EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review

Interconnections and global transitions among functional states encode activity-related dynamics as brain topology changes after yoga training

With the emphasis on sustainable health, understanding the neural dynamics associated with sustainable practices such as widely practiced yoga has gained significant importance. In this work, we explored the underlying neural mechanisms of yoga training by means of electroencephalogram recordings. The EEG data was recorded before and after the yoga training of 13 participants, for a total of 39 trials, with each trial recorded on consecutive days. The temporal analysis was performed by means of microstates and the changes in the oscillatory rhythms were also evaluated via spectral and statistical analysis. Spectral analysis revealed changes in the oscillatory rhythms of β,γ,α,θ over the electrode regions of O2, P8 and FC6. An analysis of the changes in the temporal microstates revealed > 65% global variance in the topographic clusters, with a significant effect on the occurrence and time coverage parameters of the microstates before and after yoga training. This study highlights that yoga training significantly influences microstate dynamics associated with brain regions, including the visual network, insular cortex, and frontal gyrus, thereby potentially enhancing functions related to attention and cognitive decisions. These findings may suggest a multinetwork neurophysiological basis for the role of yoga in improving mental focus and adaptive decision processes.

The Science of Tai Chi and Qigong as Whole Person Health- Part II: Evidence Gaps and Opportunities for Future Research and Implementation

Background: The emerging paradigm of whole person health shares many core principles with traditional complementary and integrative health frameworks, including Tai Chi and Qigong (TCQ). Methods: In the fall of 2023, the Harvard Medical School Osher Center for Integrative Health hosted the inaugural international conference on The Science of Tai Chi & Qigong for Whole Person Health: Advancing the Integration of Mind-Body Practices into Contemporary Healthcare at Harvard Medical School. A two-part white paper was written to summarize key conference topics, findings, and issues. Results and Discussion: Part II presented here summarizes evidence gaps and future research opportunities, including: understudied clinical conditions and populations, impact of long-term TCQ training, understanding the impact of specific TCQ styles, training regimens, dosage, and contextual effects; implementation, cost-effectiveness, and medical utilization research; individual data meta-analysis, and teaching competencies, credentialing, and licensure. Part I of this white paper discusses the rationale for the conference, synthesizes the state of evidence for TCQ as rehabilitative and preventive tools for a range of clinical conditions, and summarizes the translational research informing therapeutic mechanisms associated with TCQ training.

Decoding Mindfulness With Multivariate Predictive Models

Identifying the brain mechanisms that underlie the salutary effects of mindfulness meditation and related practices is a critical goal of contemplative neuroscience. Here, we suggest that the use of multivariate predictive models represents a promising and powerful methodology that could be better leveraged to pursue this goal. This approach incorporates key principles of multivariate decoding, predictive classification, and model-based analyses, all of which represent a strong departure from conventional brain mapping approaches. We highlight 2 such research strategies-state induction and neuromarker identification-and provide illustrative examples of how these approaches have been used to examine central questions in mindfulness, such as the distinction between internally directed focused attention and mind wandering and the effects of mindfulness interventions on somatic pain and drug-related cravings. We conclude by discussing important issues to be addressed with future research, including key tradeoffs between using a personalized versus population-based approach to predictive modeling.

ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the field of meditation and contemplative neuroscience.

Oscillating Mindfully: Using Machine Learning to Characterize Systems-Level Electrophysiological Activity During Focused Attention Meditation

Background

There has been rapid growth of neuroelectrophysiological studies that aspire to uncover the "black box" of mindfulness and meditation. Reliance on traditional data analysis methods hinders understanding of the complex, nonlinear, multidimensional, and systemic nature of the functional neuroelectrophysiology of meditation states.

Methods

Thus, to reveal the complex systemic neuroelectrophysiology of meditation, we applied a machine learning extreme gradient boosting classification algorithm and 4 complementary feature importance methods to extract systemic electroencephalography features characterizing mindful states from electroencephalography recorded during a focused attention meditation and a control mind-wandering state among 26 experienced meditators.

Results

The algorithm classified meditation versus mind-wandering states with 83% accuracy, with an area under the receiver operating characteristic curve of 79% and F1 score of 74%. Feature importance techniques identified 10 electroencephalography features associated with increased power and coherence of high-frequency oscillations during focused attention meditation relative to an instructed mind-wandering state.

Conclusions

The findings help delineate the complex systemic oscillatory activity that characterizes meditation.

Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline

This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. Here, two meditation techniques, LKM-Self and LKM-Others were compared with non-meditation EEG data for 12 participants. Among many tested, three BCI pipelines we built produced promising results, successfully detecting features in meditation/ non-meditation EEG data. While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. As a novel concept, the third BCI pipeline used a feature extraction algorithm that fused the features of CSP and STFT, achieving the highest classification accuracies among all tested pipelines. Analyses were conducted using EEG data of 3, 4 or 5 sessions, totaling 3960 tests on the entire dataset. At the end of the study, when considering all the tests, the overall classification accuracy using SCP alone was 67.1%, and it was 67.8% for STFT alone. The algorithm combining the features of CSP and STFT achieved an overall classification accuracy of 72.9% which is more than 5% higher than the other two pipelines. At the same time, the highest mean classification accuracy for the 12 participants was achieved using the pipeline with the combination of CSP STFT algorithm, reaching 75.5% for LKM-Self/ non-meditation for the case of 5 sessions of data. Additionally, the highest individual classification accuracy of 88.9% was obtained by the participant no. 14. Furthermore, the results showed that the classification accuracies for all three pipelines increased with the number of training sessions increased from 2 to 3 and then to 4. The study was successful in classifying a new session of EEG meditation/ non-meditation data after training machine learning algorithms using a different set of session data, and this achievement will be beneficial in the development of algorithms that support meditation.

The Role of Music in Psychedelic-Assisted Therapy: A Comparative Analysis of Neuroscientific Research, Indigenous Entheogenic Ritual, and Contemporary Care Models

Music is deeply rooted in the human experience as well as a fundamental part of psychedelic-assisted therapies (PAT) and entheogenic ceremonies. Although a large body of research exists highlighting the importance of music from rehabilitative, psychological, neurobiological, anthropological, religious, and sociological contexts, there is limited scientific literature regarding the specific relevance of music in PAT and indigenous entheogenic ritual as a means of enhancing clinical outcomes. As demand for mental health services continues to grow and awareness of the medicinal benefits of psychedelic substances to treat mental and neurological conditions increases, a new wave of interest has emerged to support the development of care models, including how music is used during PAT. Music is a reliable cornerstone in therapeutic and ritualistic spaces using psychedelics, however there is still an immense opportunity to cultivate PAT models with interdisciplinary, evidence-informed perspectives and thoughtful analysis of music use in treatment. To contribute to this development, this review evaluates neuroscientific, psychological, and anthropological research on the neural and cognitive underpinnings of music as well as music use with psychedelics both in modern research settings and indigenous entheogenic ceremonies. In addition, personalized approaches to music protocols in PAT, how music use in traditional rituals may help inform best practices, and the need for researchers to specify music protocols in treatment models are detailed. Consideration of carefully respecting the bridging of indigenous practices and current medical models is discussed to highlight areas for future development.

Exploring neural markers of dereification in meditation based on EEG and personalized models of electrophysiological brain states

With mounting evidence for the benefits of meditation, there has been a growing interest in measuring and quantifying meditative states. This study introduces the Inner Dereification Index (IDI), a class of personalized models designed to quantify the distance from non-meditative states such as mind wandering based on a single individual's neural activity. In addition to demonstrating high classification accuracy (median AUC: 0.996) at distinguishing meditation from thinking states moment by moment, IDI can accurately stratify meditator cohorts by experience, and correctly identify the practices most effective at training the dereification aspect of meditation (decentering from immersion with thoughts and perceptions and recognizing them as mental constructs). These results suggest that IDI models may be a useful real-time proxy for dereification and meditation progress, requiring only 1 min of mind wandering data (and no meditation data) during model training. Thus, they show promise for applications such as real-time meditation feedback, progress tracking, personalization of practices, and potential therapeutic applications of neurofeedback-assisted generation of positive states of consciousness.

Network analysis of meditative states in highly skilled meditators using EEG and horizontal visibility graphs

The benefits of meditation are increasingly recognized, and some forms are now used for clinical intervention. However, the electrophysiological correlates of meditative states are not yet well understood, and the limited predictive accuracy of known markers of meditation suggest that not all information relevant to meditation has been captured by previous work.Here, we convert electroencephalography (EEG) time series into scale-free networks using horizontal visibility graphs (HVGs), which are well-suited to distinguishing deterministic dynamical systems from stochastic systems, allowing them to model novel aspects of cortical oscillatory activity. Based on HVGs, we introduce and evaluate a general class of predictors, which can be used to augment existing features in contemplative neuroscience, and exhibit high predictive power for several types of meditation.We show the statistical significance of these network predictors - and their increased performance compared to popular spectral and non-linear features such as complexity or entropy - on data from highly skilled meditators, in a continuous setting applicable to real-time analysis and applications such as neurofeedback.

