Wearable Sensing’s wireless DSI-Flex is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-Flex takes on average less than 5 minutes to set up, making it the ideal solution for scientists in need of a simple, easy to use, EEG system. Our patented sensor technology not only delivers uncompromised signal quality but also enables our system to be virtually immune against motion and electrical artifacts.
The DSI-Flex has dry sensors on flexible cables, enabling scientists to place the electrodes in varying configurations on the head. These flexible sensors are designed to be screwed into custom caps, so that scientists can order 1 DSI-Flex, and multiple caps, allowing for rapid application of multiple electrode configurations. Every sensor on the DSI-Flex can be customized as either ExG, GSR, TEMP, and REP. It also has a 4-bit trigger input to synchronize with other devices such as Eye-Tracking, Motion (IMU), and more.
Used around the world by leaders in Research, & Brain-Computer Interfaces
With over 90% correlation to research-grade wet EEG systems, the dry sensor interface (DSI) offers unparalleled quality and performance
Multiple adjustment points and a foam pad lined interior enable the system to be worn for up to 8 hours on any head shape or size
All DSI systems include free, unlimited licenses of DSI-Streamer, our data acquisition software which can record raw data, in .csv and .edf file formats
Faraday cage's, spring-loaded electrodes, and our patented common-mode follower technology, provides near immunity against electrical and motion artifacts
Using 70% isopropyl alcohol and a cleaning brush, the DSI-24 only takes a minute to clean, 3 minutes to dry, and can be up and running on the next subject in minutes
All DSI systems include our free C based .dll API, which enables users to pull the raw data directly from the headset, for custom software on Windows, Mac OS, Linux, and ARM
The DSI-Flex was designed for ultra-rapid setup, taking on average less than 5 minutes to don, and works on any type of hair, including long hair, thick hair, afros, and more
DSI headsets have active sensors, amplifiers, digitizers, batteries, onboard storage, and wireless transmission, making them complete, mobile, wearable EEG systems
DSI systems exclusively work with QStates, a machine learning algorithm for cognitive classification on states such as mental workload, engagement, and fatigue
Our Wireless Trigger Hub simplifies the synchronization of DSI headsets with other devices. It features:
An additional benefit of the Trigger Hub design is that it allows synchronization across multiple data sources that are distributed across multiple systems, each of which running at its own clock rate. One such case commonly experienced in EEG experiments involves the synchronization of EEG and eye-tracking measurements, where the inevitable clock drift that arises between two systems during extended measurements creates difficulty in aligning data to events across the two systems.
The DSI-Flex can be customized so that an EEG sensor is replaced with a DSI auxiliary sensor. There are up to 7 locations on the DSI-Flex, enabling any configuration of the following sensors: EEG, ECG, EMG, EOG, GSR, RESP, & TEMP. The sensor data is collected and recorded in our data acquisition software, DSI-Streamer, where you can view the EEG and Aux sensors in real-time.
EEG Channels
Up to 7 Custom Sensor Locations
Reference / Ground
Common Mode Follower / Custom
Head Size Range
Custom Caps
Sampling Rate
300 Hz (600Hz upgrade available)
Bandwidth
0.003 – 150 Hz
A/D resolution
0.317 μV referred to input
Input Impedance (1Hz)
47 GΩ
CMRR
> 120 dB
Amplifier / Digitizer
16 bits / 7 channels
Wireless
Bluetooth
Wireless Range
10 m
Run-time
> 12 hours
Onboard Storage
~ 68 Hours (available option)
Data Acquisition
Real time, evoked potentials
Signal Quality Monitoring
Continuous impedance, Baseline offset, Noise (1-50 Hz)
Data Type
Raw and Filtered Data available
File Type
.CSV and .EDF
Data Output Streaming
TCP/IP socket, API (C Based), LSL
Cognitive State Classification
Brain Computer Interface
SSVEP BCI Algorithms; BCI2000; OpenViBE; PsychoPy; BCILab
Data Integration / Analysis
CAPTIV; Lab Streaming Layer; NeuroPype; BrainStorm; NeuroVIS
Neurofeedback
Applied Neuroscience NeuroGuide; Brainmaster Brain Avatar; EEGer
Neuromarketing
CAPTIV Neurolab
Presentation
Presentation; E-Prime
Yu, Heeseung; Han, Eunkyoung
People see what they want to see: an EEG study Journal Article
In: Cognitive Neurodynamics, pp. 1–15, 2023.
