Biofeedback Signals Research Articles

Sens i-Lab: a key facility to expand the traditional approaches in experimental acoustics

Recent advances in developing new tools and miniaturised devices to measure, analyse, model and simulate existing or future projects are more and more influencing the way to investigate and solve problems of various disciplines fostering deep changes in the research paradigms toward human-centred, multisensory and multidisciplinary approaches. In acoustics, beyond the negative effect of noise on individuals and its mitigation, researchers are even more interested in investigating how the complexity of the multisensory environment modulates the individuals' holistic experience. To this aim, the Department of the Università degli Studi della Campania "Luigi Vanvitelli" has built the Sens i-Lab, a key facility integrating, in a single test room, the simulation and control of the physical environment (acoustics, vision, lighting, microclimate, IAQ) with advanced systems for simulation of virtual environments. To complement the simulation and control of the stimuli, the Sens i-Lab is equipped with a set of systems and devices for motion tracking, for the measurement of the biofeedback signals (EEG, EDA, HRV, VAF) and their association with environmental stimuli and self-reported psychological measures of people well-being. Taking advantage of the Sens i-Lab setting, new research fields and applications in acoustics are possible. Some of them are presented.

Seeking the Psilocybiome: Psychedelics meet the microbiota-gut-brain axis

Moving towards a systems psychiatry paradigm embraces the inherent complex interactions across all levels from micro to macro and necessitates an integrated approach to treatment. Cortical 5-HT2A receptors are key primary targets for the effects of serotonergic psychedelics. However, the therapeutic mechanisms underlying psychedelic therapy are complex and traverse molecular, cellular, and network levels, under the influence of biofeedback signals from the periphery and the environment. At the interface between the individual and the environment, the gut microbiome, via the gut-brain axis, plays an important role in the unconscious parallel processing systems regulating host neurophysiology. While psychedelic and microbial signalling systems operate over different timescales, the microbiota-gut-brain (MGB) axis, as a convergence hub between multiple biofeedback systems may play a role in the preparatory phase, the acute administration phase, and the integration phase of psychedelic therapy. In keeping with an interconnected systems-based approach, this review will discuss the gut microbiome and mycobiome and pathways of the MGB axis, and then explore the potential interaction between psychedelic therapy and the MGB axis and how this might influence mechanism of action and treatment response. Finally, we will discuss the possible implications for a precision medicine-based psychedelic therapy paradigm.

DeviceD: Audience-dancer interaction via social media posts and wearable for haptic feedback.

The performative installation DeviceD utilizes a network of systems toward facilitating interaction between dancer, digital media, and audience. Central to the work is a wearable haptic feedback system able to wirelessly deliver vibrotactile stimuli, with the latter initiated by the audience through posting on Twitter social media platform; the system in use searches for specific mentions, hashtags, and keywords, with positive results causing the system to trigger patterns of haptic biofeedback across the wearable's four actuator motors. The system acts as the intermediator between the audience's online actions and the dancer receiving physical stimuli; the dancer interprets these biofeedback signals according to Laban's Effort movement qualities, with the interpretation informing different states of habitual and conscious choreographic performance. In this article, the authors reflect on their collaborative process while developing DeviceD alongside a multidisciplinary team of technologists, detailing their experience of refining the technology and methodology behind the work while presenting it in three different settings. A literature review is used to situate the work among contemporary research on interaction over internet and haptics in performance practice; haptic feedback devices have been widely used within artistic work for the past 25years, with more recent practice and research outputs suggesting an increased interest for haptics in the field of dance research. The authors detail both technological and performative elements making up the work, and provide a transparent evaluation of the system, as means of providing a foundation for further research on wearable haptic devices.

Development of closed-loop modelling framework for adaptive respiratory pacemakers

ObjectiveVentilatory pacing by electrical stimulation of the phrenic nerve has many advantages compared to mechanical ventilation. However, commercially available respiratory pacing devices operate in an open-loop fashion, which require manual adjustment of stimulation parameters for a given patient. Here, we report the model development of a closed-loop respiratory pacemaker, which can automatically adapt to various pathological ventilation conditions and metabolic demands. MethodsTo assist the model design, we have personalized a computational lung model, which incorporates the mechanics of ventilation and gas exchange. The model can respond to the device stimulation where the gas exchange model provides biofeedback signals to the device. We use a pacing device model with a proportional integral (PI) controller to illustrate our approach. ResultsThe closed-loop adaptive pacing model can provide superior treatment compared to open-loop operation. The adaptive pacing stimuli can maintain physiological oxygen levels in the blood under various simulated breathing disorders and metabolic demands. ConclusionWe demonstrate that the respiratory pacing devices with the biofeedback can adapt to individual needs, while the lung model can be used to validate and parametrize the device. SignificanceThe closed-loop model-based framework paves the way towards an individualized and autonomous respiratory pacing device development.