Efficacy of cranial electrotherapy stimulation on mood and sense of well-being in people with subclinical insomnia

Cranial electrotherapy stimulation is a non-invasive brain stimulation method characterised by using a microcurrent. The objective of the study was to investigate whether a novel device with a stable supplement of electronic stimulation would improve sleep and the accompanying mood symptoms in people with subclinical insomnia. People who had insomnia symptoms without meeting the criteria for chronic insomnia disorder were recruited and randomly assigned to an active or a sham device group. They were required to use the provided device for 30 min each time, twice a day for 2 weeks. Outcome measures included questionnaires for sleep, depression, anxiety, and quality of life, 4 day actigraphy, and 64-channel electroencephalography. Fifty-nine participants (male 35.6%) with a mean age of 41.1 ± 12.0 years were randomised. Improvement of depression (p = 0.032) and physical well-being (p = 0.041) were significant in the active device group compared with the sham device group. Anxiety was also improved in the active device group, although the improvement was not statistically significant (p = 0.090). Regarding sleep, both groups showed a significant improvement in subjective rating, showing no significant group difference. The change in electroencephalography after the 2 week intervention was significantly different between the two groups, especially for occipital delta (p = 0.008) and beta power (p = 0.012), and temporo-parieto-occipital theta (p = 0.022). In conclusion, cranial electrotherapy stimulation can serve as an adjunctive therapy to ameliorate psychological symptoms and to alter brain activity. The effects of the device in a clinical population and an optimal set of parameters of stimulation should be further investigated.

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

Introduction

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).

Methods

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.

Results

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.

Conclusion

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.

Eliciting brain waves of people with cognitive impairment during meditation exercises using portable electroencephalography in a smart-home environment: a pilot study

Objectives

Meditation imparts relaxation and constitutes an important non-pharmacological intervention for people with cognitive impairment. Moreover, EEG has been widely used as a tool for detecting brain changes even at the early stages of Alzheimer's Disease (AD). The current study investigates the effect of meditation practices on the human brain across the AD spectrum by using a novel portable EEG headband in a smart-home environment.

Methods

Forty (40) people (13 Healthy Controls-HC, 14 with Subjective Cognitive Decline-SCD and 13 with Mild Cognitive Impairment-MCI) participated practicing Mindfulness Based Stress Reduction (Session 2-MBSR) and a novel adaptation of the Kirtan Kriya meditation to the Greek culture setting (Session 3-KK), while a Resting State (RS) condition was undertaken at baseline and follow-up (Session 1-RS Baseline and Session 4-RS Follow-Up). The signals were recorded by using the Muse EEG device and brain waves were computed (alpha, theta, gamma, and beta).

Results

Analysis was conducted on four-electrodes (AF7, AF8, TP9, and TP10). Statistical analysis included the Kruskal-Wallis (KW) nonparametric analysis of variance. The results revealed that both states of MBSR and KK lead to a marked difference in the brain's activation patterns across people at different cognitive states. Wilcoxon Signed-ranks test indicated for HC that theta waves at TP9, TP10 and AF7, AF8 in Session 3-KK were statistically significantly reduced compared to Session 1-RS Z = -2.271, p = 0.023, Z = -3.110, p = 0.002 and Z = -2.341, p = 0.019, Z = -2.132, p = 0.033, respectively.

Conclusion

The results showed the potential of the parameters used between the various groups (HC, SCD, and MCI) as well as between the two meditation sessions (MBSR and KK) in discriminating early cognitive decline and brain alterations in a smart-home environment without medical support.

The Electrome of a Parasitic Plant in a Putative State of Attention Increases the Energy of Low Band Frequency Waves: A Comparative Study with Neural Systems

Selective attention is an important cognitive phenomenon that allows organisms to flexibly engage with certain environmental cues or activities while ignoring others, permitting optimal behaviour. It has been proposed that selective attention can be present in many different animal species and, more recently, in plants. The phenomenon of attention in plants would be reflected in its electrophysiological activity, possibly being observable through electrophytographic (EPG) techniques. Former EPG time series obtained from the parasitic plant Cuscuta racemosa in a putative state of attention towards two different potential hosts, the suitable bean (Phaseolus vulgaris) and the unsuitable wheat (Triticum aestivum), were revisited. Here, we investigated the potential existence of different band frequencies (including low, delta, theta, mu, alpha, beta, and gamma waves) using a protocol adapted from neuroscientific research. Average band power (ABP) was used to analyse the energy distribution of each band frequency in the EPG signals, and time dispersion analysis of features (TDAF) was used to explore the variations in the energy of each band. Our findings indicated that most band waves were centred in the lower frequencies. We also observed that C. racemosa invested more energy in these low-frequency waves when suitable hosts were present. However, we also noted peaks of energy investment in all the band frequencies, which may be linked to extremely low oscillatory electrical signals in the entire tissue. Overall, the presence of suitable hosts induced a higher energy power, which supports the hypothesis of attention in plants. We further discuss and compare our results with generic neural systems.