@article{yu2023people,
title = {People see what they want to see: an EEG study},
author = {Heeseung Yu and Eunkyoung Han},
url = {https://link.springer.com/article/10.1007/s11571-023-09982-8},
year = {2023},
date = {2023-06-13},
urldate = {2023-01-01},
journal = {Cognitive Neurodynamics},
pages = {1–15},
publisher = {Springer},
abstract = {This study explored selective exposure and confirmation bias in the choices participants made about which political videos to watch, and whether their political positions changed after they watched videos that either agreed with or opposed their positions on two controversial issues in South Korea: North Korea policy and social welfare policy. The participants completed questionnaires before and after they watched the videos, were asked to select thumbnails of videos before they watched any, and had their brain wave activity measured through electroencephalogram (EEG) as they watched both types of videos. The participants demonstrated selective exposure as they primarily selected video thumbnails with content that matched their political orientations, and they demonstrated confirmation bias as their questionnaire responses after they watched the videos indicated that their positions had hardened. There were also statistically significant differences in alpha, beta, sensory motor rhythm, low beta, mid beta, and fast alpha activity depending on the political orientation consistency between the participants and the videos. Future studies could expand this line of research beyond college students and beyond Asia, and longitudinal work could also be conducted to determine if the obtained patterns remain constant over time.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ferrisi, Leonardo M
Optimizing an assistive Brain Computer Interface that uses Auditory Attention as Input Masters Thesis
2023.
@mastersthesis{ferrisi2023optimizing,
title = {Optimizing an assistive Brain Computer Interface that uses Auditory Attention as Input},
author = {Leonardo M Ferrisi},
url = {https://digitalworks.union.edu/cgi/viewcontent.cgi?article=3754&context=theses},
year = {2023},
date = {2023-06-01},
urldate = {2023-01-01},
abstract = {Brain Computer Interfaces (BCIs) allow individuals to operate technology using (typically consciously controllable) aspects of their brain activity. Auditory BCIs utilize principles of Auditory Event Related
Potentials or Auditory Evoked Potentials as a reproducible controllable features that individuals can use to operate a BCI. These Auditory BCIs in their most basic format can allow users to answer yes or no questions by listening to either one auditory stimuli or the other. Current accuracy in intended response detection for these kinds of BCIs can be as good as mean accuracy of 77 % [5]. BCI research tends to optimize the computer side of the system however the ease of use for the human operating the system is an important point of consideration as well. This research project aimed to determine what factors make a human operator able to achieve the highest accuracy using a given previously successfully demonstrated classifier. This research project primarily sought to answer the questions; to what degree people can improve their accuracy in operating an Auditory BCI and what factors of the stimulus used can be altered to achieve this. The results of this project, obtained through the data collected from six individuals, found that slower stimuli speeds for eliciting Auditory Event Related Potentials were significantly better at achieving higher prediction accuracies compared to faster stimulus speeds. The amount of time spent using the system appeared to result in diminishing returns in accuracy regardless of condition however not before an initial spike in greater classifier prediction accuracy for the second condition run on each subject. Although further research is needed to gain more conclusive evidence for or against the hypothesis, the results of this research may be able suggest that individuals can improve their performance using Auditory BCIs with practice at optimal parameters albeit within a given time frame before experiencing diminishing returns. These findings would stand to provide benefit both to continued research in making optimal non-invasive alternative communication technologies as well as making progress in finding the potential ceiling in accuracy that an Auditory BCI can have in interpreting brain activity for the intended action of the user},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Demarest, Phillip; Rustamov, Nabi; Swift, James; Xie, Tao; Adamek, Markus; Cho, Hohyun; Wilson, Elizabeth; Han, Zhuangyu; Belsten, Alexander; Luczak, Nicholas; others,
A Novel Theta-Controlled Vibrotactile Brain-Computer Interface To Treat Chronic Pain: A Pilot Study Journal Article
In: 2023.
