Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms.

Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine.

Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.

With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.

Learning from feedback: Evaluation of dynamic decision-making in virtual reality under various repetitive training frameworks.

Dynamic decision-making involves a series of interconnected interdependent confluence of decisions to be made. Experiential training is preferred over traditional methods to train individuals in dynamic decision-making. Imparting experiential training in physical settings can be very expensive and unreliable. In virtual reality (VR), synthetic environments play a significant role in providing flexible and cost-effective training environments to enhance dynamic decision-making. However, it is still unclear how VR can be used to impart dynamic decision-making training to increase cognitive performance in complex situations. Besides, different repetitive training methods like desirable difficulty framework and heterogeneity of practice have been evaluated on generic cognitive and motor tasks. However, an evaluation of how these repetitive training methods facilitate dynamic decision-making in an individual in a virtual complex environment setting is lacking in the literature. The objective of this study is to evaluate the effect of different repetitive training methods in immersive VR on dynamic decision-making in a complex search-and-shoot environment. In a lab-based experiment, 66 healthy subjects are divided equally and randomly into three between-subject training conditions: heterogenous, difficult, and sham. On Day 1, all the participants, regardless of the condition, executed an environment of a baseline difficulty level. From Days 2 to 7, the participants alternatively executed the novice difficulty and expert difficulty versions of the environment in the heterogenous condition. In difficult conditions, the participants executed the expert difficulty version of the environment from Days 2 to 7. In the sham condition, the participants executed an unrelated VR environment from Days 2 to 7. On Day 8, the participants executed the baseline difficulty version of the environment again in all the conditions. Various performance and workload-based measures were acquired. Results revealed that the participants in the heterogenous and difficult conditions performed significantly better on Day 8 compared with Day 1. The results inferred that a combination of immersive VR environment with repetitive heterogenous training maximized performance and decreased cognitive workload at transfer. We expect to use these conclusions to create effective training environments in VR for imparting training to military personnel in dynamic decision-making scenarios.

Effective Stress Management through Meditation: An Electroencephalograph-Based Study.

Introduction: Stress among college students is a common health problem that is directly correlated with poor cognitive health. For instance, cognitive mechanisms required for sustenance can be affected due to stress caused by daily mundane events, not necessarily by chronic events. Thus, it becomes essential to manage stress effectively especially for college students. Meditation is one of the useful techniques that facilitates cognitive flexibility and has consequences at the molecular and endocrinal level to treat stress. Objectives: The present study attempts to understand the effect of meditation on the brain waves when participants face stressful events. Methods: A randomized controlled pre-post experimental design was used. Total 18 subjects were randomly assigned to control group and experimental group. Subsequently, Electroencephalograph (EEG) data were recorded during the determination test (DT) before and after the meditation. The Control group underwent relaxation music while the experimental group practiced Sudarshan Kriya Yoga (SKY) (a type of meditation). Non-linear EEG signal processing algorithm was applied to capture dynamics and complexity in brain waves. Results: Results indicated that the efficacy of meditation was reflected with the improved information processing in the brain. Improved performance and reduced errors were reported in DT Scores in the experimental group. Increased complexity of beta band was observed for non-linear features, signifying efficient utilization of cognitive resources while performing the task. Conclusion: Findings implicated the usefulness of the meditation process for effective stress management.

Where to display vital information? ERP evidence for background changes.

Critical decision systems require expeditious and accurate responses to the displayed information. In addition to content, location and background are equally important. Several visual search studies have pointed out the differences and compared modes of top-down attention allocation: distractor suppression and attentional capture. Previous studies have used color (mainly) and shape as a feature but have overlooked luminance as a feature for studying underlying attention mechanisms. The present study attempts to bridge this gap. In this study, participants performed a target-distractor discrimination task by identifying a randomly appearing target from the pool of distractors based on defined luminance levels. Background change was noticed by manipulating the task such as making visible quadrant boundaries over the screen. The preliminary evidence suggested that displaying information at the top-left of the screen had higher percentage accuracy; whereas, response time (RT) remained unaffected. Improvement in RT and percentage accuracy was observed with task manipulation. Event-related potential (ERP) analysis revealed elicited Distractor Positivity (PD), providing evidence for the distractor suppression hypothesis. Further, differences emerged in the topographic plot of N2pc and PD. In sum, the result contributes to classic debate of capture vs. suppression and provides a crucial connection between display design and electrophysiological indices, emphasizing locations and background as equally important factors.

