Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 177
Filter
1.
Proc Natl Acad Sci U S A ; 119(46): e2120221119, 2022 11 16.
Article in English | MEDLINE | ID: covidwho-2106733

ABSTRACT

The COVID-19 pandemic has created a large population of patients who are slow to recover consciousness following mechanical ventilation and sedation in the intensive care unit. Few clinical scenarios are comparable. Possible exceptions are the rare patients in post-cardiac arrest coma with minimal to no structural brain injuries who recovered cognitive and motor functions after prolonged delays. A common electroencephalogram (EEG) signature seen in these patients is burst suppression [8]. Biophysical modeling has shown that burst suppression is likely a signature of a neurometabolic state that preserves basic cellular function "during states of lowered energy availability." These states likely act as a brain protective mechanism [9]. Similar EEG patterns are observed in the anoxia resistant painted turtle [24]. We present a conceptual analysis to interpret the brain state of COVID-19 patients suffering prolonged recovery of consciousness. We begin with the Ching model and integrate findings from other clinical scenarios and studies of the anoxia-tolerant physiology of the painted turtle. We postulate that prolonged recovery of consciousness in COVID-19 patients could reflect the effects of modest hypoxic injury to neurons and the unmasking of latent neuroprotective mechanisms in the human brain. This putative protective down-regulated state appears similar to that observed in the painted turtle and suggests new approaches to enhancing coma recovery [12].


Subject(s)
COVID-19 , Coma , Humans , Pandemics , Electroencephalography , Brain , Hypoxia
2.
PLoS One ; 17(10): e0273346, 2022.
Article in English | MEDLINE | ID: covidwho-2054322

ABSTRACT

While the psychological predictors of antiscience beliefs have been extensively studied, neural underpinnings of the antiscience beliefs have received relatively little interest. The aim of the current study is to investigate whether attitudes towards the scientific issues are reflected in the N400 potential. Thirty-one individuals were asked to judge whether six different issues presented as primes (vaccines, medicines, nuclear energy, solar energy, genetically-modified organisms (GMO), natural farming) are well-described by ten positive and ten negative target words. EEG was recorded during the task. Furthermore, participants were asked to rate their own expertise in each of the six topics. Both positive and negative target words related to GMO elicited larger N400, than targets associated with vaccines and natural farming. The results of the current study show that N400 may be an indicator of the ambiguous attitude toward scientific issues.


Subject(s)
Evoked Potentials , Vaccines , Attitude , Climate Change , Electroencephalography , Female , Humans , Male , Plants, Genetically Modified , Semantics
3.
Med Biol Eng Comput ; 60(12): 3447-3460, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2048496

ABSTRACT

The precise assessment of cognitive load during a learning phase is an important pathway to improving students' learning efficiency and performance. Physiological measures make it possible to continuously monitor learners' cognitive load in remote learning during the COVID-19 outbreak. However, maintaining a good balance between performance and computational cost is still a major challenge in advancing cognitive load recognition technology to real-world applications. This paper introduced an adaptive feature recalibration (AFR) convolutional neural network to overcome this challenge by capturing the most discriminative physiological features (EEG and eye-tracking). The results revealed that the optimal average classification accuracy of the feature combination obtained by the AFR method reached 95.56% with only 60 feature dimensions. Additionally, compared with the best result of the conventional correlation-based feature selection (CFS) method, the introduced AFR algorithm achieved higher accuracy and cheaper computational cost, as well as a 2.06% improvement in accuracy and a 51.21% reduction in feature dimension, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.


