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.
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}
}
Rustamov, Nabi; Demarest, Phillip; Han, Zhuangyu; Mohamud, Safia; Haroutounian, Simon; Leuthardt, Eric C
Cortical Spectral Dynamics of Vibrotactile Frequency Processing Journal Article
In: Scientific Reports, 2025.
@article{rustamov2025cortical,
title = {Cortical Spectral Dynamics of Vibrotactile Frequency Processing},
author = {Nabi Rustamov and Phillip Demarest and Zhuangyu Han and Safia Mohamud and Simon Haroutounian and Eric C Leuthardt},
doi = {https://doi.org/10.21203/rs.3.rs-6043161/v1},
year = {2025},
date = {2025-02-21},
urldate = {2025-01-01},
journal = {Scientific Reports},
abstract = {While scientific research has extensively explored how the brain integrates touch and pain signals, the cerebral processing of specific vibrotactile frequencies remains poorly understood. This gap is particularly significant given clinical evidence that vibrotactile stimulation can reduce pain in both chronic pain patients and experimental settings. Our study investigated the cortical electrophysiological correlates of peripheral vibrotactile stimulation across different frequencies in healthy volunteers, with a focus on frequency-dependent patterns of neuronal activation. While electroencephalogram (EEG) was recorded, healthy participants received vibrotactile stimulation to the left index fingertip at frequencies corresponding to established neural rhythms: delta (2 Hz), theta (6 Hz), alpha (12 Hz), beta (20 Hz), and gamma (40 Hz). We compared the EEG bandwidth activity between vibrotactile stimulation conditions relative to resting baseline. Our findings demonstrated that vibrotactile stimulation produces distinct frequency-dependent patterns of cortical activation. A key finding was that 6 Hz stimulation selectively enhanced theta power in the left prefrontal cortex - an electrophysiological signature previously linked to successful pain relief. These findings advance the understanding of the "spectrotopic" nature of vibrotactile frequency processing in the cortex and provide a mechanistic foundation for developing novel vibration-based therapies in the future.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Peiro, Nicolas Calvo; Haugland, Mathias Ramm; Kutuzova, Alena; Graef, Cosima; Bocum, Aminata; Tai, Yen Foung; Borovykh, Anastasia; Haar, Shlomi
Deep Learning-Driven EEG Analysis for Personalized Deep Brain Stimulation Programming in Parkinson's Disease Journal Article
In: medRxiv, pp. 2025–02, 2025.
@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}
}
Peters, Betts; Celik, Basak; Gaines, Dylan; Galvin-McLaughlin, Deirdre; Imbiriba, Tales; Kinsella, Michelle; Klee, Daniel; Lawhead, Matthew; Memmott, Tab; Smedemark-Margulies, Niklas; others,
In: Journal of Neural Engineering, 2025.
@article{peters2025rsvp,
title = {RSVP Keyboard with Inquiry Preview: mixed performance and user experience with an adaptive, multimodal typing interface combining EEG and switch input},
author = {Betts Peters and Basak Celik and Dylan Gaines and Deirdre Galvin-McLaughlin and Tales Imbiriba and Michelle Kinsella and Daniel Klee and Matthew Lawhead and Tab Memmott and Niklas Smedemark-Margulies and others},
doi = {10.1088/1741-2552/ada8e0},
year = {2025},
date = {2025-01-10},
urldate = {2025-01-01},
journal = {Journal of Neural Engineering},
abstract = {Objective. The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce Inquiry Preview, a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes. Approach. Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of Inquiry Preview and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of Inquiry Preview and two modes of switch input on typing accuracy and speed during a copy-spelling task. Main results. For participants without disabilities, Inquiry Preview and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience with Inquiry Preview and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with Inquiry Preview and switch input and gave favorable user experience ratings to those conditions, but preferred standard RSVP Keyboard. Significance. Inquiry Preview is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and user experience and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users. ClinicalTrials.gov Identifier: NCT04468919.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Al-Ezzi, Abdulhakim; Astraea, Natalie; Yu, J; Wu, Xiaomeng; Fonteh, Alfred N; Minazad, Yafa; Kloner, Robert A; Arakaki, Xianghong
Brain Functional Connectivity During Stroop Task and CSF Amyloid/tau in Cognitively Healthy Individuals Journal Article
In: Alzheimer's & Dementia, vol. 20, pp. e090859, 2025.
