Mobile Brain/body Imaging Research Articles

Using neuroscience to explore creative media in art therapy: a systematic narrative review

ABSTRACT Background Few studies have investigated brain responses to different art media. Investigations into brain processes during art making have highlighted important structures. Neuroimaging tools have been used to investigate activation of brain areas whilst artmaking, but not in a therapeutic setting. This review highlights recent advancements in this area and encourages researchers to be the first to apply this in the UK. Aim To understand how the principles of neuroscience are currently informing the literature to explain the effects of art media in art therapy practice. Methods Review of published peer-reviewed research between years 2000 and 2020 on neuroscience and art therapy. Results Findings were summarised into categories discussing psychological/neurobiological issues, art media, neuroimaging technology, and models posited. Forty-six studies were found; majority discussed the structure and function of the brain to explain art therapy processes. The Expressive Therapies Continuum (ETC) model theorised that media properties stimulate different levels of visual and cognitive processing. The novel Mobile Brain/Body Imaging (MoBI) neuroimaging technology may be used as a means of quantifying data. Conclusion Significant progress has been made in attempting to explain brain responses during the art therapy process. However empirical data is needed to prove theoretical models. The use of neuroimaging has started this process to lead research into evidence-based practice. Implications for practice/further research Evidencing the ground-breaking ETC model, using neuroimaging and MoBI technology is needed through close collaboration between transdisciplinary departments. Art therapists should be encouraged to use the ETC to inform art therapy assessment, planning and treatment. Plain-language summary Neuroscience is the study of the brain and its processes, and recently technology has been available to researchers to examine brain processes in detail. This systematic narrative review explores recent literature that uses the principles of neuroscience to investigate the effects of art therapy, with a specific interest in art media. A systematic narrative review is when the findings of the study rely on the use of words to summarise the data. Art media is an important part of art therapy, as it is through artistic expressions that the client communicates their inner troubles. Different art media has been shown to elicit different feelings and behavioural responses in clients. However, research showing brain responses to different art media is limited. This study highlights ways in which further research in this field can take place. Results show that significant advancements have already been made that identify important structures and functions of the brain accessed during art therapy. Important models which incorporate neuroscience principles and theorise the art therapy process are highlighted. A significant model is the Expressive Therapies Continuum. This model explains how the brain processes information when different art media is used. However empirical data is needed to substantiate the theory behind this model. The use of neuroimaging technology is one way to achieve this. Therefore, this paper encourages transdisciplinary research to further investigate the effects different art media has on the brain during the art therapy process.

Using mobile brain/body imaging to advance research in arts, health, and related therapeutics.

The uses of mobile brain/body imaging (MoBI) are expanding and allow for more direct study of the neurophysiological signals associated with behavior in psychotherapeutic encounters. Neuroaesthetics is concerned with the cognitive and neural basis of art appreciation, and scientific correlations are being made in the field that might help to clarify theories claimed in the creative arts therapies. Yet, most neuroaesthetics studies are confined to the laboratory and do not propose a translation for research methods and clinical applications. The creative arts therapies have a long history of clinical success with various patient populations and will benefit from increased scientific explanation to support intervention strategies. Examining the brain dynamics and motor behaviors that are associated with the higher complex processes involved in artistic expression offers MoBI as a promising instrumentation to move forward in linking ideas from neuroaesthetics to the creative arts therapies. Tracking brain dynamics in association with behavioral change allows for more objective and quantitative physiological monitors to evaluate, and together with subjective patient reports provides insight into the psychological mechanisms of change in treatment. We outline a framework that shows how MoBI can be used to study the effectiveness of creative arts therapy interventions motivated by the 4E approach to cognition with a focus on visual art therapy. The article illuminates how a new partnership among the fields of art therapy, neuroscience, and neuroaesthetics might work together within the 4E/MoBI framework in efforts to advance transdisciplinary research for clinical health populations.