Subject(s)
COVID-19 , Electroencephalography , Humans , Electroencephalography/methods , Feasibility Studies , Neural Networks, Computer , Cognition
4.
J Child Neurol ; 37(12-14): 1014, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2042923
5.
J Psychosom Res ; 162: 111046, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2041977

ABSTRACT

OBJECTIVE: Psychogenic non-epileptic seizures (PNES) resemble epileptic seizures but are not due to underlying epileptic activity and in some cases coexist alongside epilepsy. We described the clinical characteristics of patients with PNES as reported in the literature from the outbreak of the COVID-19 pandemic. We evaluated differences between patients with a diagnosis made immediately before the pandemic (pPNES) and those newly diagnosed during it (nPNES). METHODS: A systematic search with individual patient analysis of PNES cases published since the COVID-19 pandemic outbreak was performed. Differences between pPNES and nPNES were analyzed using Chi-square or Fisher exact test. RESULTS: Eleven articles were included, with 133 patients (106 pPNES and 27 nPNES). In the pPNES group, PNES frequency increased during the pandemic in 20/106 patients, whereas in 78/106, the frequency remained stable or decreased. nPNES was associated with higher risks of SARS-CoV-2 infection and epilepsy diagnosis, whereas psychiatric comorbidities were less frequent. CONCLUSIONS: During the pandemic, most patients with pPNES remained stable or improved, whereas nPNES was associated with a lower burden of psychiatric comorbidities. These intriguing findings suggest that, at least in some patients, the COVID-19 pandemic may not necessarily lead to worsening in the frequency of PNES and quality of life.


Subject(s)
COVID-19 , Epilepsy , COVID-19/epidemiology , Electroencephalography , Epilepsy/diagnosis , Epilepsy/epidemiology , Humans , Pandemics , Quality of Life/psychology , SARS-CoV-2 , Seizures/diagnosis
7.
Cogn Affect Behav Neurosci ; 22(5): 984-1000, 2022 10.
Article in English | MEDLINE | ID: covidwho-2024387

ABSTRACT

Spontaneously touching one's own face (sFST) is an everyday behavior that occurs in people of all ages, worldwide. It is-as opposed to actively touching the own face-performed without directing one's attention to the action, and it serves neither instrumental (scratching, nose picking) nor communicative purposes. These sFST have been discussed in the context of self-regulation, emotional homeostasis, working memory processes, and attention focus. Even though self-touch research dates back decades, neuroimaging studies of this spontaneous behavior are basically nonexistent. To date, there is only one electroencephalography study that analyzed spectral power changes before and after sFST in 14 participants. The present study replicates the previous study on a larger sample. Sixty participants completed a delayed memory task of complex haptic relief stimuli while distracting sounds were played. During the retention interval 44 of the participants exhibited spontaneous face touch. Spectral power analyses corroborated the results of the replicated study. Decreased power shortly before sFST and increased power right after sFST indicated an involvement of regulation of attentional, emotional, and working memory processes. Additional analyses of spectral power changes during the skin contact phase of sFST revealed that significant neurophysiological changes do not occur while skin contact is in progress but at the beginning of sFST (movement toward face and initial skin contact). The present findings clearly illustrate the complexity of sFST and that the specific trigger mechanisms and functions of this spontaneous behavior need to be further investigated in controlled, experimental studies.


Subject(s)
Emotional Regulation , Touch Perception , Cognition , Electroencephalography , Humans , Touch/physiology , Touch Perception/physiology
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4135-4138, 2022 07.
Article in English | MEDLINE | ID: covidwho-2018753