Formulating a multi-layered framework for automobile architecture.

Product architecture is defined as allocation of components to functions and their interfaces. But what makes this formulation insufficient for automobile architecture is their multi-layered allocation of components and the visual aspect of the architecture. This paper suggests, through an empirical study of two-wheeler models being manufactured by a prominent manufacturer, a multi-tiered framework along with a visual template for two-wheeler architecture which includes their visual schema as well. This paper investigates and demonstrates that this framework and template satisfy the requirements of product architecture by using examples from the domain of motorcycle design. Further, this paper investigates the utility of this framework for platforming strategy, innovation, adoption of new technology and standardization.

Reliability and factor analysis of Hindi version of IES-R scale: Effect of Rajyoga meditation on perceiving the impact of COVID-19.

The purpose of this study is to present the Hindi translation and validation of the Impact of Event Scale-Revised and to evaluate psychometric qualities of this scale in a sample of regular Rajyoga meditators to examine the psychological impact of Coronavirus on them. The convenience sampling method was used to collect the data from 801 Rajyoga meditators through online survey. Data were analysed using SPSS 26.0. The Hindi version of IES-R demonstrated good internal consistency with the value of alpha coefficient being 0.91 for the scale and ranging between 0.81 to 0.83 for the subscales. The correlations between the subscales varied between 0.55 and 0.66. Principal components analysis using Varimax rotation was run with three-factor solution based on eigen value greater than one. This solution explained 54 percent of the total variance. It generated mainly two factors, an intrusion hyperarousal factor and an avoidance factor and third factor with one item only. Only 4.7 percent of the meditators rated the outbreak's psychological impact as moderate or severe. The mean score of IES-R was 10.01 (with an S.D. of 11.107). Significant positive correlations were found among IES-R scores and the presence of COVID symptoms. Thus, in clinical and research contexts, the scale appears to be a valid measure of post-trauma occurrences. The present study was conducted to generate a validated Hindi version of the IES-R that is easier and more compatible for use in the Indian population.

Effect of Complexity on Frontal Event Related Desynchronisation in Mental Rotation Task.

A complexity (orientation and shape of stimuli) in the mental rotation (MR) task often affects reaction time (RT) and response accuracy, but the nature of such reflections in neuroscientific research is commonly undocumented. A number of studies have explored the effect of complexity and subsequently noted down the differences in performance. However, a few studies explored complexity (in the term of angular disparity) and cognitive strategies with respect to correct responses only. In contrast, the present study investigated frontal alpha desynchronization with reference to the complexity and proportions of correct and incorrect responses. Behavioral and neurophysiological responses were investigated to understand the switching between strategies (Analytic vs. Holistic). Results showed longer response time with respect to increased complexity. Frontal alpha desynchronization increased for difficult trials and incorrect responses, suggesting a higher utilization of cognitive resources at the frontal region during the MR task. Higher left frontal desynchronization reflected a trading off between strategies for difficult trials. Taken together, these findings suggest that the effect of stimuli complexity is more nuanced than implied by a simple hemispheric dichotomy for frontal cortex and discuss possible future directions to better understand the multitudinous brain mechanisms involved in MR.

Desktop VR Is Better Than Non-ambulatory HMD VR for Spatial Learning.