Using the MoBI motion capture system to rapidly and accurately localize EEG electrodes in anatomic space.

During mobile brain/body imaging (MoBI) experiments, electroencephalography and motion capture systems are used in concert to record high temporal resolution neural activity and movement kinematics while participants perform demanding perceptual and cognitive tasks in a naturalistic environment. A typical MoBI setup involves positioning multi‐channel electrode caps based on anatomical fiducials as well as experimenter and participant intuition regarding the scalp midpoint location (i.e., Cz). Researchers often use the “template” electrode locations provided by the manufacturer, however, the “actual” electrode locations can vary based on each participant's head morphology. Accounting for differences in head morphologies could provide more accurate clinical diagnostic information when using MoBI to identify neurological deficits in patients with motor, sensory, or cognitive impairments. Here, we asked whether the existing motion capture system used in a MoBI setup could be easily adapted to improve spatial localization of electrodes across participants without requiring additional or specialized equipment that might impede clinical adoption. Using standard electrode configurations, infrared markers were placed on a subset of electrodes and anatomical fiducials, and the remaining electrode locations were estimated using spherical or ellipsoid models. We identified differences in event‐related potentials between “template” and “actual” electrode locations during a Go/No‐Go task (p < 9.8e–5) and an object‐manipulation task (p < 9.8e–5). Thus, the motion capture system already used in MoBI experiments can be effectively deployed to accurately register and quantify the neural activity. Improving the spatial localization without needing specialized hardware or additional setup time to the workflow has important real‐world implications for translating MoBI to clinical environments.

Alteration of brain dynamics during dual‐task overground walking

When walking in our natural environment, we often solve additional cognitive tasks. This increases the demand of resources needed for both the cognitive and motor systems, resulting in Cognitive-Motor Interference (CMI). A large portion of neurophysiological investigations on CMI took place in static settings, emphasizing the experimental rigor but overshadowing the ecological validity. As a more ecologically valid alternative to treadmill and desktop-based setups to investigate CMI, we developed a dual-task walking scenario in virtual reality (VR) combined with Mobile Brain/Body Imaging (MoBI). We aimed at investigating how brain dynamics are modulated by dual-task overground walking with an additional task in the visual domain. Participants performed a visual discrimination task in VR while standing (single-task) and walking overground (dual-task). Even though walking had no impact on the performance in the visual discrimination task, a P3 amplitude reduction along with changes in power spectral densities (PSDs) were observed for discriminating visual stimuli during dual-task walking. These results reflect an impact of walking on the parallel processing of visual stimuli even when the cognitive task is particularly easy. This standardized and easy to modify VR paradigm helps to systematically study CMI, allowing researchers to control for the impact of additional task complexity of tasks in different sensory modalities. Future investigations implementing an improved virtual design with more challenging cognitive and motor tasks will have to investigate the roles of both cognition and motion, allowing for a better understanding of the functional architecture of attention reallocation between cognitive and motor systems during active behavior.

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network

Objective. Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. The aim of this work is to propose and validate a deep learning model to decode gait phases from Electroenchephalography (EEG). Approach. A Long-Short Term Memory (LSTM) deep neural network has been trained to deal with time-dependent information within brain signals during locomotion. The EEG signals have been preprocessed by means of Artifacts Subspace Reconstruction (ASR) and Reliable Independent Component Analysis (RELICA) to ensure that classification performance was not affected by movement-related artifacts. Main results. The network was evaluated on the dataset of 11 healthy subjects walking on a treadmill. The proposed decoding approach shows a robust reconstruction (AUC > 90%) of gait patterns (i.e. swing and stance states) of both legs together, or of each leg independently. Significance. Our results support for the first time the use of a memory-based deep learning classifier to decode walking activity from non-invasive brain recordings. We suggest that this classifier, exploited in real time, can be a more effective input for devices restoring locomotion in impaired people.

Mobile brain/body Imaging in dance: A dynamic transdisciplinary field for applied research.