ABSTRACT

Stage 2 sleep spindles are considered useful biomarkers for the integrity of the central nervous system and for cognitive and memory skills. We investigated sleep spindles patterns in subjects after 12 months of their hospitalization in the intensive care unit (ICU) of the Padova Teaching Hospital due to COVID-19 between March and November 2020. Before the nap, participants (13 hospitalized in ICU - ICU; 9 hospitalized who received noninvasive ventilation - nonlCU; 9 age and sex-matched healthy controls - CTRL, i.e., not infected by COVID-19) underwent a cognitive and psychological as-sessment. During the nap, high-density electroencephalography (EEG) recordings were acquired. Slow (i.e., [9]-[12] Hz) and fast (i.e.,]12-16] Hz) spindles were automatically detected. Spindle density and spindle source reconstruction in brain grey matter were extracted. The psychological assessment revealed a statistical difference comparing CTRL and nonlCU in Beck Depression Inventory score and in the Physical Quality of Life index (pvalue = 0.03). The cognitive assessment revealed a trend of worsening results in executive functions in COVID-19 survivors. Slow spindle density significantly decreased comparing CTRL to COVID-19 survivors (pvalue= 0.001). There were statistically significant differences in EEG source-waveforms fast spindle amplitude onset among the three groups, mainly between CTRL and nonlCU. Clinical Relevance- Our results suggest that nonlCU were more susceptible to the hospitalization experience than ICU participants with a slight effect on cognitive tests. This impacted the spindle generation revealing a decreased density of slow spindles and affecting the generators of fast spindles in COVID-19 survivors especially in nonlCU.


Subject(s)
COVID-19 , Electroencephalography/methods , Humans , Neuropsychological Tests , Quality of Life , Sleep/physiology
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 3702-3705, 2022 07.
Article in English | MEDLINE | ID: covidwho-2018751

ABSTRACT

The current study is aimed to evaluate the effect of COVID-19 vaccine on human EEG and the persistence of the effect. Within a one-year-long resting EEG study period, the healthy male subject was administered two Comirnaty doses three weeks apart to prevent COVID-19. Fourteen recordings were acquired from the subject in one year: twelve reference and two post-vaccination recordings after administrating the second dose of Comirnaty. The changes in absolute powers of EEG frequency bands, EEG spectral asymmetry index (SASI), and Higuchi's fractal dimension (HFD) were analyzed. The results indicated a statistically significant increase in absolute gamma power, SASI and HFD values on the fifth day after the vaccination, while the EEG had restored its normal character on the twelfth day after vaccination. These measures seem to have higher sensitivity for the detection of the effects of the vaccine Clinical Relevance- This is the first study evaluating COVID-19 vaccine effect on healthy human EEG. The study indicated that the vaccine disturbs EEG but the impact is not long-lasting.


Subject(s)
COVID-19 Vaccines , COVID-19 , COVID-19/prevention & control , Electroencephalography/methods , Fractals , Humans , Male , RNA, Messenger
10.
Neurol Sci ; 43(11): 6159-6166, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2014175

ABSTRACT

INTRODUCTION: During the COVID-19 pandemic, electroencephalography (EEG) proved to be a useful tool to demonstrate brain involvement. Many studies reported non-reactive generalized slowing as the most frequent pattern and epileptiform activity in a minority of patients. OBJECTIVE: To investigate the prevalence of diffuse unreactive background attenuation or suppression and its correlation with outcome in a cohort of COVID-19 patients. METHODS: The EEGs recorded during the first year of the COVID-19 pandemic were retrospectively evaluated to identify the main pattern and focus on the occurrence of a low-voltage background, either attenuated (10-20 µV) or suppressed (< 10 µV). We sought a correlation between in-hospital mortality and low-voltage EEG. In a subsample of patients, biomarkers of inflammation, hypoxemia and organ failure were collected. Brain imaging was also evaluated. RESULTS: Among 98 EEG performed in 50 consecutive patients, diffuse unreactive slowing was the most prevalent pattern (54%), followed by unreactive attenuation or suppression pattern (26%), being the latter significantly correlated with an unfavourable outcome (p = 0.0004). Survivors showed significantly lower interleukine-6 values compared to non-survivors. Patients with attenuated EEG and non-survivors also showed lower PaO2/FiO2 values. Neuroradiological findings were very heterogeneous with a prevalence of lesions suggestive of a microangiopathic substrate. CONCLUSIONS: EEG attenuation or suppression may be more frequent than previously reported and significantly associated with a poor outcome. SARS-CoV-2 infection may result in encephalopathy and reduced EEG voltage through mechanisms that are still unknown but deserve attention given its negative impact on prognosis.