@article{demarest2023novel,
title = {A Novel Theta-Controlled Vibrotactile Brain-Computer Interface To Treat Chronic Pain: A Pilot Study},
author = {Phillip Demarest and Nabi Rustamov and James Swift and Tao Xie and Markus Adamek and Hohyun Cho and Elizabeth Wilson and Zhuangyu Han and Alexander Belsten and Nicholas Luczak and others},
doi = {https://doi.org/10.21203/rs.3.rs-2973437/v1},
year = {2023},
date = {2023-06-01},
urldate = {2023-01-01},
abstract = {Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4–7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity and pain interference scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Maffei, Luigi; Masullo, Massimiliano
Sens i-Lab: a key facility to expand the traditional approaches in experimental acoustics Journal Article
In: Institute of Noise Control Engineering, vol. 266, no. 2, pp. 134–140, 2023.
@article{maffei2023sens,
title = {Sens i-Lab: a key facility to expand the traditional approaches in experimental acoustics},
author = {Luigi Maffei and Massimiliano Masullo},
doi = {https://doi.org/10.3397/NC_2023_0019},
year = {2023},
date = {2023-05-25},
urldate = {2023-01-01},
booktitle = {INTER-NOISE and NOISE-CON Congress and Conference Proceedings},
journal = {Institute of Noise Control Engineering},
volume = {266},
number = {2},
pages = {134–140},
organization = {Institute of Noise Control Engineering},
abstract = {Recent advances in developing new tools and miniaturised devices to measure, analyse, model and simulate existing or future projects are more and more influencing the way to investigate and solve problems of various disciplines fostering deep changes in the research paradigms toward human-centred, multisensory and multidisciplinary approaches. In acoustics, beyond the negative effect of noise on individuals and its mitigation, researchers are even more interested in investigating how the complexity of the multisensory environment modulates the individuals' holistic experience. To this aim, the Department of the Università degli Studi della Campania "Luigi Vanvitelli" has built the Sens i-Lab, a key facility integrating, in a single test room, the simulation and control of the physical environment (acoustics, vision, lighting, microclimate, IAQ) with advanced systems for simulation of virtual environments. To complement the simulation and control of the stimuli, the Sens i-Lab is equipped with a set of systems and devices for motion tracking, for the measurement of the biofeedback signals (EEG, EDA, HRV, VAF) and their association with environmental stimuli and self-reported psychological measures of people well-being. Taking advantage of the Sens i-Lab setting, new research fields and applications in acoustics are possible. Some of them are presented.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lim, Hyunmi; Jeong, Chang Hyeon; Kang, Youn Joo; Ku, Jeonghun
Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients Journal Article
In: Cyberpsychology, Behavior, and Social Networking, 2023.
@article{lim2023attentional,
title = {Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients},
author = {Hyunmi Lim and Chang Hyeon Jeong and Youn Joo Kang and Jeonghun Ku},
doi = {https://doi.org/10.1089/cyber.2022.0176},
year = {2023},
date = {2023-04-20},
urldate = {2023-01-01},
journal = {Cyberpsychology, Behavior, and Social Networking},
publisher = {Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…},
abstract = {Brain–computer interface (BCI) is a promising technique that enables patients' interaction with computers or machines by analyzing specific brain signal patterns and provides patients with brain state-dependent feedback to assist in their rehabilitation. Action observation (AO) and peripheral electrical stimulation (PES) are conventional methods used to enhance rehabilitation outcomes by promoting neural plasticity. In this study, we assessed the effects of attentional state-dependent feedback in the combined application of BCI-AO with PES on sensorimotor cortical activation in patients after stroke. Our approach involved showing the participants a video with repetitive grasping actions under four different tasks. A mu band suppression (8–13 Hz) corresponding to each task was computed. A topographical representation showed that mu suppression of the dominant (healthy) and affected hemispheres (stroke) gradually became prominent during the tasks. There were significant differences in mu suppression in the affected motor and frontal cortices of the stroke patients. The involvement of both frontal and motor cortices became prominent in the BCI-AO+triggered PES task, in which feedback was given to the patients according to their attentive watching. Our findings suggest that synchronous stimulation according to patient attention is important for neurorehabilitation of stroke patients, which can be achieved with the combination of BCI-AO feedback with PES. BCI-AO feedback combined with PES could be effective in facilitating sensorimotor cortical activation in the affected hemispheres of stroke patients.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Seo, Seoung Won; Kim, Yong Seong
Stroke Patients: Effects of Combining Sitting Table Tennis Exercise with Neurological Physical Therapy on Brain Waves Journal Article
In: The Journal of Korean Physical Therapy, vol. 35, no. 1, pp. 19–23, 2023.