Use of virtual reality (VR) technology is proliferating for designing and upgrading entertainment devices, and creating virtual environments that could be used for research and training. VR is becoming a strong research tool by providing a tighter control on the experimental environment and by allowing almost limitless possibilities of creating ecologically valid stimuli. However, the enhanced fidelity between the real and virtual worlds that VR provides does not always benefit human performance. For a better understanding, and increasing VR's usability, we need to identify the relevant constituent components of immersive technologies, and differentiate their roles, for example, how visual and interaction fidelity differentially improves human performance. We conducted an experiment to examine how two common VR display modes, head mounted display (HMD) and desktop (DT), would affect spatial learning when we restrict ambulatory locomotion in HMD. This manipulation allowed examining the role of varying visual fidelity with low interaction fidelity. We used a between-group design with 40 naïve participants. They explored a virtual environment and later drew its sketch-map. Our results showed participants spent more time and perceived less motion-sickness and task effort using desktop than HMD VR. With reduced interaction fidelity, the high visual fidelity of HMD as compared to desktop resulted in similar or poorer performance on different spatial learning tasks after accounting for motion-sickness and workload effort. Participants were better in recalling spatial components related to junction and cyclic order of the navigated virtual space in desktop vs. HMD VR, and performed equally well on components related to street segments and object associations. We explain these results in terms of deficient idiothetic information in non-ambulatory HMD and lesser sensory conflicts in desktop mode. Overall, our results highlight the differential effect of visual vs. interaction fidelity on human performance based on using immersive technologies, how such an effect depends on the nature of cognitive and functional behavior users employ, and the higher usability of traditional desktop VR. These results are relevant for developing customized and sustainable virtual reality based human-computer interactions.

Exploration of Lower Frequency EEG Dynamics and Cortical Alpha Asymmetry in Long-term Rajyoga Meditators.

BACKGROUND: Rajyoga meditation is taught by Prajapita Brahmakumaris World Spiritual University (Brahmakumaris) and has been followed by more than one million followers across the globe. However, rare studies were conducted on physiological aspects of rajyoga meditation using electroencephalography (EEG). Band power and cortical asymmetry were not studied with Rajyoga meditators. AIMS: This study aims to investigate the effect of regular meditation practice on EEG brain dynamics in low-frequency bands of long-term Rajyoga meditators. SETTINGS AND DESIGN: Subjects were matched for age in both groups. Lower frequency EEG bands were analyzed in resting and during meditation. MATERIALS AND METHODS: Twenty-one male long-term meditators (LTMs) and same number of controls were selected to participate in study as par inclusion criteria. Semi high-density EEG was recorded before and during meditation in LTM group and resting in control group. The main outcome of the study was spectral power of alpha and theta bands and cortical (hemispherical) asymmetry calculated using band power. STATISTICAL ANALYSIS: One-way ANOVA was performed to find the significant difference between EEG spectral properties of groups. Pearson's Chi-square test was used to find difference among demographics data. RESULTS: Results reveal high-band power in alpha and theta spectra in meditators. Cortical asymmetry calculated through EEG power was also found to be high in frontal as well as parietal channels. However, no correlation was seen between the experience of meditation (years, hours) practice and EEG indices. CONCLUSION: Overall findings indicate contribution of smaller frequencies (alpha and theta) while maintaining meditative experience. This suggests a positive impact of meditation on frontal and parietal areas of brain, involved in the processes of regulation of selective and sustained attention as well as provide evidence about their involvement in emotion and cognitive processing.

Enhanced White Matter Integrity in Corpus Callosum of Long-Term Brahmakumaris Rajayoga Meditators.

Meditation has a versatile nature to affect cognitive functioning of human brain. Recent researches demonstrated its effects on white matter (WM) properties of human brain. In this research, we aim to investigate WM microstructure of corpus callosum (CC) in long-term meditators (LTMs) of rajayoga meditation using diffusion tensor imaging. For this cross-sectional analysis, 22 LTMs and 17 control participants of age ranging from 30 to 50 years were recruited. Results show high fractional anisotropy values with low mean diffusivity in whole as well as different segments of CC in the LTM group. Also the experience of meditation was correlated with WM properties of CC tracts. Findings may suggest rajayoga meditation to bring potential changes in microstructure of CC segments. Further studies are suggested in clinical population to check its validity and efficacy against disorders involving agenesis of WM.

Corrigendum: Influence of Landmarks on Wayfinding and Brain Connectivity in Immersive Virtual Reality Environment.

