175 research outputs found
Multi-modal virtual scenario enhances neurofeedback learning
In the past decade neurofeedback (NF) has become the focus of a growing body of research. With real-time functional magnetic resonance imaging (fMRI) enabling online monitoring of emotion-related areas, such as the amygdala, many have begun testing its therapeutic benefits. However, most existing NF procedures still use monotonic uni-modal interfaces, thus possibly limiting user engagement and weakening learning efficiency. The current study tested a novel multi-sensory NF animated scenario (AS) aimed at enhancing user experience and improving learning. We examined whether relative to a simple uni-modal 2D interface, learning via an interface of complex multi-modal 3D scenario will result in improved NF learning. As a neural-probe, we used the recently developed fMRI-inspired EEG model of amygdala activity (“amygdala-EEG finger print”; amygdala-EFP), enabling low-cost and mobile limbic NF training. Amygdala-EFP was reflected in the AS by the unrest level of a hospital waiting room in which virtual characters become impatient, approach the admission desk and complain loudly. Successful downregulation was reflected as an ease in the room unrest level. We tested whether relative to a standard uni-modal 2D graphic thermometer (TM) interface, this AS could facilitate more effective learning and improve the training experience. Thirty participants underwent two separated NF sessions (1 week apart) practicing downregulation of the amygdala-EFP signal. In the first session, half trained via the AS and half via a TM interface. Learning efficiency was tested by three parameters: (a) effect size of the change in amygdala-EFP following training, (b) sustainability of the learned downregulation in the absence of online feedback, and (c) transferability to an unfamiliar context. Comparing amygdala-EFP signal amplitude between the last and the first NF trials revealed that the AS produced a higher effect size. In addition, NF via the AS showed better sustainability, as indicated by a no-feedback trial conducted in session 2 and better transferability to a new unfamiliar interface. Lastly, participants reported that the AS was more engaging and more motivating than the TM. Together, these results demonstrate the promising potential of integrating realistic virtual environments in NF to enhance learning and improve user’s experience
Bacteria Hunt: Evaluating multi-paradigm BCI interaction
The multimodal, multi-paradigm brain-computer interfacing (BCI) game Bacteria Hunt was used to evaluate two aspects of BCI interaction in a gaming context. One goal was to examine the effect of feedback on the ability of the user to manipulate his mental state of relaxation. This was done by having one condition in which the subject played the game with real feedback, and another with sham feedback. The feedback did not seem to affect the game experience (such as sense of control and tension) or the objective indicators of relaxation, alpha activity and heart rate. The results are discussed with regard to clinical neurofeedback studies. The second goal was to look into possible interactions between the two BCI paradigms used in the game: steady-state visually-evoked potentials (SSVEP) as an indicator of concentration, and alpha activity as a measure of relaxation. SSVEP stimulation activates the cortex and can thus block the alpha rhythm. Despite this effect, subjects were able to keep their alpha power up, in compliance with the instructed relaxation task. In addition to the main goals, a new SSVEP detection algorithm was developed and evaluated
Multipurpose virtual reality environment for biomedical and health applications
Virtual reality is a trending, widely accessible, and contemporary technology of increasing utility to biomedical and health applications. However, most implementations of virtual reality environments are tailored to specific applications. We describe the complete development of a novel, open-source virtual reality environment that is suitable for multipurpose biomedical and healthcare applications. This environment can be interfaced with different hardware and data sources, ranging from gyroscopes to fMRI scanners. The developed environment simulates an immersive (first-person perspective) run in the countryside, in a virtual landscape with various salient features. The utility of the developed VR environment has been validated via two test applications: an application in the context of motor rehabilitation following injury of the lower limbs and an application in the context of real-time functional magnetic resonance imaging neurofeedback, to regulate brain function in specific brain regions of interest. Both applications were tested by pilot subjects that unanimously provided very positive feedback, suggesting that appropriately designed VR environments can indeed be robustly and efficiently used for multiple biomedical purposes. We attribute the versatility of our approach on three principles implicit in the design: selectivity, immersiveness, and adaptability. The software, including both applications, is publicly available free of charge, via a GitHub repository, in support of the Open Science Initiative. Although using this software requires specialized hardware and engineering know-how, we anticipate our contribution to catalyze further progress, interdisciplinary collaborations and replicability, with regards to the usage of virtual reality in biomedical and health applications.Peer ReviewedPostprint (author's final draft
Active and Passive Brain-Computer Interfaces Integrated with Extended Reality for Applications in Health 4.0
This paper presents the integration of extended reality (XR) with brain-computer interfaces (BCI) to open up new possibilities in the health 4.0 framework. Such integrated systems are here investigated with respect to an active and a passive BCI paradigm. Regarding the active BCI, the XR part consists of providing visual and vibrotactile feedbacks to help the user during motor imagery tasks. Therefore, XR aims to enhance the neurofeedback by enhancing the user engagement. Meanwhile, in the passive BCI, user engagement monitoring allows the adaptivity of a XR-based rehabilitation game for children.