@article{seo2023stroke,
title = {Stroke Patients: Effects of Combining Sitting Table Tennis Exercise with Neurological Physical Therapy on Brain Waves},
author = {Seoung Won Seo and Yong Seong Kim},
doi = {https://doi.org/10.18857/jkpt.2023.35.1.19},
year = {2023},
date = {2023-02-28},
urldate = {2023-01-01},
journal = {The Journal of Korean Physical Therapy},
volume = {35},
number = {1},
pages = {19--23},
publisher = {The Korea Society of Physical Therapy},
abstract = {Purpose: The purpose of this study is to analyze the brain waves and develop various exercise programs to improve the physical and mental aspects of stroke patients when neurological physical therapy and sitting table tennis exercise are applied to stroke patients.
Methods: In this study, an experiment was conducted on 15 patients diagnosed with stroke, and training was performed after changing the ping-pong table to a sitting position to apply ping-pong exercise to stroke patients. After training was conducted for 40 minutes twice a week for 4 weeks, brain waves were measured before and after. EEG was measured using Laxtha’s DSI-24 equipment as a measurement tool, and data values were extracted through the Telescan program.
Results: Most of the relative beta waves showed a significant difference before and after the intervention. As for the characteristics of beta waves, this result can be seen as being highly activated during exercise or other activities.
Conclusion: Ping-pong exercise in a sitting position is a good intervention method for stroke patients, and it can help to use it as basic data in clinical practice by showing brain activity.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Chen, Sheng; Xie, Haiqun; Yang, Hongjun; Fan, Chenchen; Hou, Zengguang; Zhang, Chutian
A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments Journal Article
In: Intelligent Robotics: Third China Annual Conference, CCF CIRAC 2022, pp. 349–361, 2023.
@article{chen2023classification,
title = {A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments},
author = {Sheng Chen and Haiqun Xie and Hongjun Yang and Chenchen Fan and Zengguang Hou and Chutian Zhang},
url = {https://link.springer.com/chapter/10.1007/978-981-99-0301-6_27},
year = {2023},
date = {2023-02-18},
urldate = {2023-01-01},
booktitle = {Intelligent Robotics: Third China Annual Conference, CCF CIRAC 2022, Xi’an, China, December 16--18, 2022, Proceedings},
journal = {Intelligent Robotics: Third China Annual Conference, CCF CIRAC 2022},
pages = {349--361},
organization = {Springer},
abstract = {Mild cognitive impairment (MCI) is the preliminary stage of dementia, and has a high risk of progression to Alzheimer’s disease (AD) in the elderly. Early detection of MCI plays a vital role in preventing progression of AD. Clinical diagnosis of MCI requires many examinations, which are highly demanding on hospital equipment and expensive for patients. Electroencephalography (EEG) offers a non-invasive and less expensive way to diagnose MCI early. In this paper, we propose a multi-modal fusion classification framework for MCI detection. We collect EEG data using a delayed match-to-sample task and analyze the differences between the two groups. Based on analysis results, we extract Power spectral density (PSD), PSD enhanced, Event-related potential (ERP) features in EEG signal along with physiological features and behavioral features of the subjects to classify MCI and healthy elderly. By comparing the effect of different features on classification performance, we find that the time-domain based ERP features are better than the frequency-domain based PSD or PSD enhanced features to overcome inter-individual differences to distinguish MCI, and these two features have good complementarity, fusing ERP and PSD enhanced features can greatly improve the classification accuracy to 84.74%. The final result shows that MCI and healthy elderly can be well classified by using this framework.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kambhamettu, Sudhendra; Cruz, Meenalosini Vimal; Anitha, S; Chakkaravarthy, S Sibi; Kumar, K Nandeesh
Brain-Computer Interface-Assisted Automated Wheelchair Control Management--Cerebro: A BCI Application Journal Article
In: Brain-Computer Interface: Using Deep Learning Applications, pp. 205–229, 2023.