Subject(s)
COVID-19 , Humans , Pandemics , Retrospective Studies , SARS-CoV-2 , Electroencephalography/methods
11.
Telemed J E Health ; 28(8): 1159-1165, 2022 08.
Article in English | MEDLINE | ID: covidwho-1577484

ABSTRACT

Introduction: Access to mental health care is a significant challenge in patients with psychogenic nonepileptic seizures (PNES). Telepsychology can curb the access barriers and improve adherence but the role of telepsychology in improving adherence has not been well investigated. The current study examines the utility of telepsychology during the COVID-19 pandemic and treatment adherence in PNES patients. Materials and Methods: Patients with PNES admitted to a 12-week counseling program were offered two visit types: telepsychology and in-office. Visit type, visit status, and demographic information were obtained from department database. Follow-up visits in 6 months were used to examine the effect of visit type on visit status. Adherence to treatment was measured by higher attendance of scheduled visits and less cancellation and no-show rates. Results: Two hundred fifty-seven (n) patients who scheduled virtual or telepsychology visits were included in the study. After adjusting for demographic variables, and accounting for repeated measures, telepsychology visits were significantly more likely to be attended (odds ratio [OR] = 2.40, 95% confidence interval [CI] = 1.69-3.41, p < 0.001) and were significantly less likely to be canceled (OR = 0.43, 95% CI = 0.29-0.64, p < 0.001). The regression model showed patients in the telepsychology visit group attended more than three times as many visits as in-office patients (incidence rate ratios = 3.16, 95% CI = 2.13-4.73, p < 0.001). Conclusions: Patients with PNES have logistical and psychological barriers that can impede their ability to attend counseling treatment. Receiving care remotely may have been associated with higher engagement with mental health treatment compared to having to travel to counseling clinics. Considering the symptom-related restrictions patients with PNES have and the barriers presented by the COVID-19 pandemic, telepsychology played a key role for continuation of mental health treatment.


Subject(s)
COVID-19 , Seizures , COVID-19/epidemiology , Electroencephalography , Humans , Pandemics , Psychogenic Nonepileptic Seizures , Seizures/epidemiology , Seizures/psychology , Seizures/therapy , Treatment Adherence and Compliance
12.
Sensors (Basel) ; 22(17)2022 Sep 01.
Article in English | MEDLINE | ID: covidwho-2010251

ABSTRACT

Previous research and clinical reports have shown that some individuals after COVID-19 infection may demonstrate symptoms of so-called brain fog, manifested by cognitive impairment and disorganization in behavior. Meanwhile, in several other conditions, related to intellectual function, a specific pattern of changes in electric brain activity, as recorded by quantitative electroencephalography (QEEG) has been documented. We hypothesized, that in post-COVID brain fog, the subjective complaints may be accompanied by objective changes in the QEEG profile. In order to test this hypothesis, we have performed an exploratory study on the academic staff of our University with previous records of QEEG originating in the pre-COVID-19 era. Among them, 20 subjects who revealed neurological problems in the cognitive sphere (confirmed as covid fog/brain fog by a clinical specialist) after COVID-19 infection were identified. In those individuals, QEEG was performed. We observed, that opposite to baseline QEEG records, increased Theta and Alpha activity, as well as more intensive sensimotor rhythm (SMR) in C4 (right hemisphere) in relation to C3 (left hemisphere). Moreover, a visible increase in Beta 2 in relation to SMR in both hemispheres could be documented. Summarizing, we could demonstrate a clear change in QEEG activity patterns in individuals previously not affected by COVID-19 and now suffering from post-COVID-19 brain fog. These preliminary results warrant further interest in delineating their background. Here, both neuroinflammation and psychological stress, related to Sars-CoV2-infection may be considered. Based on our observation, the relevance of QEEG examination as a supportive tool for post-COVID clinical workup and for monitoring the treatment effects is also to be explored.