Neuroscience of dance is an emerging field with important applications related to health and well-being, as dance has shown potential to foster adaptive neuroplasticity and is increasingly popular as a therapeutic activity or adjunct therapy for people living with conditions such as Parkinson's and Alzheimer's diseases. However, the multimodal nature of dance presents challenges to researchers aiming to identify mechanisms involved when dance is used to combat neurodegeneration or support healthy ageing. Requiring simultaneous engagement of motor and cognitive domains, dancing includes coordination of systems involved in timing, memory and spatial learning. Studies on dance to this point rely primarily on assessments of brain dynamics and structure through pre/post-tests or studies on expertise, as traditional brain imaging modalities restrict participant movement to avoid movement-related artefacts. In this paper, we describe the process of designing and implementing a study that uses mobile brain/body imaging (MoBI) to investigate real-time changes in brain dynamics and behaviour during the process of learning and performing a novel dance choreography. We show the potential for new insights to emerge from the coordinated collection of movement and brain-based data, and the implications of these in an emerging field whose medium is motion.

Perfect timing: Mobile brain/body imaging scaffolds the 4E-cognition research program.

Recent technological advancements encompassed under the Mobile Brain/Body Imaging (MoBI) framework have produced exciting new experimental results linking mind, brain and behaviour. Nevertheless, novel hypotheses, measures and experimental paradigms are needed in order to tackle MoBI's ultimate goal: to model and understand cognition, behaviour and experience as it emerges and unfolds unto and from the world. Such a goal is not completely novel or unique to the MoBI framework; it is at the core of a long-standing scientific and philosophical challenge. The ages-long debate revolves around the role of the body and the world on the emergence of the mind. Considering this, the present work has two goals. Our first goal is to briefly summarize some of the main ideas encompassed by the materialist/naturalist view of cognition as a complex emergent phenomenon. Our second and main goal is to argue that thanks to both MoBI and recent theoretical advances encompassed under the 4E-Cognition banner, theory and methodology might be finally synchronized, giving way to a revitalized form of emergentism, which lays new grounds for the understanding of cognitive phenomena. Finally, we offer the reader what we consider to be the main objective for the MoBI/4E framework and the understanding of the functional role of brain/body/world couplings in the emergence of cognition.

High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task

In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.

Assaying neural activity of children during video game play in public spaces: a deep learning approach

Objective. Understanding neural activity patterns in the developing brain remains one of the grand challenges in neuroscience. Developing neural networks are likely to be endowed with functionally important variability associated with the environmental context, age, gender, and other variables. Therefore, we conducted experiments with typically developing children in a stimulating museum setting and tested the feasibility of using deep learning techniques to help identify patterns of brain activity associated with different conditions. Approach. A four-channel dry EEG-based Mobile brain-body imaging data of children at rest and during videogame play (VGP) was acquired at the Children’s Museum of Houston. A data-driven approach based on convolutional neural networks (CNN) was used to describe underlying feature representations in the EEG and their ability to discern task and gender. The variability of the spectral features of EEG during the rest condition as a function of age was also analyzed. Main results. Alpha power (7–13 Hz) was higher during rest whereas theta power (4–7 Hz) was higher during VGP. Beta (13–18 Hz) power was the most significant feature, higher in females, when differentiating between males and females. Using data from both temporoparietal channels to classify between VGP and rest condition, leave-one-subject-out cross-validation accuracy of 67% was obtained. Age-related changes in EEG spectral content during rest were consistent with previous developmental studies conducted in laboratory settings showing an inverse relationship between age and EEG power. Significance. These findings are the first to acquire, quantify and explain brain patterns observed during VGP and rest in freely behaving children in a museum setting using a deep learning framework. The study shows how deep learning can be used as a data driven approach to identify patterns in the data and explores the issues and the potential of conducting experiments involving children in a natural and engaging environment.

Summary of Twenty-First Century Great Conversations in Art, Neuroscience and Related Therapeutics.