@article{al2024brain,
title = {Brain Functional Connectivity During Stroop Task and CSF Amyloid/tau in Cognitively Healthy Individuals},
author = {Abdulhakim Al-Ezzi and Natalie Astraea and J Yu and Xiaomeng Wu and Alfred N Fonteh and Yafa Minazad and Robert A Kloner and Xianghong Arakaki},
doi = {https://doi.org/10.1002/alz.090859},
year = {2025},
date = {2025-01-09},
urldate = {2024-01-09},
journal = {Alzheimer's & Dementia},
volume = {20},
pages = {e090859},
publisher = {Wiley Online Library},
abstract = {Background
At the pre-clinical stages of Alzheimer’s disease (AD) development, the accumulation of amyloid-β (Aβ) and tau induces neural toxicity, synaptic dysfunction, and excitation/inhibition instability of neural network activity, leading to cognitive decline. However, the effects of Aβ/tau accumulation on electroencephalography (EEG) functional connectivity (FC) in cognitively healthy (CH) individuals during a cognitive challenge have not been elucidated. Therefore, the main objective of this work is to evaluate the association between Aβ/tau level and brain FC during a cognitive challenge in CH individuals.
Method
Established EEG data was recorded using a 21-EEG sensor headset (Wearable Sensing, DSI-24) during a cognitive challenge (Stroop interference test). Amyloid-β and total-tau proteins were measured from cerebrospinal fluid (CSF) using electrochemiluminescence. We assessed FC at sensor level using Partial Directed Coherence (PDC) from EEG data of CH participants including 22 normal CSF Aβ/tau ratio (CH-NAT) and 24 pathological CSF Aβ/tau (CH-PAT). For each trial type (congruent or incongruent) in the Stroop task and each of 19 sensors, linear regression was used to estimate the difference in mean FC between CH-NAT and CH-PAT, while controlling for age, gender, and years of education. The Benjamini–Hochberg procedure was used to control the false discovery rate below q = .1.
Result
Compared with CH-NATs, CH-PATs exhibit higher causal connectivity at specific sensors which each had an estimated local effect size index Cohen’s f2 >0.15, and putatively represent brain regions including medial prefrontal cortex (Fz, Fp1, and F8), central cortex (Cz and C4), and posterior cingulate cortex (Pz). For each of the same sensors, the estimated difference between CH-NAT and CH-PAT had an adjusted P value below .1.
Conclusion
These findings suggest a potential association between the CSF Aβ42/tau pathology and differences between individuals in brain connectivity during a cognitive challenge. These differences may involve compensatory mechanisms as the brain adapts to the amyloid/tau pathology. These results provide potential insights into the neurobiological implications of varying CSF amyloid/tau on brain networks. The areas with estimated medium-to-large effect sizes are favorable to doing a study with a lower significance level to confirm the findings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Tanveer, Aimen; Usman, Syed Muhammad; Khalid, Shehzad; Imran, Ali Shariq; Zubair, Muhammad
Multimodal Consumer Choice Prediction Using EEG Signals and Eye Tracking Journal Article
In: Frontiers in Computational Neuroscience, vol. 18, pp. 1516440, 2025.