Subject(s)
COVID-19 , Brain , Electroencephalography , Humans , Mental Fatigue , RNA, Viral , SARS-CoV-2
13.
Sci Rep ; 12(1): 14908, 2022 Sep 01.
Article in English | MEDLINE | ID: covidwho-2008318

ABSTRACT

The current global crisis facing the world is the COVID-19 pandemic. Infection from the SARS-CoV-2 virus leads to serious health complications and even death. As it turns out, COVID-19 not only physically assails the health of those infected, but also leads to serious mental illness regardless of the presence of the disease. Social isolation, fear, concern for oneself and one's loved ones, all of this occurs when a pandemic overloads people. People exhibit numerous neurological disorders that have never happened to them before. Patients are diagnosed with frequent panic attacks, the result of which can be seen in their Quantitative Electroencephalogram results. This test may be one of the main diagnostic tools of the COVID-19 pandemic. From the results obtained, it is possible to compare and draw conclusions. This method of testing effectively allows EEG biofeedback training and observes its effect on brain activity. The feedback received in this way gives us the opportunity to properly tailor a protocol for the patient and their conditions. Numerous studies support the effectiveness of EEG biofeedback for panic attacks and other psychiatric disorders. The purpose of our study was to show the effectiveness of EEG biofeedback with a Quantitative Electroencephalogram of the brainwave pattern after having COVID-19 and what symptoms may result.


Subject(s)
Brain Waves , COVID-19 , Neurofeedback , Panic Disorder , COVID-19/therapy , Electroencephalography/methods , Humans , Pandemics , Panic Disorder/therapy , SARS-CoV-2
14.
IEEE Trans Biomed Eng ; 69(6): 1983-1994, 2022 06.
Article in English | MEDLINE | ID: covidwho-1997179

ABSTRACT

OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) with the spatial resolution of functional near-infrared spectroscopy (fNIRS) require novel approaches to decoding. METHODS: We present an EEG-fNIRS Hybrid BCI that employs a new bimodal deep neural network architecture consisting of two convolutional sub-networks (subnets) to decode overt and imagined speech. Features from each subnet are fused before further feature extraction and classification. Nineteen participants performed overt and imagined speech in a novel cue-based paradigm enabling investigation of stimulus and linguistic effects on decoding. RESULTS: Using the hybrid approach, classification accuracies (46.31% and 34.29% for overt and imagined speech, respectively (chance: 25%)) indicated a significant improvement on EEG used independently for imagined speech (p = 0.020) while tending towards significance for overt speech (p = 0.098). In comparison with fNIRS, significant improvements for both speech-types were achieved with bimodal decoding (p<0.001). There was a mean difference of ∼12.02% between overt and imagined speech with accuracies as high as 87.18% and 53%. Deeper subnets enhanced performance while stimulus effected overt and imagined speech in significantly different ways. CONCLUSION: The bimodal approach was a significant improvement on unimodal results for several tasks. Results indicate the potential of multi-modal deep learning for enhancing neural signal decoding. SIGNIFICANCE: This novel architecture can be used to enhance speech decoding from bimodal neural signals.


Subject(s)
Brain-Computer Interfaces , Deep Learning , Electroencephalography/methods , Humans , Neural Networks, Computer , Speech
15.
Int J Environ Res Public Health ; 19(15)2022 08 02.
Article in English | MEDLINE | ID: covidwho-1994059

ABSTRACT

Improving the mental health of urban residents is a global public health priority. This study builds on existing work that demonstrates the ability of virtual exposure to restorative environments to improve population mental health. It compares the restorative effects of green, blue and historic environments delivered by both flat screen and immersive virtual reality technology, and triangulates data from psychological, physiological and qualitative sources. Results from the subjective measure analyses showed that exposures to all the experimental videos were associated with self-reported reduced anxiety and improved mood, although the historic environment was associated with a smaller reduction of anxiety (p < 0.01). These results were supported by the qualitative accounts. For two of the electroencephalography (EEG) frequency bands, higher levels of activity were observed for historic environments. In relation to the mode of delivery, the subjective measures did not suggest any effect, while for the EEG analyses there was evidence of a significant effect of technology across three out of four frequency bands. In conclusion, this study adds to the evidence that the benefits of restorative environments can be delivered through virtual exposure and suggests that virtual reality may provide greater levels of immersion than flat screen viewing.