Attention enhancement system for college students with brain biofeedback signals based on virtual reality

Attention Deficit Hyperactivity Disorder (ADHD) is a common and heritable disease that has an environmental influence on brain function. The diseases affects multiple aspects of the lives of college students, not only on their study but also on the relationships with other people. The problem with ADHD attention involves short term memory. The purpose of this paper is to investigate the capability of improving short term working memory for ADHD patients by the aid of technology a proper VR environment is built for ADHD, who are isolated from the real circumference. Electroencephalography (EEG) is taken as biofeedback to read the brain signal from the patient. A deep learning approach and an artificial neural network method, are employed to efficiently and accurately process EEG. The findings of the trial indicate that the virtual reality recommended system will play a greater role in improving the attention to the ADHD patient.

Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation.

Deep brain stimulation has developed into an established treatment for movement disorders and is being actively investigated for numerous other neurological as well as psychiatric disorders. An accurate electrode placement in the target area and the effective programming of DBS devices are considered the most important factors for the individual outcome. Recent research in humans highlights the relevance of widespread networks connected to specific DBS targets. Improving the targeting of anatomical and functional networks involved in the generation of pathological neural activity will improve the clinical DBS effect and limit side-effects. Here, we offer a comprehensive overview over the latest research on target structures and targeting strategies in DBS. In addition, we provide a detailed synopsis of novel technologies that will support DBS programming and parameter selection in the future, with a particular focus on closed-loop stimulation and associated biofeedback signals.

A Limb Tracking Platform for Tele-Rehabilitation

The adoption of motor-rehabilitative therapies is highly demanded in a society where the average age of the population is constantly increasing. A recent trend to contain costs while providing high quality of healthcare services is to foster the adoption of self-care procedures, performed primarily in patients’ environments rather than in hospitals or healthcare structures, especially in the case of intensive and chronic patients’ rehabilitation. This work presents a platform to enhance limb functional recovery through telerehabilitation sessions. It relies on a sensing system based on inertial sensors and data fusion algorithms, a module to provide bio-feedback tailored to the users, and a module dedicated to the physicians’ practices. The system design had to face several cyber-physical challenges due to the tight interaction between patient and sensors. For instance, integrating the body kinematics into the sensory processing improved the precision of measurements, simplified the calibration procedure, and made it possible to generate bio-feedback signals. The precision of the proposed system is presented through a set of experiments, showing a resolution below one degree in monitoring joint angles. A validation of the proposed solution has been performed through a medical trial on 50 patients affected by osteo-articular diseases. The presented framework has been designed to operate in other application fields, such as neurological rehabilitation (e.g., Parkinson, Stroke, etc.), sports training, and fitness activities.

PHYSIOLOGICAL BASICS OF CARDIORESPIRATORY BIOFEEDBACK CONTROL OF OSCILLATORY POSTURAL LOAD

The technology of functional normalization of the cardiovascular system parameters by means of the alternating biofeedback on cardiac rhythm (cardiorespiratory training) with the use of a turning bed (verticalizer) is described, which allows to implement the oscillatory postural effects of ortho- and antiorthostatic nature automatically. Aim of the study. Increasing of application area of cardiorespiratory training due to the variety of options for biofeedback signals. Methods. The oscillation of the turning bed controlled by means of the parameters of heart rate variability sets for the examinee the order of alternation of the inspiration and expiration phases and the phases of the inspiration/expiration beginning. The method is available to patients with low vision, bedridden patients, early ages children; patients who are not able to understand the essence of the procedure. Results of researches. Positive dynamics of the test subjects’ functional state is, in particular, the normalization of activity of components of the autonomic nervous system; the reinstatement of the natural breathing pattern lost due to various reasons; increasing the variability of the heart rate; the cardiovascular system training due to moderate alternating ortho- and antiorthostatic load, estimated by number of physiological parameters.

