Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection

Petrescu, Livia; Petrescu, Cătălin; Mitruț, Oana; Moise, Gabriela; Moldoveanu, Alin; Moldoveanu, Florica; Leordeanu, Marius

doi:10.3390/s20247088

Cited by 36 publications

(33 citation statements)

References 95 publications

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“…Al-Ezzi et al achieved the accuracies of 92.86%, 92.86%, 96.43%, and 89.29% for severe, moderate, mild anxiety and HC by using a deep learning model (convolutional neural network + long short-term memory) with task-state EEG data, respectively [ 27 ]. Moreover, lower classification accuracies were reported using other modalities data [ 50 , 51 , 52 ], such as an accuracy of 87.4% with Self-Rating Anxiety Scale questionnaires data [ 12 ], and an accuracy of 86% with language-based features [ 53 ]. The above studies give us the following insight: better performance with our proposed EEG feature extraction and selection than the results of other existing studies indicates the importance of the EEG feature extraction process in classification problems.…”

Section: Discussionmentioning

confidence: 99%

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework

Shen

Fang

et al. 2022

Sensors

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Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized anxiety disorder (GAD). To this end, this study intends to develop an analysis framework for automatic GAD detection through incorporating multidimensional EEG feature extraction and machine learning techniques. Specifically, resting-state EEG signals with a duration of 10 min were obtained from 45 patients with GAD and 36 healthy controls (HC). Then, an analysis framework of multidimensional EEG characteristics (including univariate power spectral density (PSD) and fuzzy entropy (FE), and multivariate functional connectivity (FC), which can decode the EEG information from three different dimensions) were introduced for extracting aberrated multidimensional EEG features via statistical inter-group comparisons. These aberrated features were subsequently fused and fed into three previously validated machine learning methods to evaluate classification performance for automatic patient detection. We showed that patients exhibited a significant increase in beta rhythm and decrease in alpha1 rhythm of PSD, together with the reduced long-range FC between frontal and other brain areas in all frequency bands. Moreover, these aberrated features contributed to a very good classification performance with 97.83 ± 0.40% of accuracy, 97.55 ± 0.31% of sensitivity, 97.78 ± 0.36% of specificity, and 97.95 ± 0.17% of F1. These findings corroborate previous hypothesis of disconnectivity in psychiatric disorders and further shed light on distribution patterns of aberrant spatio-spectral EEG characteristics, which may lead to potential application of automatic diagnosis of GAD.

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Section: Discussionmentioning

confidence: 99%

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework

Shen

Fang

et al. 2022

Sensors

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“…For our study, we were interested in the non-specific SCRs (ns-SCRs), which are spontaneous, phasic increases in EDA that are not associated with any specific stimuli ( 62 ). An increased frequency of ns-SCRs is considered a biomarker of high arousal in situations of stress, emotional reactivity and anticipatory anxiety ( 46 , 62 , 69 ).…”

Section: Methodsmentioning

confidence: 99%

Targeted Memory Reactivation During REM Sleep in Patients With Social Anxiety Disorder

et al. 2022

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BackgroundSocial anxiety disorder (SAD) is characterized by a significant amount of fear when confronted to social situations. Exposure therapy, which is based on fear extinction, does not often lead to full remission. Here, based on evidence showing that rapid eye movement (REM) sleep promotes the consolidation of extinction memory, we used targeted memory reactivation (TMR) during REM sleep to enhance extinction learning in SAD.MethodsForty-eight subjects with SAD were randomly assigned to two groups: control or TMR group. All patients had two successive exposure therapy sessions in a virtual reality (VR) environment, where they were asked to give a public talk in front of a virtual jury. At the end of each session, and only in the TMR group (N = 24), a sound was paired to the positive feedback phase of therapy (i.e., approval of their performance), which represented the memory to be strengthened during REM sleep. All participants slept at home with a wearable headband device which automatically identified sleep stages and administered the sound during REM sleep. Participants' anxiety level was assessed using measures of parasympathetic (root mean square of successive differences between normal heartbeats, RMSSD) and sympathetic (non-specific skin conductance responses, ns-SCRs) activity, and subjective measures (Subjective Units of Distress Scale, SUDS), during the preparation phase of their talks before (T1) and after (T2) one full-night's sleep and after 1 week at home (T3). Participants also filled in a dream diary.ResultsWe observed an effect of time on subjective measures of anxiety (SUDS). We did not find any difference in the anxiety levels of the two groups after 1 week of TMR at home. Importantly, the longer the total duration of REM sleep and the more stimulations the TMR group had at home, the less anxious (increased RMSSD) these participants were. Finally, fear in dreams correlated positively with ns-SCRs and SUDS at T3 in the TMR group.ConclusionTMR during REM sleep did not significantly modulate the beneficial effect of therapy on subjective anxiety. Yet, our results support that REM sleep can contribute to extinction processes and substantiate strong links between emotions in dreams and waking stress levels in these patients.

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“…Considering pre-existing approaches, to further assure the safeness of the future users of VR for grief, psychophysiological monitoring systems might be included in such interventions. This inclusion has been already done in the case of adaptative physiological systems in protocols on phobias, stress, and PTSD (Ćosić et al, 2010;Gaggioli et al, 2014;Petrescu et al, 2020), or biofeedback in relaxation (Blum et al, 2019(Blum et al, , 2020.…”

Section: Possibilities and Potential Side Effects Of A Vr For Griefmentioning

confidence: 99%

From virtual to real healing: a critical overview of the therapeutic use of virtual reality to cope with mourning

et al. 2021

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In recent years, virtual reality (VR) has been effectively employed in several settings, ranging from health care needs to leisure and gaming activities. A new application of virtual stimuli appeared in social media: in the documentary ‘I met you’ from the South-Korean Munhwa Broadcasting, a mother made the experience of interacting with the avatar of the seven-year-old daughter, who died four years before. We think that this new application of virtual stimuli should open a debate on its possible implications: it represents contents related to grief, a dramatic and yet natural experience, that can have deep psychological impacts on fragile subjects put in virtual environments. In the present work, possible side-effects, as well as hypothetical therapeutical application of VR for the treatment of mourning, are discussed.

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Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection

Cited by 36 publications

References 95 publications

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework

Targeted Memory Reactivation During REM Sleep in Patients With Social Anxiety Disorder

From virtual to real healing: a critical overview of the therapeutic use of virtual reality to cope with mourning

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