Development of an Online Mind-Body Physical Activity Intervention for Young Adults during COVID-19: A Pilot Study

The purpose of this study was to examine the association between the implementation of an online mind-body physical activity (MBPA) intervention and physical activity (PA), stress, and well-being in young adults during COVID-19. The participants were a sample of college students (N = 21; 81% female). The MBPA intervention was organized in four online modules that were administered asynchronously for 8 weeks with three separate 10 min sessions per week. The intervention components consisted of traditional deep breathing, diaphragm mindful breathing, yoga poses, and walking meditation. Objective PA behaviors were assessed using wrist-worn ActiGraph accelerometers, and stress and well-being data were collected using validated self-report instruments. A 2 (sex) × 3 (time) doubly multivariate analysis of variance test with a univariate follow-up showed that the % of wear time in light (LPA) and moderate-to-vigorous physical activity (MVPA) was significantly higher at the end of the intervention compared to baseline (LPA mean difference = 11.3%, p = 0.003, d = 0.70; MVPA mean difference = 2.9%, p < 0.001, d = 0.56). No significant differences were observed for perceived stress and well-being, and there was no moderating effect of sex. The MBPA intervention showed promise, as it was associated with higher PA in young adults during COVID-19. No improvements were observed for stress and well-being. These results warrant further testing of the intervention's effectiveness using larger samples.

Systemic Signals Induced by Single and Combined Abiotic Stimuli in Common Bean Plants

To survive in a dynamic environment growing fixed to the ground, plants have developed mechanisms for monitoring and perceiving the environment. When a stimulus is perceived, a series of signals are induced and can propagate away from the stimulated site. Three distinct types of systemic signaling exist, i.e., (i) electrical, (ii) hydraulic, and (iii) chemical, which differ not only in their nature but also in their propagation speed. Naturally, plants suffer influences from two or more stimuli (biotic and/or abiotic). Stimuli combination can promote the activation of new signaling mechanisms that are explicitly activated, as well as the emergence of a new response. This study evaluated the behavior of electrical (electrome) and hydraulic signals after applying simple and combined stimuli in common bean plants. We used simple and mixed stimuli applications to identify biochemical responses and extract information from the electrical and hydraulic patterns. Time series analysis, comparing the conditions before and after the stimuli and the oxidative responses at local and systemic levels, detected changes in electrome and hydraulic signal profiles. Changes in electrome are different between types of stimulation, including their combination, and systemic changes in hydraulic and oxidative dynamics accompany these electrical signals.

Effects of yoga on menopausal symptoms and sleep quality across menopause statuses: A randomized controlled trial

This randomized controlled trial investigated the effects of yoga on menopausal symptoms and sleep quality across menopause statuses. Participants were randomly assigned to either the intervention or control group (n = 104 each), and those in the intervention group practiced yoga for 20 weeks. The participants completed the following questionnaires: the Depression, Anxiety, and Stress Scale; Multidimensional Scale of Perceived Social Support; Menopause Rating Scale; and Pittsburgh Sleep Quality Index. The results revealed that yoga effectively decreased menopausal symptoms, with the strongest effects noted in postmenopausal women (mean ± standard deviation: 14.98 ± 7.10), followed by perimenopausal women (6.11 ± 2.07). Yoga significantly improved sleep quality in postmenopausal and perimenopausal women after controlling for social support, depression, anxiety, stress, and menopausal symptoms (p < 0.001). However, yoga did not affect sleep quality in premenopausal women. Overall sleep quality significantly improved in postmenopausal and perimenopausal women. Our data indicate that yoga can help decrease menopausal symptoms, particularly in perimenopausal and postmenopausal women, and improve their health.

