@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.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dong2022influence,

title = {Influence of ambient temperature on personnel thermal comfort and working efficiency under isolation condition of underground engineering},

author = {Xian Dong and Yeyu Wu and Zhijun Tu and Bin Cao and Xianting Li and Zixu Yang and Fei Liu and Zheli Xing},

doi = {https://doi.org/10.1016/j.enbuild.2022.112438},

year  = {2022},

date = {2022-08-29},

urldate = {2022-01-01},

journal = {Energy and Buildings},

pages = {112438},

publisher = {Elsevier},

abstract = {When attacked by weapons of mass destruction, underground engineering will operate under isolation condition, which results in the increase of temperature, humidity, and CO2 concentration. At present, there are few studies on personnel thermal comfort and working efficiency in underground engineering, especially under isolation condition. To improve the personnel thermal comfort and working efficiency under such condition, the influence of environmental temperature changes on human thermal comfort and working efficiency was investigated through a combination of subjective and objective methods. A subjective questionnaire and working efficiency test were conducted on the subjects in the artificial climate chamber, and synchronously monitored electrocardiography (ECG), electroencephalography (EEG), and other physiological parameters of the subjects were recorded, when isolation condition was achieved in the artificial climate chamber. The results show that: (1) the human neutral temperature is 24.2 °C, and thermal comfort zone is [23 °C, 25.5 °C] for isolation condition; (2) the high working efficiency area is [27.3 °C, 28.8 °C] for isolation condition; (3) the average of the TSV corresponding to the highest working efficiency point is 1.3 under isolation condition; (4) from the correlation analysis of working efficiency and personnel physiological indicators, personnel EEG index and task performance are significantly related, and the ECG index and task performance are not relevant for subjects performing brain work under isolation conditions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{humphries2022motor,

title = {Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface},

author = {Joseph B Humphries and Daniela JS Mattos and Jerrel Rutlin and Andy GS Daniel and Kathleen Rybczynski and Theresa Notestine and Joshua S Shimony and Harold Burton and Alexandre Carter and Eric C Leuthardt},

doi = {https://doi.org/10.1080/2326263X.2022.2057757},

year  = {2022},

date = {2022-07-01},

urldate = {2022-01-01},

journal = {Brain-Computer Interfaces},

volume = {9},

number = {3},

pages = {179--192},

publisher = {Taylor & Francis},

abstract = {Upper extremity weakness in chronic stroke remains a problem not fully addressed by current therapies. Brain–computer interfaces (BCIs) engaging the unaffected hemisphere are a promising therapy that are entering clinical application, but the mechanism underlying recovery is not well understood. We used resting state functional MRI to assess the impact a contralesionally driven EEG BCI therapy had on motor system functional organization. Patients used a therapeutic BCI for 12 weeks at home. We acquired resting-state fMRI scans and motor function data before and after the therapy period. Changes in functional connectivity (FC) strength between motor network regions of interest (ROIs) and the topographic extent of FC to specific ROIs were analyzed. Most patients achieved clinically significant improvement. Motor FC strength and topographic extent decreased following BCI therapy. Motor recovery correlated with reductions in motor FC strength across the entire motor network. These findings suggest BCI-mediated interventions may reverse pathologic strengthening of dysfunctional network interactions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rustamov2022thetab,

title = {Theta-gamma coupling as a cortical biomarker of brain-computer interface mediated motor recovery in chronic stroke},

author = {Nabi Rustamov and Joseph Humphries and Alexandre Carter and Eric C Leuthardt},

doi = {https://doi.org/10.1093/braincomms/fcac136},

year  = {2022},

date = {2022-05-25},

urldate = {2022-01-01},

journal = {Brain Communications},

volume = {4},

issue = {3},

abstract = {Chronic stroke patients with upper-limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain–computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain–computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm-specific mechanism would correlate with the functional improvements effected by a brain–computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain–computer interface therapy are associated with alterations in phase–amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain–computer interface task for 12 weeks. Resting-state EEG recordings and motor function data were acquired before initiating brain–computer interface therapy and once every 4 weeks after the therapy. Changes in phase–amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta–gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain–computer interface therapy sessions. Importantly, an increase in theta–gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta–gamma coupling increase following brain–computer interface therapy were mostly located in the hand regions of the primary motor cortex on the left and right cerebral hemispheres. Beta–gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha–gamma coupling was not altered by brain–computer interface therapy. Power spectra did not change significantly over the course of the brain–computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain–computer interface therapy was strongly correlated with increased theta–gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross-frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{snider2022predicting,

title = {Predicting hypoxic hypoxia using machine learning and wearable sensors},

author = {Dallas H Snider and Steven E Linnville and Jeffrey B Phillips and Merrill G Rice},

url = {https://doi.org/10.1016/j.bspc.2021.103110},

year  = {2021},

date = {2021-09-04},

journal = {Biomedical Signal Processing and Control},

volume = {71},

pages = {103110},

publisher = {Elsevier},

abstract = {The capability of detecting symptoms of hypoxia (i.e., reduced oxygen) and other cognitive impairments in-flight with wearable sensors and machine learning based algorithms will benefit the aviation community by saving lives and preventing mishaps. In this study, knowledge discovery processes were implemented to build classification models to predict hypoxia from wearable, dry-EEG sensor data collected from 85 participants in a two-phase study. Over a 35-minute period and while wearing aviation flight masks which regulated their oxygen intake, participants would alternate between a 2-minute cognitive test on CogScreen Hypoxia Edition and a 3-minute simulated flying task on X-Plane 11, with the oxygen concentration reducing every 5 min following the simulated flight task. The decrease in oxygen each 5 min simulated an increase in altitude. Features extracted from the EEG waveforms were transformed using principal component analysis to reduce the dimensionality of the data. Naïve Bayes, decision tree, random forest, and neural network algorithms were utilized to classify the transformed brain wave data as either normal or hypoxic. The algorithms sensitivity ranged from 0.83 to 1.00 while the specificity ranged from 0.91 to 1.00. This study makes a step forward in developing a real-time, in-flight hypoxia detection system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rahimi2021image,

title = {Image luminance changes contrast sensitivity in visual cortex},

author = {Hamed Rahimi-Nasrabadi and Jianzhong Jin and Reece Mazade and Carmen Pons and Sohrab Najafian and Jose-Manuel Alonso},

doi = {https://doi.org/10.1016/j.celrep.2021.108692},

year  = {2021},

date = {2021-02-02},

journal = {Cell reports},

volume = {34},

number = {5},

pages = {108692},

publisher = {Elsevier},

abstract = {Accurate measures of contrast sensitivity are important for evaluating visual disease progression and for navigation safety. Previous measures suggested that cortical contrast sensitivity was constant across widely different luminance ranges experienced indoors and outdoors. Against this notion, here, we show that luminance range changes contrast sensitivity in both cat and human cortex, and the changes are different for dark and light stimuli. As luminance range increases, contrast sensitivity increases more within cortical pathways signaling lights than those signaling darks. Conversely, when the luminance range is constant, light-dark differences in contrast sensitivity remain relatively constant even if background luminance changes. We show that a Naka-Rushton function modified to include luminance range and light-dark polarity accurately replicates both the statistics of light-dark features in natural scenes and the cortical responses to multiple combinations of contrast and luminance. We conclude that differences in light-dark contrast increase with luminance range and are largest in bright environments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}