Preliminary results suggest that the XR neurofeedback helps the BCI users to carry on motor imagery tasks with up to 84% classification accuracy, and that the level of emotional and cognitive engagement can be detected with an accuracy greater than 75%
Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience
Real-time functional magnetic resonance imaging (rt-fMRI) has revived the translational perspective of neurofeedback (NF)1. Particularly for stress management, targeting deeply located limbic areas involved in stress processing2 has paved new paths for brain-guided interventions. However, the high cost and immobility of fMRI constitute a challenging drawback for the scalability (accessibility and cost-effectiveness) of the approach, particularly for clinical purposes3. The current study aimed to overcome the limited applicability of rt-fMRI by using an electroencephalography (EEG) model endowed with improved spatial resolution, derived from simultaneous EEG–fMRI, to target amygdala activity (termed amygdala electrical fingerprint (Amyg-EFP))4,5,6. Healthy individuals (n = 180) undergoing a stressful military training programme were randomly assigned to six Amyg-EFP-NF sessions or one of two controls (control-EEG-NF or NoNF), taking place at the military training base. The results demonstrated specificity of NF learning to the targeted Amyg-EFP signal, which led to reduced alexithymia and faster emotional Stroop, indicating better stress coping following Amyg-EFP-NF relative to controls. Neural target engagement was demonstrated in a follow-up fMRI-NF, showing greater amygdala blood-oxygen-level-dependent downregulation and amygdala–ventromedial prefrontal cortex functional connectivity following Amyg-EFP-NF relative to NoNF. Together, these results demonstrate limbic specificity and efficacy of Amyg-EFP-NF during a stressful period, pointing to a scalable non-pharmacological yet neuroscience-based training to prevent stress-induced psychopathology
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Volitional limbic neuromodulation has a multifaceted clinical benefit in Fibromyalgia patients
Volitional neural modulation using neurofeedback has been indicated as a potential treatment for chronic conditions that involve peripheral and central neural dysregulation. Here we utilized neurofeedback in patients suffering from Fibromyalgia - a chronic pain syndrome that involves sleep disturbance and emotion dysregulation. These ancillary symptoms, which have an amplification effect on pain, are known to be mediated by heightened limbic activity. In order to reliably probe limbic activity in a scalable manner fit for EEG-neurofeedback training, we utilized an Electrical Finger Print (EFP) model of amygdala-BOLD signal (termed Amyg-EFP), that has been successfully validated in our lab in the context of volitional neuromodulation.
We anticipated that Amyg-EFP-neurofeedback training aimed at limbic down modulation should improve chronic pain in patients suffering from Fibromyalgia, by balancing disturbed indices for sleep and affect. We further expected that improved clinical status would correspond to successful training as indicated by improved down modulation of the Amygdala-EFP signal.
Thirty-Four Fibromyalgia patients (31F; age 35.6 ± 11.82) participated in a randomized placebo-controlled trial with biweekly Amyg-EFP-neurofeedback sessions and placebo of sham neurofeedback (n = 9) for a total duration of five consecutive weeks. Following training, participants in the Real-neurofeedback group were divided into good (n = 13) or poor (n = 12) modulators according to their success in the neurofeedback training. Before and after treatment, self-reports on pain, depression, anxiety, fatigue and sleep quality were obtained, as well as objective sleep Indices. Long-term clinical follow-up was made available, within up to three years of the neurofeedback training completion.
REM latency and objective sleep quality index were robustly improved following the treatment course only in the Real-neurofeedback group (both time × group p < 0.05) and to a greater extent among good modulators (both time*sub-group p < 0.05). In contrast, self-report measures did not reveal a treatment-specific response at the end of the treatment. However, the follow-up assessment revealed a delayed improvement in chronic pain and subjective sleep experience, evident only in the Real-neurofeedback group (both time × group p < 0.05). Moderation analysis showed that the enduring clinical effects on pain evident in the follow-up assessment were predicted by the immediate improvements following training in objective sleep and subjective affect measures.
