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.
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}
}
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}
}
Carter, Felix; Gilchrist, Iain D; Stanton-Fraser, Danae
EEG Functional Connectivity, Heartrate Synchrony, and Eye Movements Reveal Distinct Components within Narrative Engagement and Immersion Journal Article
In: Journal of Cognitive Neuroscience, pp. 1–22, 2025.
@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.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lim, Hyunmi; Ku, Jeonghun
Effects of Augmented Reality (AR) Mirror Therapy Combined with Peripheral Electrical Journal Article
In: Emerging Therapies in Neurorehabilitation III: Proceedings of the 10/11/12th Summer School on Neurorehabilitation (SSNR), held in 2022, 2023, 2024, in Baiona, Spain, vol. 34, pp. 100, 2025.
@article{lim2025effects,
title = {Effects of Augmented Reality (AR) Mirror Therapy Combined with Peripheral Electrical},
author = {Hyunmi Lim and Jeonghun Ku},
url = {https://books.google.com/books?hl=en&lr=&id=fx1TEQAAQBAJ&oi=fnd&pg=PA100&dq=Effects+of+Augmented+Reality+(AR)+Mirror+Therapy+Combined+with+Peripheral+Electrical&ots=jIWVkp4Jhf&sig=9Y7R_WCksKg36QGoCge2JwgHwTU#v=onepage&q=Effects%20of%20Augmented%20Reality%20(AR)%20Mirror%20Therapy%20Combined%20with%20Peripheral%20Electrical&f=false},
year = {2025},
date = {2025-04-05},
urldate = {2025-01-01},
journal = {Emerging Therapies in Neurorehabilitation III: Proceedings of the 10/11/12th Summer School on Neurorehabilitation (SSNR), held in 2022, 2023, 2024, in Baiona, Spain},
volume = {34},
pages = {100},
publisher = {Springer Nature},
abstract = {Abstract. In this study, we investigated the effects of an augmented reality (AR)- based mirror therapy program that combines peripheral electrical stimulation (PES) with mirror visual feedback (MVF) on brain activity. The program was tested on three healthy adults in their 20s. The results showed that the combined feedback of MVF and PES was effective in enhancing motor learning-related brain activity in the mirror-reflected hand. Further research will investigate the effects of different AR feedback factors on brain activity, which will aid in developing optimal rehabilitation protocols. These findings could be applied to rehabilitation therapy for stroke patients, potentially improving the effectiveness of their treatment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wang, Zehua; Zhang, Ye; Ma, Ning; Qiao, Huiting; Xia, Meiyun; Li, Deyu
Study on cognitive impairment evaluation based on photoelectric neural information Journal Article
In: Journal of Alzheimer's Disease Reports, vol. 9, pp. 25424823251325537, 2025.
@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}
}
Gupta, Disha; Brangaccio, Jodi Ann; Mojtabavi, Helia; Carp, Jonathan S; Wolpaw, Jonathan R; Hill, N Jeremy
Frequency dependence of cortical somatosensory evoked response to peripheral nerve stimulation with controlled afferent excitation Journal Article
In: Journal of Neural Engineering, 2025.
@article{gupta2025frequency,
title = {Frequency dependence of cortical somatosensory evoked response to peripheral nerve stimulation with controlled afferent excitation},
author = {Disha Gupta and Jodi Ann Brangaccio and Helia Mojtabavi and Jonathan S Carp and Jonathan R Wolpaw and N Jeremy Hill},
doi = {10.1088/1741-2552/adc204},
year = {2025},
date = {2025-03-18},
urldate = {2025-01-01},
journal = {Journal of Neural Engineering},
abstract = {Objective: H-reflex Targeted Neuroplasticity (HrTNP) protocols comprise a promising rehabilitation approach to improve motor function after brain or spinal injury. In this operant conditioning protocol, concurrent measurement of cortical responses, such as somatosensory evoked potentials (SEPs), would be useful for examining supraspinal involvement and neuroplasticity mechanisms. To date, this potential has not been exploited. However, the stimulation parameters used in the HrTNP protocol deviate from the classically recommended settings for SEP measurements. Most notably, it demands a much longer pulse width, higher stimulation intensity, and lower frequency than traditional SEP settings. In this paper, we report SEP measurements performed within the HrTNP stimulation parameter constraints, specifically characterizing the effect of stimulation frequency. Approach: SEPs were acquired for tibial nerve stimulation at three stimulation frequencies (0.2, 1, and 2 Hz) in 13 subjects while maintaining the afferent volley by controlling the direct soleus muscle response via the Evoked Potential Operant Conditioning System. The amplitude and latency of the short-latency P40 and mid-latency N70 SEP components were measured at the central scalp region using non-invasive electroencephalography. Results: As frequency rose from 0.2Hz, P40 amplitude and latency did not change. In contrast, N70 amplitude decreased significantly (39% decrease at 1 Hz, and 57% decrease at 2Hz), presumably due to gating effects. N70 latency was not affected. Across all three frequencies, N70 amplitude increased significantly with stimulation intensity and correlated with M-wave amplitude. Significance: We assess SEPs within an HrTNP protocol, focusing on P40 and N70, elicited with controlled afferent excitation at three stimulation frequencies. HrTNP conditioning protocols show promise for enhancing motor function after brain and spinal injuries. While SEPs offer valuable insights into supraspinal involvement, the stimulation parameters in HrTNP often differ from standard SEP measurement protocols. We address these deviations and provide recommendations for effectively integrating SEP assessments into HrTNP studies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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