Approach},
author = {Heegyu Kim and Sung Chan Jun and Chang S Nam},
url = {https://link.springer.com/chapter/10.1007/978-3-031-93724-8_2},
year = {2025},
date = {2025-05-25},
urldate = {2025-01-01},
booktitle = {International Conference on Human-Computer Interaction},
pages = {18–31},
organization = {Springer},
abstract = {Understanding social interaction from various human behaviors is a complex task. Hyperscanning research tackles this challenge by delving into behavioral mechanisms through a neuroscience lens. While traditional studies focus on inter-brain synchrony in paired functional brain networks, they often lack methods for measuring interactions at the group level. In this study, we propose a multilayer network approach to estimate group brain synchrony and gain deeper insights into the brain’s intricate organization. By utilizing the Prisoner’s Dilemma Game, our goal is to find group interaction processes through distinct behaviors such as cooperation and defection. Thus, the inter-brain synchrony along with differences in network connectivity and structural properties within the functional group network were statistically analyzed between cooperation and defection.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nyffenegger, Daniela; Baur, Heiner; Henle, Philipp; Busch, Aglaja
In: The Knee, vol. 55, pp. 168–178, 2025.
@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}
}
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