Transdisciplinary collaboration is the future of knowledge making in advanced post-industrial societies and there is a growing awareness that the most vexing problems we face cannot be solved by any single discipline. Best practices for complex and challenging physical and mental disorders require a multi-disciplinary approach, yet there is a void in bridging the gap between the most contemporary models. It is in this capacity that the Twenty-First Century Great Conversations in Art, Neuroscience, and Related Therapeutics serves as a missing link. It was with active minds and a collective spirit that artists, scientists, therapists, physicians, engineers, technology experts, healthcare practitioners, and researchers from across the globe transcended historical silos to explore the capacities for collaborative partnerships to influence the health of patients and the amelioration of disease. Hosted at Indiana University-Purdue University Indianapolis (IUPUI), presenters shared insights through didactic sessions and panel discussions aligned with three tracks led by prominent experts in their respective fields: (1) Neuroaesthetics, Anjan Chatterjee, MD; (2) Creativity and Consciousness, Arne Dietrich, PhD; and (3) Mobile Brain/Body Imaging (MoBI), Klaus Gramann, PhD. The goals for this symposium were developed from a vision which embraces cross-disciplinary intersectionality, a merging of viewpoints, and active dialogue surrounding the development of a common language with which to advance the Creative Arts Therapies and neurosciences. The goal was also to contribute to the development of a simplified roadmap to enhance and enrich the CATs with a greater understanding of neuroscience and the available technologies that can assist in research.

Cognitive load reduces the effects of optic flow on gait and electrocortical dynamics during treadmill walking.

During navigation of complex environments, the brain must continuously adapt to both external demands, such as fluctuating sensory inputs, and internal demands, such as engagement in a cognitively demanding task. Previous studies have demonstrated changes in behavior and gait with increased sensory and cognitive load, but the underlying cortical mechanisms remain largely unknown. In the present study, in a mobile brain/body imaging (MoBI) approach, 16 young adults walked on a treadmill with high-density EEG while 3-dimensional (3D) motion capture tracked kinematics of the head and feet. Visual load was manipulated with the presentation of optic flow with and without continuous mediolateral perturbations. The effects of cognitive load were assessed by the performance of a go/no-go task on half of the blocks. During increased sensory load, participants walked with shorter and wider strides, which may indicate a more restrained pattern of gait. Interestingly, cognitive task engagement attenuated these effects of sensory load on gait. Using an independent component analysis and dipole-fitting approach, we found that cautious gait was accompanied by neuro-oscillatory modulations localized to frontal (supplementary motor area, anterior cingulate cortex) and parietal (inferior parietal lobule, precuneus) areas. Our results show suppression in alpha/mu (8-12 Hz) and beta (13-30 Hz) rhythms, suggesting enhanced activation of these regions with unreliable sensory inputs. These findings provide insight into the neural correlates of gait adaptation and may be particularly relevant to older adults who are less able to adjust to ongoing cognitive and sensory demands while walking. NEW & NOTEWORTHY The neural underpinnings of gait adaptation in humans are poorly understood. To this end, we recorded high-density EEG combined with three-dimensional body motion tracking as participants walked on a treadmill while exposed to full-field optic flow stimulation. Perturbed visual input led to a more cautious gait pattern with neuro-oscillatory modulations localized to premotor and parietal regions. Our findings show a possible brain-behavior link that might further our understanding of gait and mobility impairments.

Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground

Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain’s involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack the ability to infer information about the peripheral and central signalling during actual task execution. In this report, we present a dataset containing simultaneously recorded electroencephalography (EEG), lower-limb electromyography (EMG), and full body motion capture recorded from ten able-bodied individuals. The subjects completed an average of twenty trials on an experimental gait course containing level-ground, ramps, and stairs. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle sites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. In this report, we present the rationale for collecting these data, a detailed explanation of the experimental setup, and a brief validation of the data quality.