Our findings suggest that Amyg-EFP- neurofeedback that specifically targets limbic activity down modulation offers a successful principled approach for volitional EEG based neuromodulation training in Fibromyalgia patients. Importantly, it seems that via its immediate sleep improving effect, the neurofeedback training induced a delayed reduction in the target subjective symptom of chronic pain, far and beyond the immediate placebo effect. This indirect approach to chronic pain management reflects the necessary link between somatic and affective dysregulation that can be successfully targeted using neurofeedback
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Prefrontal asymmetry BCI neurofeedback datasets
Prefrontal cortex (PFC) asymmetry is an important marker in affective neuroscience and has attracted significant interest, having been associated with studies of motivation, eating behavior, empathy, risk propensity, and clinical depression. The data presented in this paper are the result of three different experiments using PFC asymmetry neurofeedback (NF) as a Brain-Computer Interface (BCI) paradigm, rather than a therapeutic mechanism aiming at long-term effects, using functional near-infrared spectroscopy (fNIRS) which is known to be particularly well-suited to the study of PFC asymmetry and is less sensitive to artifacts. From an experimental perspective the BCI context brings more emphasis on individual subjects' baselines, successful and sustained activation during epochs, and minimal training. The subject pool is also drawn from the general population, with less bias toward specific behavioral patterns, and no inclusion of any patient data. We accompany our datasets with a detailed description of data formats, experiment and protocol designs, as well as analysis of the individualized metrics for definitions of success scores based on baseline thresholds as well as reference tasks. The work presented in this paper is the result of several experiments in the domain of BCI where participants are interacting with continuous visual feedback following a real-time NF paradigm, arising from our long-standing research in the field of affective computing. We offer the community access to our fNIRS datasets from these experiments. We specifically provide data drawn from our empirical studies in the field of affective interactions with computer-generated narratives as well as interfacing with algorithms, such as heuristic search, which all provide a mechanism to improve the ability of the participants to engage in active BCI due to their realistic visual feedback. Beyond providing details of the methodologies used where participants received real-time NF of left-asymmetric increase in activation in their dorsolateral prefrontal cortex (DLPFC), we re-establish the need for carefully designing protocols to ensure the benefits of NF paradigm in BCI are enhanced by the ability of the real-time visual feedback to adapt to the individual responses of the participants. Individualized feedback is paramount to the success of NF in BCIs
A motivational model of BCI-controlled heuristic search
Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems
Vibrotactile Sensory Augmentation and Machine Learning Based Approaches for Balance Rehabilitation
Vestibular disorders and aging can negatively impact balance performance. Currently, the most effective approach for improving balance is exercise-based balance rehabilitation. Despite its effectiveness, balance rehabilitation does not always result in a full recovery of balance function. In this dissertation, vibrotactile sensory augmentation (SA) and machine learning (ML) were studied as approaches for further improving balance rehabilitation outcomes.
Vibrotactile SA provides a form of haptic cues to complement and/or replace sensory information from the somatosensory, visual and vestibular sensory systems. Previous studies have shown that people can reduce their body sway when vibrotactile SA is provided; however, limited controlled studies have investigated the retention of balance improvements after training with SA has ceased. The primary aim of this research was to examine the effects of supervised balance rehabilitation with vibrotactile SA. Two studies were conducted among people with unilateral vestibular disorders and healthy older adults to explore the use of vibrotactile SA for therapeutic and preventative purposes, respectively. The study among people with unilateral vestibular disorders provided six weeks of supervised in-clinic balance training. The findings indicated that training with vibrotactile SA led to additional body sway reduction for balance exercises with head movements, and the improvements were retained for up to six months. Training with vibrotactile SA did not lead to significant additional improvements in the majority of the clinical outcomes except for the Activities-specific Balance Confidence scale. The study among older adults provided semi-supervised in-home balance rehabilitation training using a novel smartphone balance trainer. After completing eight weeks of balance training, participants who trained with vibrotactile SA showed significantly greater improvements in standing-related clinical outcomes, but not in gait-related clinical outcomes, compared with those who trained without SA.
In addition to investigating the effects of long-term balance training with SA, we sought to study the effects of vibrotactile display design on people’s reaction times to vibrational cues. Among the various factors tested, the vibration frequency and tactor type had relatively small effects on reaction times, while stimulus location and secondary cognitive task had relatively large effects. Factors affected young and older adults’ reaction times in a similar manner, but with different magnitudes.
Lastly, we explored the potential for ML to inform balance exercise progression for future applications of unsupervised balance training. We mapped body motion data measured by wearable inertial measurement units to balance assessment ratings provided by physical therapists. By training a multi-class classifier using the leave-one-participant-out cross-validation method, we found approximately 82% agreement among trained classifier and physical therapist assessments.
The findings of this dissertation suggest that vibrotactile SA can be used as a rehabilitation tool to further improve a subset of clinical outcomes resulting from supervised balance rehabilitation training. Specifically, individuals who train with a SA device may have additional confidence in performing balance activities and greater postural stability, which could decrease their fear of falling and fall risk, and subsequently increase their quality of life. This research provides preliminary support for the hypothesized mechanism that SA promotes the central nervous system to reweight sensory inputs. The preliminary outcomes of this research also provide novel insights for unsupervised balance training that leverage wearable technology and ML techniques. By providing both SA and ML-based balance assessment ratings, the smart wearable device has the potential to improve individuals’ compliance and motivation for in-home balance training.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143901/1/baotian_1.pd
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