@article{chiossi2023exploring,

title = {Exploring Physiological Correlates of Visual Complexity Adaptation: Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR Adaptive Systems},

author = {Francesco Chiossi and Changkun Ou and Sven Mayer},

url = {https://www.researchgate.net/profile/Francesco-Chiossi/publication/369033584_Exploring_Physiological_Correlates_of_Visual_Complexity_Adaptation_Insights_from_EDA_ECG_and_EEG_Data_for_Adaptation_Evaluation_in_VR_Adaptive_Systems/links/64064d5d0cf1030a567a05fb/Exploring-Physiological-Correlates-of-Visual-Complexity-Adaptation-Insights-from-EDA-ECG-and-EEG-Data-for-Adaptation-Evaluation-in-VR-Adaptive-Systems.pdf},

year  = {2023},

date = {2023-04-23},

urldate = {2023-04-23},

abstract = {Physiologically-adaptive Virtual Reality can drive interactions and adjust virtual content to better fit users’ needs and support specific goals. However, the complexity of psychophysiological inference hinders efficient adaptation as the relationship between cognitive and physiological features rarely show one-to-one correspondence. Therefore, it is necessary to employ multimodal approaches to evaluate the effect of adaptations. In this work, we analyzed a multimodal dataset (EEG, ECG, and EDA) acquired during interaction with a VR-adaptive system that employed EDA as input for adaptation of secondary task difficulty. We evaluated the effect of dynamic adjustments on different physiological features and their correlation. Our results show that when the adaptive system increased the secondary task difficulty, theta, beta, and phasic EDA features increased. Moreover, we found a high correlation between theta, alpha, and beta oscillations during difficulty adjustments. Our results show how specific EEG and EDA features can be employed for evaluating VR adaptive systems.

},

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pubstate = {published},

tppubtype = {article}

}

@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.



},

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pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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.



},

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pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@article{chang2022effect,

title = {Effect of Immersive Virtual Mirror Visual Feedback on Mu Suppression and Coherence in Motor and Parietal Cortex in Stroke},

author = {Won Kee Chang and Hyunmi Lim and Seo Hyun Park and Chaiyoung Lim and Nam-Jong Paik and Won-Seok Kim and Jeonghun Ku},

doi = {https://doi.org/10.21203/rs.3.rs-2253842/v1},

year  = {2022},

date = {2022-11-11},

urldate = {2022-01-01},

abstract = {Background: This study aimed to investigate the activation pattern of the motor cortex (M1) and parietal cortex during immersive virtual reality (VR)-based mirror visual feedback (MVF) of the upper limb in patients with chronic stroke. Methods: Fourteen patients with chronic stroke with severe upper limb hemiparesis (Brunnstrom stage of hand 1-3) and 21 healthy controls were included. The participants performed wrist extension tasks with their unaffected wrists (or the dominant side in controls). In the MVF condition, the movement of the affected hand was synchronized with that of the unaffected hand. In contrast, only the movement of the unaffected hand was shown in the no-MVF condition. Electroencephalography was obtained during experiments with two conditions (MVF vs no-MVF). Mu suppression in the bilateral M1 and parietal cortex and mu coherence between the ipsilateral M1 and parietal cortex in each hemisphere and interhemispheric M1 were used for analyses. Results: In patients with stroke, MVF induced significant mu suppression in both the ipsilesional M1 and parietal lobes (p=0.006 and p=0.009, respectively), while significant mu suppression was observed in the bilateral M1 (p=0.003 for ipsilesional and p=0.041 for contralesional M1, respectively) and contralesional (contralateral hemisphere to the moving hand) parietal lobes in the healthy controls (p=0.036). The ipsilesional mu coherence between the M1 and parietal cortex in patients with stroke was stronger than that in controls regardless of MVF condition (p<0.001), while mu coherence between interhemispheric M1 cortices was significantly weaker in patients with stroke (p=0.032). Conclusion: In patients with stroke, MVF using immersive VR induces mu suppression in the ipsilesional M1 and parietal lobe. Our findings provide evidence of the neural mechanism of MVF using immersive VR and support its application in patients with stroke with severe hemiparesis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{woo2022effects,

title = {Effects of Low Visual Acuity Simulations on Eye-Hand Coordination and Brainwaves in Healthy Adults},

author = {Hee-Soon Woo and Chiang-Soon Song},

doi = {https://doi.org/10.14474/ptrs.2022.11.3.296},

year  = {2022},

date = {2022-09-30},

urldate = {2022-01-01},

journal = {Physical Therapy Rehabilitation Science},

volume = {11},

number = {3},

pages = {296--303},

publisher = {Korean Academy of Physical Therapy Rehabilitation Science},

abstract = {Objective: In general, macular degeneration, cataracts and glaucoma generally cause visual injury in clinical settings. This study aimed to examine the effects of low visual acuity simulations on hand manual dexterity function and brainwaves in healthy young adults.

Design: Cross-sectional study design

Methods: This study was an observational, cross-sectional study. Seventy healthy young adults participated in this study. To evaluate the effects of low visual acuity simulations on hand function and brain waves, this study involved four different visual conditions including (1) normal vision, (2) simulated cataracts, (3) simulated glaucoma, and (4) simulated macular degeneration. The hand function was measured to use the Minnesota manual dexterity test (MMDT), and the brainwaves was also measured to use the electroencephalography.

Results: In hand function, placing and turning performance on the MMDT in the normal visual condition was significantly different than that in the cataract and macular degeneration conditions (p<0.05), and the placing performance was significantly differred in the normal condition than that in the simulated glaucoma. However, turning was not significantly different in the normal condition than that in the simulated glaucoma. The alpha, beta, and gamma waves did not significantly differ among the four visual conditions (p＞0.05).

Conclusions: The results suggest that limited visual information negatively affects the ability to perform tasks requiring arm-hand dexterity and eye-hand coordination. However, the effectiveness of low visual acuity on the brainwaves should be further studied for rehabilitative evidence of visual impairment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}