Heartfulness Meditation: A Yogic and Neuroscientific Perspective

Today, as research into the contemplative sciences is being widely referenced, the research community would benefit from an understanding of the Heartfulness method of meditation. Heartfulness offers an in-depth experiential practice focused on the evolution of human consciousness using the ancient technique of Pranahuti (yogic Transmission) during Meditation, in combination with the more active mental practice of “Cleaning.” Both are enabled by initiation into the Heartfulness practices. These unique features distinguish Heartfulness from other paths that have been described in the scientific literature thus far. In this introductory paper, we present the Heartfulness practices, the philosophy upon which the practices are based, and we reflect on the putative mechanisms through which Heartfulness could exert its effects on the human body and mind in the light of scientific research that has been done in other meditation systems. We conclude with suggestions for future research on the Heartfulness way of meditation.

A Retrospective Analysis of Three Focused Attention Meditation Techniques: Mantra, Breath, and External-Point Meditation

Objective The goal of this study is to compare the effectiveness of three different meditation techniques (two internal focus techniques and one external focus technique) using a low-cost portable electroencephalography (EEG) device, namely, MUSE, for an objective comparison. Methods This is an IRB-approved retrospective study. All participants in the study were healthy adults. Each study participant (n = 34) was instructed to participate in three meditation sessions: mantra (internal), breath (internal), and external point. The MUSE brain-sensing headband (EEG) was used to document the "total time spent in the calm state" and the "total time spent in the calm or neutral state" (outcomes) in each three-minute session to conduct separate analyses for the meditation type. Separate generalized linear models (GLM) with unstructured covariance structures were used to examine the association between each outcome and the explanatory variable (meditation type). For all models, if there was a significant association between the outcome and the explanatory variable, pairwise comparisons were carried out using the Tukey-Kramer correction. Results The median time (in seconds) spent in the calm state while practicing mantra meditation was 131.5 (IQR: 94-168), while practicing breath meditation was 150 (IQR: 113-164), and while practicing external-point meditation was 100 (IQR: 62-126). Upon analysis, there was a significant association between the meditation type and the time spent in the calm state (p-value = 0.0006). Conclusion This is the first study comparing "internal" versus "external" meditation techniques using an objective measure. Our study shows the breath and mantra technique as superior to the external-point technique as regards time spent in the calm state. Additional research is needed using a combination of "EEG" and patient-reported surveys to compare various meditative practices. The findings from this study can help incorporate specific meditation practices in future mindfulness-based studies that are focused on healthcare settings and on impacting clinical outcomes, such as survival or disease outcomes.

Attention and Emotional States during Horticultural Activities of Adults in 20s Using Electroencephalography: A Pilot Study

Since indoor, sedentary lifestyles became prevalent in society, humans have lost a sustainable connection to nature. An intervention utilizing outdoor horticultural activities could address such a challenge, but their beneficial effects on the brain and emotions have not been characterized in a quantitative approach. We aimed to investigate brain activity and emotional changes in adults in their 20s during horticultural activity to confirm feasibility of horticultural activity to improve cognitive and emotional states. Sixty university students participated in 11 outdoor horticultural activities at 2-min intervals. We measured brain waves of participants’ prefrontal cortex using a wireless electroencephalography device while performing horticultural activities. Between activities, we evaluated emotional states of participants using questionnaires. Results showed that each horticultural activity showed promotion of brain activity and emotional changes at varying degrees. The participants during physically intensive horticultural activities—digging, raking, and pruning—showed the highest attention level. For emotional states, the participants showed the highest fatigue, tension, and vigor during digging and raking. Plant-based activities—harvesting and transplanting plants—made participants feel natural and relaxed the most. Therefore, this pilot study confirmed the possibility of horticultural activity as a short-term physical intervention to improve attention levels and emotional stability in adults.

Real-World fNIRS Brain Activity Measurements during Ashtanga Vinyasa Yoga

Functional near-infrared spectroscopy (fNIRS) is often praised for its portability and robustness towards motion artifacts. While an increasing body of fNIRS research in real-world environments is emerging, most fNIRS studies are still conducted in laboratories, and do not incorporate larger movements performed by participants. This study extends fNIRS applications in real-world environments by conducting a single-subject observational study of a yoga practice with considerable movement (Ashtanga Vinyasa Yoga) in a participant's natural environment (their apartment). The results show differences in cognitive load (prefrontal cortex activation) when comparing technically complex postures to relatively simple ones, but also some contrasts with surprisingly little difference. This study explores the boundaries of real-world cognitive load measurements, and contributes to the empirical knowledge base of using fNIRS in realistic settings. To the best of our knowledge, this is the first demonstration of fNIRS brain imaging recorded during any moving yoga practice. Future work with fNIRS should take advantage of this by accomplishing studies with considerable real-world movement.