Subject(s)
Smart Glasses , Virtual Reality , Anxiety Disorders , Electroencephalography , Humans , Mental Health
16.
Biosensors (Basel) ; 11(12)2021 Dec 06.
Article in English | MEDLINE | ID: covidwho-1993933

ABSTRACT

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.


Subject(s)
Depressive Disorder, Major , Electroencephalography , Depressive Disorder, Major/diagnosis , Humans , Machine Learning , Support Vector Machine
17.
Psychol Med ; 52(11): 2189-2197, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1991456

ABSTRACT

BACKGROUND: The two key mechanisms affected by internet gaming disorder (IGD) are cognitive and reward processing. Despite their significance, little is known about neurophysiological features as determined using resting-state electroencephalography (EEG) source functional connectivity (FC). METHODS: We compared resting-state EEG source FC within the default mode network (DMN) and reward/salience network (RSN) between patients with IGD and healthy controls (HCs) to identify neurophysiological markers associated with cognitive and reward processing. A total of 158 young male adults (79 patients with IGD and 79 HCs) were included, and the source FC of the DMN and RSN in five spectral bands (delta, theta, alpha, beta, and gamma) were assessed. RESULTS: Patients with IGD showed increased theta, alpha, and beta connectivity within the DMN between the orbitofrontal cortex and parietal regions compared with HCs. In terms of RSN, patients with IGD exhibited elevated alpha and beta connectivity between the anterior cingulate gyrus and temporal regions compared with HCs. Furthermore, patients with IGD showed negative correlations between the severity of IGD symptoms and/or weekly gaming time and theta and alpha connectivity within the DMN and theta, alpha, and beta connectivity within the RSN. However, the duration of IGD was not associated with EEG source FC. CONCLUSIONS: Hyper-connectivities within the DMN and RSN may be considered potential state markers associated with symptom severity and gaming time in IGD.


Subject(s)
Behavior, Addictive , Brain Mapping , Adult , Humans , Male , Neural Pathways/diagnostic imaging , Internet Addiction Disorder/diagnostic imaging , Brain , Magnetic Resonance Imaging , Electroencephalography , Reward , Internet
18.
J Integr Neurosci ; 21(4): 115, 2022 Jun 20.
Article in English | MEDLINE | ID: covidwho-1957654

ABSTRACT

Seizures have been increasingly identified as a neurologic manifestation of coronavirus disease 2019 (COVID-19) infection. They may be symptomatic due to systemic infections, as a result of direct central nervous system (CNS) invasion, or occur in response to inflammatory reactions to the virus. It is possible that proinflammatory molecules released in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to hyperexcitability and epileptogenesis, similar to infections caused by other neurotrophic viruses. Cerebral spinal fluid (CSF) in patients with COVID-19 and seizures is negative for SARS-CoV-2 (PCR) in the majority of patients, but has been found to be positive for proinflammatory molecules like IL-6, IL-8, and anti-neuronal autoantibodies. Electroencephalogram (EEG) in COVID-19 patients are nonspecific. However, in the encephalopathic and critically ill subpopulation, EEG is essential in detecting nonconvulsive seizures and status epilepticus which is associated with increased overall mortality in COVID-19 patients. Thus, as encephalopathy is often the only CNS symptom evidenced in patients with nonconvulsive seizures, more judicious use of continuous EEG in encephalopathic COVID-19 patients should be considered. This would facilitate earlier detection and treatment of seizures in this population, which would ultimately improve outcomes. Further research into the onset and potential for development of seizures and epilepsy in patients with COVID-19 is needed.