A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks.

There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a novel biologically plausible supervised learning algorithm for learning precisely timed multiple spikes in a multilayer SNNs. Based on the spike-timing-dependent plasticity learning rule, the proposed learning method trains an SNN through the synergy between weight and delay learning. The weights of the hidden and output neurons are adjusted in parallel. The proposed learning method captures the contribution of synaptic delays to the learning of synaptic weights. Interaction between different layers of the network is realized through biofeedback signals sent by the output neurons. The trained SNN is used for the classification of spatiotemporal input patterns. The proposed learning method also trains the spiking network not to fire spikes at undesired times which contribute to misclassification. Experimental evaluation on benchmark data sets from the UCI machine learning repository shows that the proposed method has comparable results with classical rate-based methods such as deep belief network and the autoencoder models. Moreover, the proposed method can achieve higher classification accuracies than single layer and a similar multilayer SNN.

Adjustment of the Vocal Tract Shape via Biofeedback: A Case Study

In this study, an adjustment of the vocal tract shape toward selected sound waves in the frequency range of the first and second formants without phonation is discussed. The sound waves of a loudspeaker in front of the open mouth and amplified by the vocal tract are used as biofeedback signals. It is shown that the resonance amplification of the vocal tract complies with the concept of forced oscillation, with the driver being the sound source and the resonator being the vocal tract. An adjustment toward increased amplification via vocal tract resonance can be related to smaller bandwidths and lower damping. Furthermore, the applied adjustment frequencies are preserved as vocal tract resonances during exhalation and even phonation. This novel form of biofeedback might enrich standard voice training procedures by exercises without phonation.

Generating the Visual Biofeedback Signals Applicable to Reduction of Wrist Spasticity: A Pilot Study on Stroke Patients.

Introduction:Application of biofeedback techniques in rehabilitation has turned into an exciting research area during the recent decade. Providing an appropriate visual or auditory biofeedback signal is the most critical requirement of a biofeedback technique. In this regard, changes in Surface Electromyography (SEMG) signals during wrist movement can be used to generate an indictable visual biofeedback signal for wrist movement rehabilitation via SEMG biofeedback. This paper proposes a novel methodology for selecting the most appropriate features out of wrist muscle SEMG signals.Methods:To this end, the surface EMG signals from flexor and extensor muscle groups during wrist joint movements were recorded and analyzed. Some linear and nonlinear features in frequency, time, and time-frequency domains were extracted from the recorded surface EMG signals of the flexor and extensor muscles. Experiments and analyses were performed on ten healthy subjects and four stroke patients with wrist muscle spasticity as the movement disorder subjects. Some heuristic feature selection measures were applied. The main motivation behind choosing applied heuristic feature selection measures was meeting. In the first step, the designed visual biofeedback signal should indicate a healthy wrist motion profile as its successful tracking by the patient guarantees rehabilitation. In addition, the visual biofeedback signal should be a smooth curve thus preventing the patient from discomfort while tracking it on a monitor during the biofeedback therapy.Results:In this pilot study, after using the introduced feature selection measures, quantitative and qualitative analyses of the extracted features indicated that Shannon entropy is the most appropriate feature for generating a visual biofeedback signal as a healthy wrist motion profile to improve the ability of stroke patients in controlling wrist joint motion. In addition, it was shown that when the wrist joint moves between a flexed and rest position, the flexor muscle EMG signal should be used for generating a visual biofeedback signal. However when the wrist joint moves between a rest position and an extended position, the extensor muscle EMG signal is appropriate for providing a visual biofeedback signal. It is worth noting that the achieved pilot study results should be confirmed by the future studies with larger samples.Conclusion:According to the obtained results, it can be concluded that among the analyzed features, the Shannon entropy was the most appropriate feature. It can be employed for generating a visual biofeedback signal for reduction of spasticity in patients with stroke.