@article{kambhamettu2023brain,
title = {Brain-Computer Interface-Assisted Automated Wheelchair Control Management--Cerebro: A BCI Application},
author = {Sudhendra Kambhamettu and Meenalosini Vimal Cruz and S Anitha and S Sibi Chakkaravarthy and K Nandeesh Kumar},
doi = {https://doi.org/10.1002/9781119857655.ch9},
year = {2023},
date = {2023-02-10},
urldate = {2023-01-01},
journal = {Brain-Computer Interface: Using Deep Learning Applications},
pages = {205--229},
publisher = {Wiley Online Library},
abstract = {Technology today serves millions of people suffering from mobility impairments across the globe in numerous ways. Although advancements in medicine and healthcare systems improve the life expectancy of the general population, sophisticated engineering techniques and computing processes have long facilitated the patient in the recovery process. People struggling with mobility impairments and especially spine injuries which also leads to loss of speech, often have a narrow group of devices to aid them move from place-to-place and they are often limited to just movement functionality. BCI (Brain Computer Interface) powered wheelchairs leverage the power of the brain, i.e. translating the thoughts/neural activity into real-world movement providing automated motion without any third party intervention. Many BCI powered wheelchairs in the market are cumbersome to operate and provide only singular functionality of movement. To address this problem and improve the state of BCI products, Cerebro introduces the first ever go-to market product utilizing Artificial Intelligence to facilitate mobility features with built-in speech functionality via blink detection. Further sections of the Chapter take an in-depth look into each layer of the Cerebro system.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dhaliwal, BS; Haddad, J; Debrincat, M; others,
In: Correspondence: Peter Hurwitz, Clarity Science LLC, vol. 750, 2022.
@article{dhaliwal2022changes,
title = {Changes in Electroencephalogram (EEG) After Foot Stimulation with Embedded Haptic Vibrotactile Trigger Technology: Neuromatrix and Pain Modulation Considerations. Anesth Pain Res. 2022; 6 (2): 1-11},
author = {BS Dhaliwal and J Haddad and M Debrincat and others},
url = {https://www.scivisionpub.com/pdfs/improvement-in-balance-and-stability-using-a-novel-sensory-application-haptic-vibrotactile-trigger-technology-2537.pdf},
year = {2022},
date = {2022-12-16},
urldate = {2022-01-01},
journal = {Correspondence: Peter Hurwitz, Clarity Science LLC},
volume = {750},
abstract = {Background: Globally, pain and pain-related diseases are the leading causes of disability and disease burden. In the United States, pain is the most common reason patients consult primary care providers. An estimated 100 million people live with chronic or recurrent pain. Existing pharmacological treatments for pain include anti-inflammatory agents, opioids, and other oral and topical analgesics. Many of these have been associated with troublesome and potentially harmful adverse effects. Understanding the complex pain neuromatrix may help in identifying alternative, non-invasive strategies and treatment approaches to address pain severity, interference, and improve patient outcomes. The neuromatrix of pain is a network of neuronal pathways and circuits responding to sensory (nociceptive) stimulation. Research has suggested that the output patterns of the body-self neuromatrix are responsible for causing or triggering perceptual, homeostatic, and behavioral programs following traumatic injury, other pathology, or chronic stress. As such, pain can be considered a product of the output of a widely distributed neural network within the brain instead of a sequential result of sensory inputs triggered by injury, inflammation, or other pathology. For over a century, the Brodmann Areas remain the most widely known and frequently cited cytoarchitectural organization of the human cortex. Certain Brodmann areas of the brain have been associated with the current understanding of the neuromatrix of pain. The areas expands well beyond the thalamus and anterior cingulate, and primary (S1) and secondary (S2) somatosensory cortices to include the midbrain region of the periaqueductal gray (PAG) and the lenticular complex as well as the insula, orbitofrontal (Brodmann's area [BA] 11, 47), prefrontal (BA 9, 10, 44-46), motor (BA 6, Supplementary motor area, and M1), inferior parietal (BA 39, 40), and anterior cingulate (BA 24, 25) cortices (ACCs). Treatments that are non-invasive and non-pharmacological and target both central and peripheral nociceptive mechanisms that are identified as having an impact on the Brodmann areas associated with the neuromatrix of pain may potentially be considered a beneficial pain management option for patients. Haptic vibrotactile trigger technology targets the nociceptive pathways and is theorized to disrupt the neuromatrix of pain. The technology has been incorporated into non-pharmacological patches and other non-invasive routes of delivery such as apparel (socks), braces, wristbands, and compression sleeves. The purpose of this minimal risk study was to compare electroencephalogram (EEG) patterns in areas of the brain that have been associated with the neuromatrix for pain in subjects wearing socks that were embedded with haptic vibrotactile trigger technology with those patients that wore socks that were not embedded with the technology.