Subject(s)
Brain Diseases , COVID-19 , COVID-19/complications , Electroencephalography , Humans , SARS-CoV-2 , Seizures/etiology
19.
Neuropsychologia ; 174: 108334, 2022 09 09.
Article in English | MEDLINE | ID: covidwho-1937048

ABSTRACT

In the last two years, face-to-face interactions have drastically changed worldwide, because of the COVID-19 pandemic: the persistent use of masks has had the advantage of reducing viral transmission, but it has also had the cost of impacting on the perception and recognition of social information from faces, especially emotions. To assess the cerebral counterpart to this condition, we carried out an EEG experiment, extracting Event-Related Potentials (ERPs) evoked by emotional faces with and without surgical masks. Besides the expected impairment in emotion recognition in both accuracy and response times, also the classical face-related ERPs (N170 and P2) are altered by the presence of surgical masks. Importantly, the effect is stronger in individuals with a lower daily exposure to masks, suggesting that the brain must adapt to an extra constraint in decoding social input, due to masks hiding crucial facial information.


Subject(s)
COVID-19 , Facial Recognition , Electroencephalography , Emotions/physiology , Evoked Potentials/physiology , Facial Expression , Facial Recognition/physiology , Humans , Pandemics
20.
Comput Biol Med ; 148: 105849, 2022 09.
Article in English | MEDLINE | ID: covidwho-1926335

ABSTRACT

BACKGROUND AND OBJECTIVE: For the emerging significance of mental stress, various research directives have been established over time to understand better the causes of stress and how to deal with it. In recent years, the rise of video gameplay has been unprecedented, further triggered by the lockdown imposed due to the COVID-19 pandemic. Several researchers and organizations have contributed to the practical analysis of the impacts of such extended periods of gameplay, which lacks coordinated studies to underline the outcomes and reflect those in future game designing and public awareness about video gameplay. Investigations have mainly focused on the "gameplay stress" based on physical syndromes. Some studies have analyzed the effects of video gameplay with Electroencephalogram (EEG), Magnetic resonance imaging (MRI), etc., without concentrating on the relaxation procedure after video gameplay. METHODS: This paper presents an end-to-end stress analysis for video gaming stimuli using EEG. The power spectral density (PSD) of the Alpha and Beta bands is computed to calculate the Beta-to-Alpha ratio (BAR). The Alpha and Beta band power is computed, and the Beta-to-Alpha band power ratio (BAR) has been determined. In this article, BAR is used to denote mental stress. Subjects are chosen based on various factors such as gender, gameplay experience, age, and Body mass index (BMI). EEG is recorded using Scan SynAmps2 Express equipment. There are three types of video gameplay: strategic, puzzle, and combinational. Relaxation is accomplished in this study by using music of various pitches. Two types of regression analysis are done to mathematically model stress and relaxation curve. Brain topography is rendered to indicate the stressed and relaxed region of the brain. RESULTS: In the relaxed state, the subjects have BAR 0.701, which is considered the baseline value. Non-gamer subjects have an average BAR of 2.403 for 1 h of strategic video gameplay, whereas gamers have 2.218 BAR concurrently. After 12 minutes of listening to low-pitch music, gamers achieved 0.709 BAR, which is nearly the baseline value. In comparison to Quartic regression, the 4PL symmetrical sigmoid function performs regression analysis with fewer parameters and computational power. CONCLUSION: Non-gamers experience more stress than gamers, whereas strategic games stress the human brain more. During gameplay, the beta band in the frontal region is mostly activated. For relaxation, low pitch music is the most useful medium. Residual stress is evident in the frontal lobe when the subjects have listened to high pitch music. Quartic regression and 4PL symmetrical sigmoid function have been employed to find the model parameters of the relaxation curve. Among them, quartic regression performs better in terms of Akaike information criterion (AIC) and R2 measure.


Subject(s)
COVID-19 , Video Games , Communicable Disease Control , Electroencephalography , Humans , Pandemics
SELECTION OF CITATIONS
SEARCH DETAIL