A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface

We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record lower limb joint angles (bilateral hip, knee, and ankle) using goniometers synchronized with 60-channel scalp electroencephalography (EEG). Additionally, electrooculogram (EOG), EEG electrodes impedance, and digitized EEG channel locations were acquired to aid artifact removal and EEG dipole-source localization. This dataset is unique in that it is the first published MoBI dataset recorded during walking. It is useful in addressing several important open research questions, such as how EEG is coupled with gait cycle during closed-loop BCI, how BCI influences neural activity during walking, and how a BCI decoder may be optimized.

Assessing Brain-Muscle Connectivity in Human Locomotion through Mobile Brain/Body Imaging: Opportunities, Pitfalls, and Future Directions.

Assessment of the cortical role during bipedalism has been a methodological challenge. While surface electroencephalography (EEG) is capable of non-invasively measuring cortical activity during human locomotion, it is associated with movement artifacts obscuring cerebral sources of activity. Recently, statistical methods based on blind source separation revealed potential for resolving this issue, by segregating non-cerebral/artifactual from cerebral sources of activity. This step marked a new opportunity for the investigation of the brains’ role while moving and was tagged mobile brain/body imaging (MoBI). This methodology involves simultaneous mobile recording of brain activity with several other body behavioral variables (e.g., muscle activity and kinematics), through wireless recording wearable devices/sensors. Notably, several MoBI studies using EEG–EMG approaches recently showed that the brain is functionally connected to the muscles and active throughout the whole gait cycle and, thus, rejecting the long-lasting idea of a solely spinal-driven bipedalism. However, MoBI and brain/muscle connectivity assessments during human locomotion are still in their fledgling state of investigation. Mobile brain/body imaging approaches hint toward promising opportunities; however, there are some remaining pitfalls that need to be resolved before considering their routine clinical use. This article discusses several of these pitfalls and proposes research to address them. Examples relate to the validity, reliability, and reproducibility of this method in ecologically valid scenarios and in different populations. Furthermore, whether brain/muscle connectivity within the MoBI framework represents a potential biomarker in neuromuscular syndromes where gait disturbances are evident (e.g., age-related sarcopenia) remains to be determined.

Effective Synchronization of EEG and EMG for Mobile Brain/Body Imaging in Clinical Settings.

Mobile Brain/Body Imaging (MoBI) is rapidly gaining traction as a new imaging modality to study how cognitive processes support locomotion. Electroencephalogram (EEG) and electromyogram (EMG), due to their time resolution, non-invasiveness and portability are the techniques of choice for MoBI, but synchronization requirements among others restrict its use to high-end research facilities. Here we test the effectiveness of a technique that enables us to achieve MoBI-grade synchronization of EEG and EMG, even when other strategies (such as Lab Streaming Layer (LSL)) cannot be used e.g., due to the unavailability of proprietary Application Programming Interfaces (APIs), which is often the case in clinical settings. The proposed strategy is that of aligning several spikes at the beginning and end of the session. We delivered a train of spikes to the EEG amplifier and EMG electrodes every 2 s over a 10-min time period. We selected a variable number of spikes (from 1 to 10) both at the beginning and end of the time series and linearly resampled the data so as to align them. We then compared the misalignment of the “middle” spikes over the whole recording to test for jitter and synchronization drifts, highlighting possible nonlinearities (due to hardware filters) and estimated the maximum length of the recording to achieve a [−5 to 5] ms misalignment range. We demonstrate that MoBI-grade synchronization can be achieved within 10-min recordings with a 1.7 ms jitter and [−5 5] ms misalignment range. We show that repeated spike delivery can be used to test online synchronization options and to troubleshoot synchronization issues over EEG and EMG. We also show that synchronization cannot rely only on the equipment sampling rate advertised by manufacturers. The synchronization strategy described can be used virtually in every clinical environment, and may increase the interest among a broader spectrum of clinicians and researchers in the MoBI framework, ultimately leading to a better understanding of the brain processes underlying locomotion control and the development of more effective rehabilitation approaches.

