Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review.
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ABSTRACT:
SUBMITTER: Souza RHCE
PROVIDER: S-EPMC8650681 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesDelivery of virtual care increased throughout the COVID-19 pandemic and persisted after physical distancing measures ended. However, little is known about how to measure the quality of virtual care, as current measures focus on in-person care and may not apply to a virtual context. This scoping review aims to understand the connections between virtual care modalities used with ambulatory patient populations and quality measures across the Quintuple Aim (provider experience, patient experience, per capita cost, population health and health equity).DesignVirtual care was considered any interaction between patients and/or their circle of care occurring remotely using any form of information technology. Five databases (MEDLINE, Embase, PsycInfo, Cochrane Library, JBI) and grey literature sources (11 websites, 3 search engines) were searched from 2015 to June 2021 and again in August 2022 for publications that analysed virtual care in ambulatory settings. Indicators were extracted, double-coded into the Quintuple Aim framework; patient and provider experience indicators were further categorised based on the National Academy of Medicine quality framework (safety, effectiveness, patient-centredness, timeliness, efficiency and equity). Sustainability was added to capture the potential for continued use of virtual care.Results13 504 citations were double-screened resulting in 631 full-text articles, 66 of which were included. Common modalities included video or audio visits (n=43), remote monitoring (n=11) and mobile applications (n=11). The most common quality indicators were related to patient experience (n=58 articles), followed by provider experience (n=25 articles), population health outcomes (n=23 articles) and health system costs (n=19 articles).ConclusionsThe connections between virtual care modalities and quality domains identified here can inform clinicians, administrators and other decision-makers how to monitor the quality of virtual care and provide insights into gaps in current quality measures. The next steps include the development of a balanced scorecard of virtual care quality indicators for ambulatory settings to inform quality improvement.
| S-EPMC10989112 | biostudies-literature
Project description:The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.
| S-EPMC6562774 | biostudies-literature
Project description:Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet-based bandwidth specific feature analysis from the time-frequency domain. Then, a wrapper-based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the non-redundant features. Finally, the k-nearest neighbor (k-NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non-linear dimensionality reduction techniques, as well as those without feature ranking.
| S-EPMC6956373 | biostudies-literature
Project description:In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.
| S-EPMC8194273 | biostudies-literature
Project description:ObjectiveLocalization of epileptic seizures, usually characterized by abnormal hypersynchronous wave patterns from the cortex, remains elusive. We present a novel, robust method for automatic localization of seizures on the scalp from clinical electroencephalogram (EEG) data.MethodsSeizure patient EEG data was decomposed via the Hilbert Transform and processed through the following methodology: sorting the analytic amplitude (AA) in the time instance, locating the maximum amplitude within the vector of channels, cross-correlating amplitude values in the time index with the channel vector. The channel with highest AA value in time was located.ResultsOur approach provides an automated way to isolate the epi-genesis of seizure events with 93.3% precision and 100% sensitivity. The method differentiates seizure-related neural activity from other common EEG noise artifacts (e.g., blinks, myogenic noise).ConclusionsWe evaluated performance characteristics of our source location methodology utilizing both phase and energy of EEG signals from patients who exhibited seizure events. Feasibility of the new algorithm is demonstrated and confirmed.SignificanceThe proposed method contributes to high-performance scalp localization for seizure events that is more straightforward and less computationally intensive than other methods (e.g., inverse source modeling). Ultimately, it may aid clinicians in providing improved patient diagnosis.
| S-EPMC7437564 | biostudies-literature
Project description:Epilepsy is a chronic chaos of the central nervous system that influences individual's daily life by putting it at risk due to repeated seizures. Epilepsy affects more than 2% people worldwide of which developing countries are affected worse. A seizure is a transient irregularity in the brain's electrical activity that produces disturbing physical symptoms such as a lapse in attention and memory, a sensory illusion, etc. Approximately one out of every three patients have frequent seizures, despite treatment with multiple anti-epileptic drugs. According to a survey, population aged 65 or above in European Union is predicted to rise from 16.4% (2004) to 29.9% (2050) and also this tremendous increase in aged population is also predicted for other countries by 2050. In this paper, seizure detection techniques are classified as time, frequency, wavelet (time-frequency), empirical mode decomposition and rational function techniques. The aim of this review paper is to present state-of-the-art methods and ideas that will lead to valid future research direction in the field of seizure detection.