@article{tanveer18multimodal,
title = {Multimodal Consumer Choice Prediction Using EEG Signals and Eye Tracking},
author = {Aimen Tanveer and Syed Muhammad Usman and Shehzad Khalid and Ali Shariq Imran and Muhammad Zubair},
doi = {https://doi.org/10.3389/fncom.2024.1516440},
year = {2025},
date = {2025-01-07},
journal = {Frontiers in Computational Neuroscience},
volume = {18},
pages = {1516440},
publisher = {Frontiers},
abstract = {Marketing plays a vital role in the success of a business, driving customer engagement, brand recognition, and revenue growth. Neuromarketing adds depth to this by employing insights into consumer behavior through brain activity and emotional responses to create more effective marketing strategies. Electroencephalogram (EEG) has typically been utilized by researchers for neuromarketing, whereas Eye Tracking (ET) has remained unexplored. To address this gap, we propose a novel multimodal approach to predict consumer choices by integrating EEG and ET data. Noise from EEG signals is mitigated using a bandpass filter, Artifact Subspace Reconstruction (ASR), and Fast Orthogonal Regression for Classification and Estimation (FORCE). Class imbalance is handled by employing the Synthetic Minority Over-sampling Technique (SMOTE). Handcrafted features, including statistical and wavelet features, and automated features from Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), have been extracted and concatenated to generate a feature space representation. For ET data, preprocessing involved interpolation, gaze plots, and SMOTE, followed by feature extraction using LeNet-5 and handcrafted features like fixations and saccades. Multimodal feature space representation was generated by performing feature-level fusion for EEG and ET, which was later fed into a meta-learner-based ensemble classifier with three base classifiers, including Random Forest, Extended Gradient Boosting, and Gradient Boosting, and Random Forest as the meta-classifier, to perform classification between buy vs. not buy. The performance of the proposed approach is evaluated using a variety of performance metrics, including accuracy, precision, recall, and F1 score. Our model demonstrated superior performance compared to competitors by achieving 84.01% accuracy in predicting consumer choices and 83% precision in identifying positive consumer preferences.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sultana, Mushfika; Jain, Osheen; Halder, Sebastian; Matran-Fernandez, Ana; Nawaz, Rab; Scherer, Reinhold; Chavarriaga, Ricardo; del R Millán, José; Perdikis, Serafeim
Evaluating Dry EEG Technology Out of the Lab Conference
2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE 2024.
@conference{sultana2024evaluating,
title = {Evaluating Dry EEG Technology Out of the Lab},
author = {Mushfika Sultana and Osheen Jain and Sebastian Halder and Ana Matran-Fernandez and Rab Nawaz and Reinhold Scherer and Ricardo Chavarriaga and José del R Millán and Serafeim Perdikis},
doi = {10.1109/MetroXRAINE62247.2024.10797021},
year = {2024},
date = {2024-12-24},
urldate = {2024-01-01},
booktitle = {2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {752–757},
organization = {IEEE},
abstract = {Dry electroencephalography (EEG) electrodes have emerged as a promising solution for enhancing the usability of non-invasive Brain-Computer Interface (BCI) systems. Recent advancements in dry helmets have demonstrated competitiveness in enabling EEG and BCI applications with state-of-the-art gel-based systems. In this study, we evaluate the performance and signal quality of a dry EEG cap for BCI applications in a large population. The assessment was conducted under extremely noisy conditions at a public exhibition, surrounded by numerous attendees and multiple sources of electromagnetic interference. Our analysis includes 100 participants from the Mental Work exhibition and assesses the acquired signal's integrity through, Motor Imagery (MI) classification accuracy, the kurtosis of the raw signals and the deviation of the Power Spectral Density (PSD) spectra from the ideal curve. Results show that 56 participants achieved above chance-level single-sample accuracy, with even higher accuracy observed for peak performance. Frontal channels exhibited higher artifact presence, yet the overall signal quality was comparable to gel-based systems used in the authors' previous studies. These findings confirm the potential of dry EEG technology to enable BCI applications beyond the lab, facilitating everyday use in homes, clinics, and public spaces.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Godfrey, Kate; Weiss, Brandon; Zhang, Xinhu; Spriggs, Meg; Peill, Joseph; Lyons, Taylor; Carhart-Harris, Robin; Erritzoe, David
An investigation of acute Physiological and Psychological Moderators of Psychedelic-induced Personality Change among Healthy Volunteers Journal Article
In: Neuroscience Applied, pp. 104092, 2024.