Mobile Brain/Body Imaging: A Decade of Emergence

Event Abstract Back to Event Mobile Brain/Body Imaging: A Decade of Emergence Scott Makeig1* 1 Institute for Neural Computation, University of California, San Diego, Swartz Center for Computational Neuroscience, United States A decade ago, when my colleague Howard Poizner approached me with the idea to record EEG during a behavioral reaching experiment, the wider possibilities for performing functional EEG imaging during nearly any motor behavior dawned on me only slowly. I knew that the independent component analysis (ICA) data decomposition method I and my colleagues were pioneering, originally at Terry Sejnowski’s lab at the Salk Institute and then at our UCSD Swartz Center for Computational Neuroscience, could separate out the profuse contributions to scalp EEG from eye movements and neck muscle activities during natural movements. Thus, the technical means were at hand to separate, for the first time, potentials generated in the brain from non-brain processes that are summed in high-density scalp EEG. Non-trivial results from our first pilot experiment began to appear when we extended our ICA method to include in the analysis not only the nature of the movement the participant was making, but their immediately previous movement history. ICA-based EEG imaging revealed dynamics of cortical networks involved in motor planning and evaluation, processes that are near-continually active during what Klaus Gramann later dubbed our natural cognition. It struck me that previous brain imaging, whether of participants lying rigidly still in an fMRI, PET, or MEG scanner or staring at an EEG experiment screen fixation point, may not have seen or revealed the brain’s repertoire or cortical dynamics that supports human agency, a root aspect of human consciousness. Indeed, the human brain has evolved to support our ability to move intelligently, meaning so as to optimize the results of our behavior. These ideas seemed so scientifically important to me that I felt the new research direction deserved a name – and, optimally, its own four-letter acronym (i.e., not ‘f-m-r-I’) that might inspire and focus research interest on this new field of brain research studying how cortical network dynamics support our everyday living. I decided to venture a new term, mobile brain/body imaging, and for it a new acronym, MoBI. In my talk I will explore these thoughts and will comment (from my limited viewpoint) on the now everywhere emerging brain studies of our natural embodied cognition, and on how the high-resolution EEG methods we and others are now developing can fulfill the promise of those first pilot experiments, a decade ago, which involved participants seated in darkness and attempting to reach out to touch briefly illuminated points of light. Keywords: Mobile brain/body imaging (MoBI), independent component analysis (ICA), EEG, Signal processing, applied neuroscience Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018. Presentation Type: Oral Presentation Topic: Neuroergonomics Citation: Makeig S (2019). Mobile Brain/Body Imaging: A Decade of Emergence. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00002 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 16 Apr 2018; Published Online: 27 Sep 2019. * Correspondence: Dr. Scott Makeig, Institute for Neural Computation, University of California, San Diego, Swartz Center for Computational Neuroscience, San Diego, United States, smakeig@ucsd.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Scott Makeig Google Scott Makeig Google Scholar Scott Makeig PubMed Scott Makeig Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Modulation of Neural Activity during Guided Viewing of Visual Art.