Methods: This IRB-approved study compared electroencephalogram (EEG) patterns in subjects wearing cloth socks embedded with haptic vibrotactile trigger technology (Superneuro VTT Enhanced Socks (Srysty Holding Co., Toronto, Canada) with those patients that wore cloth socks that were not embedded with the technology. Baseline EEG data from 19 scalp locations were recorded in sixty (60) adult subjects (36 females and 24 males) ranging from ages 14 to 83 wearing standard store-purchased cloth socks on their feet. The subject’s standard socks were then removed and replaced with the Superneuro VTT enhanced socks on the subject’s feet. A second EEG recording was then obtained. Both eyes-closed and eyes-open data were recorded.
Results: The results showed statistically significant t-test differences (P < .01) in 59 out of 60 subjects in absolute power and 60 out of 60 subjects showed statistically significant differences in coherence and phase difference. The largest differences were in the alpha1 and beta2 frequency bands and especially in central scalp locations. Paired t-tests of LORETA current source densities between socks on and socks off demonstrated statistically significant differences in 60 out of 60 subjects. The largest effects of Superneuro VTT enhanced socks on were on the medial bank of the somatosensory cortex as well as in the left frontal lobes in the theta and alpha frequency.
Conclusions: Study results indicate that foot stimulation with embedded haptic vibrotactile trigger technology showed significant modulation in the Brodmann areas that have been shown to be associated with the neuromatrix for pain in the human brain. Further research is suggested to evaluate if this technology has a positive impact on pain severity, pain interference, and quality of life and to be considered as a potentially beneficial pain management strategy and as part of a multi-modal treatment approach. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ocay, Don Daniel; Teel, Elizabeth F; Luo, Owen D; Savignac, Chloé; Mahdid, Yacine; Blain-Moraes, Stefanie; Ferland, Catherine E
Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain Journal Article
In: PAIN Reports, vol. 7, no. 6, pp. e1054, 2022.
@article{ocay2022electroencephalographic,
title = {Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain},
author = {Don Daniel Ocay and Elizabeth F Teel and Owen D Luo and Chloé Savignac and Yacine Mahdid and Stefanie Blain-Moraes and Catherine E Ferland},
url = {https://journals.lww.com/painrpts/Fulltext/2022/12000/Electroencephalographic_characteristics_of.17.aspx},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
journal = {PAIN Reports},
volume = {7},
number = {6},
pages = {e1054},
publisher = {LWW},
abstract = {Introduction:
The pathophysiology of pediatric musculoskeletal (MSK) pain is unclear, contributing to persistent challenges to its management.
Objectives:
This study hypothesizes that children and adolescents with chronic MSK pain (CPs) will show differences in electroencephalography (EEG) features at rest and during thermal pain modalities when compared with age-matched controls.
Methods:
One hundred forty-two CP patients and 45 age-matched healthy controls (HCs) underwent a standardized thermal tonic heat and cold stimulations, while a 21-electrode headset collected EEG data. Cohorts were compared with respect to their EEG features of spectral power, peak frequency, permutation entropy, weight phase-lag index, directed phase-lag index, and node degree at 4 frequency bands, namely, delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz), at rest and during the thermal conditions.
Results:
At rest, CPs showed increased global delta (P = 0.0493) and beta (P = 0.0002) power in comparison with HCs. These findings provide further impetus for the investigation and prevention of long-lasting developmental sequalae of early life chronic pain processes. Although no cohort differences in pain intensity scores were found during the thermal pain modalities, CPs and HCs showed significant difference in changes in EEG spectral power, peak frequency, permutation entropy, and network functional connectivity at specific frequency bands (P < 0.05) during the tonic heat and cold stimulations.
Conclusion:
This suggests that EEG can characterize subtle differences in heat and cold pain sensitivity in CPs. The complementation of EEG and evoked pain in the clinical assessment of pediatric chronic MSK pain can better detect underlying pain mechanisms and changes in pain sensitivity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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