The onset of the COVID-19 pandemic has led to an increased demand for mental health interventions, with a special focus on digitally assisted ones. Self-regulation describes a set of meta-skills that enable one to take control over his/her mental health and it is recognized as a vital indicator of well-being. Mindfulness training is a promising training strategy for promoting self-regulation, behavioral change, and mental well-being. A growing body of research outlines that smart technologies are ready to revolutionize the way mental health training programs take place. Artificial intelligence (AI); extended reality (XR) including virtual reality (VR), augmented reality (AR), and mixed reality (MR); as well as the advancements in brain computer interfaces (BCIs) are ready to transform these mental health training programs. Mindfulness-based interventions assisted by smart technologies for mental, emotional, and behavioral regulation seem to be a crucial yet under-investigated issue. The current systematic review paper aims to explore whether and how smart technologies can assist mindfulness training for the development of self-regulation skills among people at risk of mental health issues as well as populations with various clinical characteristics. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions using a total of sixty-six experimental studies that met the inclusion criteria. The results showed that digitally assisted mindfulness interventions supported by smart technologies, including AI-based applications, chatbots, virtual coaches, immersive technologies, and brain-sensing headbands, can effectively assist trainees in developing a wide range of cognitive, emotional, and behavioral self-regulation skills, leading to a greater satisfaction of their psychological needs, and thus mental wellness. These results may provide positive feedback for developing smarter and more inclusive training environments, with a special focus on people with special training needs or disabilities.

Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review

The purpose of this study was to examine whether a virtual reality plus neurofeedback (VR+NF) meditation experience (experimental condition) was more effective than a standard guided audio-only meditation (control condition) in improving mood in one hundred healthcare workers. Data collection occurred in a hospital setting between October, 2020 and March, 2021 at the height of the COVID-19 pandemic. Participants were alternately assigned to one of the two conditions. Before and after the meditation experience, participants completed the Brunel Mood Scale. Results indicated that both groups showed a similar and significant decrease in Anger, Tension, and Depression. On scales measuring Vigor, Fatigue, and Confusion, the VR+NF group showed decreases, while the audio-only group showed no significant change. The VR+NF group showed significant increases on the Calmness and Happiness scales, which did not change significantly in the audio-only group. These results suggest that the addition of VR and neurofeedback may increase the positive outcomes associated with standard audio-guided meditation. These increased benefits may be due to the sense of presence intrinsic to VR, the inclusion of nature-based scenes in the VR experience, as well as the increased self-awareness created by the addition of neurofeedback. As the pre and post measures take place within one 50-min session, further studies assessing the longer-term changes are needed.

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A Feasibility Test of a Brief Mobile Virtual Reality Meditation for Frontline Healthcare Workers in a Hospital Setting

This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP).We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario.For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively.Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

Brain–Computer Interfaces (BCIs) are specialized systems that allow users to control computer applications using their brain waves. With the arrival of consumer-grade electroencephalography (EEG) equipment, brain-controlled systems began to find fertile ground in mental training. One particular area that is gradually gaining attention is that of mindfulness training. In this paper, the results of a literature review of BCI-assisted mindfulness training using BCI’s are presented. The specific aim is to review the effects of BCIs embedded in mindfulness interventions on training metacognitive, emotional, and attention regulation skills. Papers published the last 10 years were reviewed. The results showed that the use of BCIs provides subjects the unique opportunity to self-regulate mental and emotional functions using the feedback derived from their own brain activity. Subjects were found to raise better awareness about the ways non-conscious operations influence mental and emotional states. It was observed that subjects by learning to deal with the neurofeedback within immersive worlds or with the aid of mobile devices can better develop awareness and self-regulation skills including inhibition and flexibility. Learning environments have been undergoing rapid change driven by the evolution and availability of digital technologies. In that vein, BCIs combined with mobiles and immersive technologies could support mindfulness as an innovative practice for cognitive, emotional, and metacognitive development. This study aims to contribute to the debate about the use of BCI-assisted mindfulness practices as proactive methods and training strategies for various target groups such as students, teachers, and workers to achieve well-being and peak performance.

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Brain-computer interfaces in digital mindfulness training for metacognitive, emotional and attention regulation skills: a literature review

The holistic growth and psychological well-being of people with special needs and disabilities remain high on the priority agenda for sustainable and inclusive education. Digital well-being technologies and especially “smart technologies”, are ready to revolutionize mental health interventions by meeting trainees’ needs and providing them with more positive and transformative mental, emotional, and social experiences. Μeta-skills refer to a set of consciousness-raising competences that incorporate meta-cognitive, social–emotional, and motivational attributes, allowing individuals to intentionally achieve a state of optimal functioning. Although positive psychology and well-being technologies are considered promising intervention approaches, there is less knowledge regarding the effectiveness of such interventions among people with special needs and disabilities, especially in the crucial domain of meta-skills development. Thus, the current systematic review aims to examine positive psychology strategies as well as the synergy with well-being technologies in the development of metacognition, emotional intelligence, and motivation meta-skills in populations with special training needs and disabilities. The PRISMA methodology was utilized to answer the research questions. A total of forty-nine studies met the inclusion criteria. The results indicated that positive psychology strategies improved a wide range of meta-skills, including self-regulation, emotional control, behavioral control, inhibition control, self-awareness, intrapersonal skills, interpersonal skills, adaptation, goal setting, and self-compassion. Artificial intelligence tools, wearables, smart applications, immersive technologies (virtual and augmented reality), neurofeedback and biofeedback technologies, as well as digital games were found to effectively assist such training programs. The results of the current review may provide positive feedback in the discussion about digitally-aided mental health interventions for training the meta-skills of mental and emotional health.

Well-Being Technologies and Positive Psychology Strategies for Training Metacognition, Emotional Intelligence and Motivation Meta-Skills in Clinical Populations: A Systematic Review

Research and design of virtual reality technologies with mental health focused application has increased dramatically in recent years. However, the applications and psychological outcomes of Augmented Reality (AR) technologies are remain to be as widely explored and evaluated. This is particularly true for the use of AR for self-management of stress, anxiety, and mood. In the current study, we examined the impact of a brief Open Heart meditation AR experience on participants with moderate levels of anxiety and/or depression. Using a randomized between-group design subjects participated in the AR experience or the AR experience plus frontal Gamma asymmetry neurofeedback integrated into the experience. Self-reported mood state and resting-state EEG were recorded before and after the AR intervention for both groups. Participants also reported on engagement and perceived use of the experience as a stress and coping tool. EEG activity was analyzed as a function of frontal, midline and parietal scalp regions, and with sLORETA current source density estimates of anterior cingulate and insular cortical regions of interest. Results demonstrated that both versions of the AR meditation significantly reduced negative mood and increased positive mood. The changes in resting state EEG were also comparable between groups, with some trending differences observed, in line with existing research on Open Heart and other loving-kindness and compassion-based meditations. Engagement was favorable for both versions of the AR experience, with higher levels of engagement reported with the addition of neurofeedback. These results provide early support for the therapeutic potential of AR integrated meditations as a tool for self-regulation of mood and emotion, and sets the stage for more research and development into health and wellness promoting AR applications.

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Effects on Mood and EEG States After Meditation in Augmented Reality With and Without Adjunctive Neurofeedback

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Effects on Mood and EEG States After Meditation in Augmented Reality With and Without Adjunctive Neurofeedback