@article{godfrey2024investigation,
title = {An investigation of acute Physiological and Psychological Moderators of Psychedelic-induced Personality Change among Healthy Volunteers},
author = {Kate Godfrey and Brandon Weiss and Xinhu Zhang and Meg Spriggs and Joseph Peill and Taylor Lyons and Robin Carhart-Harris and David Erritzoe},
doi = {https://doi.org/10.1016/j.nsa.2024.104092},
year = {2024},
date = {2024-12-02},
urldate = {2024-01-01},
journal = {Neuroscience Applied},
pages = {104092},
publisher = {Elsevier},
abstract = {This study investigated the effects of a single high-dose of psilocybin on personality traits in psychedelic-naïve healthy volunteers. These data originate from a larger within-subjects fixed-order design trial, where a single high dose of psilocybin (25mg) was administered in a psychologically supportive setting and was compared against a (one-month) prior, 1mg ‘placebo’ dose. Personality shifts were assessed by the Big Five Inventory and the Big Five Aspect Scale, while the Altered States of Consciousness questionnaire (5D-ASC) and the Psychological Insight Scale gauged the acute psychological effects of the substance. Electroencephalography provided neurophysiological insights, specifically examining alpha power and Lempel-Ziv complexity (LZc). Results indicated significant reductions in neuroticism one month after 25mg psilocybin administration, a finding consistent with prior studies. Reductions in neuroticism were moderated by the subjective meaningfulness of the psychedelic experience, as well as by the dread of ego dissolution subscale of the 5D-ASC, suggesting a relationship between acute drug effects and enduring personality alterations. Thus, this study substantiates the role of acute psychedelic states in catalysing lasting personality transformations in a generally beneficial direction, with broader implications for therapeutic applications and understanding of personality dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Busch, Aglaja; Gianotti, Lorena RR; Mayer, Frank; Baur, Heiner
Monitoring Cortical and Neuromuscular Activity: Six-month Insights into Knee Joint Position Sense Following ACL Reconstruction Journal Article
In: International Journal of Sports Physical Therapy, vol. 19, no. 11, pp. 1290, 2024.
@article{busch2024monitoring,
title = {Monitoring Cortical and Neuromuscular Activity: Six-month Insights into Knee Joint Position Sense Following ACL Reconstruction},
author = {Aglaja Busch and Lorena RR Gianotti and Frank Mayer and Heiner Baur},
doi = {https://doi.org/10.26603/001c.124840},
year = {2024},
date = {2024-11-02},
urldate = {2024-01-01},
journal = {International Journal of Sports Physical Therapy},
volume = {19},
number = {11},
pages = {1290},
abstract = {Background
Changes in cortical activation patterns after rupture of the anterior cruciate ligament (ACL) have been described. However, evidence of these consequences in the early stages following the incident and through longitudinal monitoring is scarce. Further insights could prove valuable in informing evidence-based rehabilitation practices.
Purpose
To analyze the angular accuracy, neuromuscular, and cortical activity during a knee joint position sense (JPS) test over the initial six months following ACL reconstruction. Study design: Cohort Study
Methods
Twenty participants with ACL reconstruction performed a JPS test with both limbs. The measurement time points were approximately 1.5, 3-4 and 6 months after surgery, while 20 healthy controls were examined on a single occasion. The active JPS test was performed seated with a target angle of 50° for two blocks of continuous angular reproduction (three minutes per block). The reproduced angles were recorded simultaneously by an electrogoniometer. Neuromuscular activity of the quadriceps muscles during extension to the target angle was measured with surface electromyography. Spectral power for theta, alpha-2, beta-1 and beta-2 frequency bands were determined from electroencephalographic recordings. Linear mixed models were performed with group (ACL or controls), the measurement time point, and respective limb as fixed effect and each grouping per subject combination as random effect with random intercept.
Results
Significantly higher beta-2 power over the frontal region of interest was observed at the first measurement time point in the non-involved limb of the ACL group in comparison to the control group (p = 0.03). Despite individual variation, no other statistically significant differences were identified for JPS error, neuromuscular, or other cortical activity.
Conclusion
Variation in cortical activity between the ACL and control group were present, which is consistent with published results in later stages of rehabilitation. Both indicate the importance of a neuromuscular and neurocognitive focus in the rehabilitation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Please fill out the form and provide a brief description of your application so we can help match you with products that will meet your specific needs.
Please fill out the form and provide a brief description of your application so we can help match you with products that will meet your specific needs.
Please fill out the form and provide a brief description of your application so we can help match you with products that will meet your specific needs.