| S-EPMC6170938 | biostudies-other
Project description:PURPOSE:The purported affective impact of virtual reality (VR) and active video gaming (AVG) systems is a key marketing strategy underlying their use in stroke rehabilitation, yet little is known as to how affective constructs are measured or linked to intervention outcomes. The purpose of this scoping review is to 1) explore how motivation, enjoyment, engagement, immersion and presence are measured or described in VR/AVG interventions for patients with stroke; 2) identify directional relationships between these constructs; and 3) evaluate their impact on motor learning outcomes. METHODS:A literature search was undertaken of VR/AVG interventional studies for adults post-stroke published in Medline, PEDro and CINAHL databases between 2007 and 2017. Following screening, reviewers used an iterative charting framework to extract data about construct measurement and description. A numerical and thematic analytical approach adhered to established scoping review guidelines. RESULTS:One hundred fifty-five studies were included in the review. Although the majority (89%; N = 138) of studies described at least one of the five constructs within their text, construct measurement took place in only 32% (N = 50) of studies. The most frequently described construct was motivation (79%, N = 123) while the most frequently measured construct was enjoyment (27%, N = 42). A summative content analysis of the 50 studies in which a construct was measured revealed that constructs were described either as a rationale for the use of VR/AVGs in rehabilitation (76%, N = 38) or as an explanation for intervention results (56%, N = 29). 38 (76%) of the studies proposed relational links between two or more constructs and/or between any construct and motor learning. No study used statistical analyses to examine these links. CONCLUSIONS:Results indicate a clear discrepancy between the theoretical importance of affective constructs within VR/AVG interventions and actual construct measurement. Standardized terminology and outcome measures are required to better understand how enjoyment, engagement, motivation, immersion and presence contribute individually or in interaction to VR/AVG intervention effectiveness.
| S-EPMC6598261 | biostudies-literature
Project description:Problem presentation in problem-based learning can include the use of videos and interactive virtual patients. This review scopes the literature for this variation and what benefits or pitfalls there may be to their use. Themes indicate that videos and virtual patients may better prepare students for future difficult clinical interactions, while also increasing authenticity and memorability of cases. Findings are more inconsistent in determining whether they lead to clear knowledge or critical thinking gains. Despite inconsistent data, in an age where the use of technology is inevitable, the findings of this scoping review can inform future practice and guide innovation.
| S-EPMC8368294 | biostudies-literature
Project description:Background and purposeMachine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method.MethodA public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator. Therefore, the presented feature extractor was named as the cyclic group of prime order pattern, CGP17Pat. Using the proposed CGP17Pat, a new multilevel feature extraction model is presented. To choose a highly distinctive feature, iterative neighborhood component analysis (INCA) was used, and these features were classified using k-nearest neighbors (kNN) with the 10-fold cross-validation and leave-one-subject-out (LOSO) validation techniques. Finally, iterative hard majority voting was employed in the last phase to obtain channel-wise results, and the general results were calculated.ResultsThe presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy.ConclusionsThe findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used.
| S-EPMC9027158 | biostudies-literature
Project description:The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the degree of flexibility required by the scientific community. Here we present a microservice-oriented methodology, where scientific applications run in a distributed orchestration platform as software containers, referred to as on-demand, virtual research environments. The methodology is vendor agnostic and we provide an open source implementation that supports the major cloud providers, offering scalable management of scientific pipelines. We demonstrate applicability and scalability of our methodology in life science applications, but the methodology is general and can be applied to other scientific domains.
| S-EPMC7924445 | biostudies-literature