Mobile Brain-Body Imaging (MoBI) technology was deployed to record multi-modal data from 209 participants to examine the brain’s response to artistic stimuli at the Museo de Arte Contemporáneo (MARCO) in Monterrey, México. EEG signals were recorded as the subjects walked through the exhibit in guided groups of 6–8 people. Moreover, guided groups were either provided with an explanation of each art piece (Guided-E), or given no explanation (Guided-NE). The study was performed using portable Muse (InteraXon, Inc, Toronto, ON, Canada) headbands with four dry electrodes located at AF7, AF8, TP9, and TP10. Each participant performed a baseline (BL) control condition devoid of artistic stimuli and selected his/her favorite piece of art (FP) during the guided tour. In this study, we report data related to participants’ demographic information and aesthetic preference as well as effects of art viewing on neural activity (EEG) in a select subgroup of 18–30 year-old subjects (Nc = 25) that generated high-quality EEG signals, on both BL and FP conditions. Dependencies on gender, sensor placement, and presence or absence of art explanation were also analyzed. After denoising, clustering of spectral EEG models was used to identify neural patterns associated with BL and FP conditions. Results indicate statistically significant suppression of beta band frequencies (15–25 Hz) in the prefrontal electrodes (AF7 and AF8) during appreciation of subjects’ favorite painting, compared to the BL condition, which was significantly different from EEG responses to non-favorite paintings (NFP). No significant differences in brain activity in relation to the presence or absence of explanation during exhibit tours were found. Moreover, a frontal to posterior asymmetry in neural activity was observed, for both BL and FP conditions. These findings provide new information about frequency-related effects of preferred art viewing in brain activity, and support the view that art appreciation is independent of the artists’ intent or original interpretation and related to the individual message that viewers themselves provide to each piece.

Deployment of Mobile EEG Technology in an Art Museum Setting: Evaluation of Signal Quality and Usability.

Electroencephalography (EEG) has emerged as a powerful tool for quantitatively studying the brain that enables natural and mobile experiments. Recent advances in EEG have allowed for the use of dry electrodes that do not require a conductive medium between the recording electrode and the scalp. The overall goal of this research was to gain an understanding of the overall usability and signal quality of dry EEG headsets compared to traditional gel-based systems in an unconstrained environment. EEG was used to collect Mobile Brain-body Imaging (MoBI) data from 432 people as they experienced an art exhibit in a public museum. The subjects were instrumented with either one of four dry electrode EEG systems or a conventional gel electrode EEG system. Each of the systems was evaluated based on the signal quality and usability in a real-world setting. First, we describe the various artifacts that were characteristic of each of the systems. Second, we report on each system's usability and their limitations in a mobile setting. Third, to evaluate signal quality for task discrimination and characterization, we employed a data driven clustering approach on the data from 134 of the 432 subjects (those with reliable location tracking information and usable EEG data) to evaluate the power spectral density (PSD) content of the EEG recordings. The experiment consisted of a baseline condition in which the subjects sat quietly facing a white wall for 1 min. Subsequently, the participants were encouraged to explore the exhibit for as long as they wished (piece-viewing). No constraints were placed upon the individual in relation to action, time, or navigation of the exhibit. In this freely-behaving approach, the EEG systems varied in their capacity to record characteristic modulations in the EEG data, with the gel-based system more clearly capturing stereotypical alpha and beta-band modulations.

Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics.

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants’ emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers’ affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types (rs (14) = 0.525, p = 0.037) and geometry (rs (14) = −0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces.

Neural Correlates of Single- and Dual-Task Walking in the Real World.

Recent developments in mobile brain-body imaging (MoBI) technologies have enabled studies of human locomotion where subjects are able to move freely in more ecologically valid scenarios. In this study, MoBI was employed to describe the behavioral and neurophysiological aspects of three different commonly occurring walking conditions in healthy adults. The experimental conditions were self-paced walking, walking while conversing with a friend and lastly walking while texting with a smartphone. We hypothesized that gait performance would decrease with increased cognitive demands and that condition-specific neural activation would involve condition-specific brain areas. Gait kinematics and high density electroencephalography (EEG) were recorded whilst walking around a university campus. Conditions with dual tasks were accompanied by decreased gait performance. Walking while conversing was associated with an increase of theta (θ) and beta (β) neural power in electrodes located over left-frontal and right parietal regions, whereas walking while texting was associated with a decrease of β neural power in a cluster of electrodes over the frontal-premotor and sensorimotor cortices when compared to walking whilst conversing. In conclusion, the behavioral “signatures” of common real-life activities performed outside the laboratory environment were accompanied by differing frequency-specific neural “biomarkers”. The current findings encourage the study of the neural biomarkers of disrupted gait control in neurologically impaired patients.