[This corrects the article on p. 1220 in vol. 8, PMID: 28775698.].

Influence of Landmarks on Wayfinding and Brain Connectivity in Immersive Virtual Reality Environment.

Spatial navigation is influenced by landmarks, which are prominent visual features in the environment. Although previous research has focused on finding advantages of landmarks on wayfinding via experimentation; however, less attention has been given to identifying the key attributes of landmarks that facilitate wayfinding, including the study of neural correlates (involving electroencephalogram, EEG analyses). In this paper, we combine behavioral measures, virtual environment, and EEG signal-processing to provide a holistic investigation about the influence of landmarks on performance during navigation in a maze-like environment. In an experiment, participants were randomly divided into two conditions, Landmark-enriched (LM+; N = 17) and Landmark-devoid (LM-; N = 18), and asked to navigate from an initial location to a goal location in a maze. In the LM+ condition, there were landmarks placed at certain locations, which participants could use for wayfinding in the maze. However, in the LM- condition, such landmarks were not present. Beyond behavioral analyses of data, analyses were carried out of the EEG data collected using a 64-channel device. Results revealed that participants took less time and committed fewer errors in navigating the maze in the LM+ condition compared to the LM- condition. EEG analyses of the data revealed that the left-hemispheric activation was more prominent in the LM+ condition compared to the LM- condition. The event-related desynchronization/synchronization (ERD/ERS) of the theta frequency band, revealed activation in the left posterior inferior and superior regions in the LM+ condition compared to the LM- condition, suggesting an occurrence of an object-location binding in the LM+ condition along with spatial transformation between representations. Moreover, directed transfer function method, which measures information flow between two regions, showed a higher number of active channels in the LM- condition compared to the LM+ condition, exhibiting additional wiring cost associated with the cognitive demands when no landmark was available. These findings reveal pivotal role of the left-hemispheric region (especially, parietal cortex), which indicates the integration of available sensory cues and current memory requirements to encode contextual information of landmarks. Overall, this research helps to understand the role of brain regions and processes that are utilized when people use landmarks in navigating maze-like environments.

Brain connectivity during encoding and retrieval of spatial information: individual differences in navigation skills.

Emerging evidence suggests that the variations in the ability to navigate through any real or virtual environment are accompanied by distinct underlying cortical activations in multiple regions of the brain. These activations may appear due to the use of different frame of reference (FOR) for representing an environment. The present study investigated the brain dynamics in the good and bad navigators using Graph Theoretical analysis applied to low-density electroencephalography (EEG) data. Individual navigation skills were rated according to the performance in a virtual reality (VR)-based navigation task and the effect of navigator's proclivity towards a particular FOR on the navigation performance was explored. Participants were introduced to a novel virtual environment that they learned from a first-person or an aerial perspective and were subsequently assessed on the basis of efficiency with which they learnt and recalled. The graph theoretical parameters, path length (PL), global efficiency (GE), and clustering coefficient (CC) were computed for the functional connectivity network in the theta and alpha frequency bands. During acquisition of the spatial information, good navigators were distinguished by a lower degree of dispersion in the functional connectivity compared to the bad navigators. Within the groups of good and bad navigators, better performers were characterised by the formation of multiple hubs at various sites and the percentage of connectivity or small world index. The proclivity towards a specific FOR during exploration of a new environment was not found to have any bearing on the spatial learning. These findings may have wider implications for how the functional connectivity in the good and bad navigators differs during spatial information acquisition and retrieval in the domains of rescue operations and defence systems.

Mental Stress: Neurophysiology and Its Regulation by Sudarshan Kriya Yoga.

AIM: The present study focuses on analyzing the effects of Sudarshan Kriya yoga (SKY) on EEG as well as ECG signals for stress regulation. To envision the regulation of stress Determination Test (DT) has been used. We have chosen a control group for contriving a cogent comparison that could be corroborated using statistical tests. SUBJECTS AND METHODS: A total of 20 subjects were taken in the study, of which 10 were allotted to a control group. Electroencephalograph was taken during a DT task, before and after SKY the sky session with 30 days of SKY session given to the experimental group. No SKY was given to the control group. RESULTS: We quantified mental stress using EEG, ECG and DT synergistically and used SKY to regulate it. We observed that alpha band power decreases in the frontal lobe of the brain with increasing mental stress while frontal brain asymmetry decreases with increasing stress tolerance. CONCLUSIONS: These EEG, ECG and DT shows a significant decrement in mental stress and improvement in cognitive performance after SKY, indicating SKY as a good alternative of medication for stress management.

Workload regulation by Sudarshan Kriya: an EEG and ECG perspective.

Sudarshan Kriya Yoga (SKY) is a type of rhythmic breathing activity, trivially a form of Pranayama that stimulates physical, mental, emotional, and social well-being. The objective of the present work is to verify the effect of meditation in optimizing task efficiency and regulating stress. It builds on to quantitatively answer if SKY will increase workload tolerance for divided attention tasks in the people sank in it. EEG and ECG recordings were taken from a total of twenty-five subjects who had volunteered for the experiment. Subjects were randomly assigned to two groups of 'control' and 'experimental.' Their objective scores were collected from the experiment based on NASA's multi-attribute task battery II and was utilized for workload assessment. Both the groups had no prior experience of SKY. The experimental group was provided with an intervention of SKY for a duration of 30 min everyday. Pre- and post-meditation data were acquired from both groups over a period of 30 and 90 days. It was observed that subjective score of workload (WL) was significantly reduced in the experimental group and performance of the subject increased in terms of task performance. Another astute observation included a considerable increase and decrease in the alpha and beta energies and root mean square of the EEG signal for the experimental group and control group, respectively. In addition to this sympathovagal balance index also decreased in experimental group which indicated reduction in stress. SKY had an effect on stress regulation which in turn enhanced their WL tolerance capacity for a particular multitask activity.

Effect of Sudarshan Kriya (meditation) on gamma, alpha, and theta rhythm during working memory task.

AIMS: The present study focuses on analyzing the effects of Sudarshan Kriya yoga (SKY) on brain signals during a working memory (WM) task. To envision the significant effects of SKY on WM capacity (WMC), we chose a control group for contriving a cogent comparison that could be corroborated using statistical tests. SUBJECTS AND METHODS: A total of 25 subjects were taken in the study, of which 10 were allotted to a control group and 15 to an experimental group. Electroencephalograph was taken during a WM task, which was an automated operation span test before and after SKY with 90 days intervals. No SKY was given to the control group. STATISTICAL ANALYSIS USED: t-test and one-way ANOVA were applied. RESULTS: SKY promoted the efficient use of energy and power spectral density (PSD) for different brain rhythms in the desired locations as depicted by the gamma (F8 channel), alpha, and theta 2 (F7 and FC5) bands. It was found that gamma PSD reduced for both phases of memory in the experimental group. Alpha energy increased during the retrieval phase in the experimental group after SKY. Theta 1 rhythm was not affected by SKY, but theta 2 had shown left hemispheric activation. Theta rhythm was associated with memory consolidation. CONCLUSIONS: SKY had shown minimized energy losses while performing the task. SKY can improve WMC by changing the brain rhythms such that energy is utilized efficiently in performing the task.

A Diffusion Tensor Imaging Study Using a Voxel-Based Analysis, Region-of-Interest Method to Analyze White Matter Abnormalities in First-Episode, Treatment-Naïve Major Depressive Disorder.

This study used fractional anisotropy (FA), a measure of diffusion tensor imaging, to analyze white matter abnormalities in 15 first-episode treatment-naïve patients with major depressive disorder (MDD) and 15 controls matched for age. An automated voxel-based analysis and a region-of-interest (ROI) method with 3T magnetic resonance imaging were used. Compared with controls, FA values were lower in the left superior longitudinal fasciculus, the left prefrontal cortex, and the left parietal region in patients with MDD. In addition, the ROI method revealed significantly lower FA values in the right hippocampus. Voxel-based analysis, a faster technique, complements the ROI method, which highlights FA values as potential biomarkers in early MDD.