The Application of Virtual Reality and Augmented Reality Technologies in Biofeedback

Biofeedback is a therapy that has more than 50 years of history. However, by combining with virtual reality (VR) and augmented reality (AR) technologies, biofeedback is becoming more and more widely used for different clinical conditions. VR and AR provide a new way to deliver the biofeedback signals, by which is more effective. In this article, we reviewed the works of literature on the development of VR and AR technologies and the field of VR/AR biofeedback. We also pointed out its problem nowadays was not only the lack of sufficient research results, but also the low acceptance of VR/AR among elderly people and the technological bottlenecks of the head-mounted displays.

Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans.

Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of seven major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested six movement directions and four force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.

Different Brain Wave Patterns and Cortical Control Abilities in Relation to Different Creative Potentials

Contemporary understanding of brain functions provides a way to probe into the mystery of creativity. However, the prior evidence regarding the relationship between creativity and brain wave patterns reveals inconsistent conclusions. One possible reason might be that the means of selecting creative individuals in the past has varied in each study. By distinguishing creative potential as open-ended versus closed-ended based on theoretical views, this study examined different brain wave patterns and cortical control abilities in relation to different creative potentials by using electroencephalogram (EEG) biofeedback equipment. The results demonstrated that participants’ performance on the open-ended creative problem was positively related to EEG alpha frequencies, whereas performance on the closed-ended creative problem was related to larger variability in EEG dynamics between alpha and beta waves when performing either open-ended or closed-ended creativity tasks. Further, better control in changing states of brain wave activities according to the EEG biofeedback signals could predict closed-ended creativity performance. Open-ended creativity was related only to the enhancement of alpha signals. These results help clarify previous inconsistent findings, reveal different natures of distinct creativities, and further suggest ways to improve different aspects of creativity with modified biofeedback procedures.

Effectiveness of different visual biofeedback signals for human balance improvement

The aim of this study was to examine the effectiveness of visual biofeedback (VBF) signals from a force platform and accelerometer sensors placed on different body segments. The study was performed on 20 young subjects during standing on a firm and foam support surface with a VBF signal sensed from CoP, lower trunk (L5) and upper trunk (Th4). The VBF signal was controlled by 2D-movement of chosen body segment, which was presented as a red point on a monitor screen. Location of VBF signal had a significant effect on each postural parameter of CoP and trunk segments. RMS and amplitudes of postural sway in medial–lateral and anterior–posterior directions decreased during standing on both types of support surface due to VBF. L5-VBF and CoP-VBF significantly reduced CoP displacements and lower trunk tilts. Th4-VBF reduced upper trunk tilts. Frequency analysis of postural sway revealed a decrease of power spectral density (PSD) values in low frequency range (0.02–0.3Hz) and an increase of PSD values in higher frequency range (0.5–1.4Hz) in the VBF conditions during the stance on the firm surface in anterior–posterior direction. Reduction of body sway was the most significant in the body segment from which the VBF signal was sensed. The CoP position and L5 position provided the best signals for VBF. Changes in frequency ranges of body sway suggest voluntary activation of balance control. The results open new opportunities to optimize VBF system for balance improvement using accelerometers.

Biofeedback in rehabilitation

This paper reviews the literature relating to the biofeedback used in physical rehabilitation. The biofeedback methods used in rehabilitation are based on biomechanical measurements and measurements of the physiological systems of the body. The physiological systems of the body which can be measured to provide biofeedback are the neuromuscular system, the respiratory system and the cardiovascular system. Neuromuscular biofeedback methods include electromyography (EMG) biofeedback and real-time ultrasound imaging (RTUS) biofeedback. EMG biofeedback is the most widely investigated method of biofeedback and appears to be effective in the treatment of many musculoskeletal conditions and in post cardiovascular accident (CVA) rehabilitation. RTUS biofeedback has been demonstrated effective in the treatment of low back pain (LBP) and pelvic floor muscle dysfunction. Cardiovascular biofeedback methods have been shown to be effective in the treatment of a number of health conditions such as hypertension, heart failure, asthma, fibromyalgia and even psychological disorders however a systematic review in this field has yet to be conducted. Similarly, the number of large scale studies examining the use of respiratory biofeedback in rehabilitation is limited. Measurements of movement, postural control and force output can be made using a number of different devices and used to deliver biomechanical biofeedback. Inertial based sensing biofeedback is the most widely researched biomechanical biofeedback method, with a number of studies showing it to be effective in improving measures of balance in a number of populations. Other types of biomechanical biofeedback include force plate systems, electrogoniometry, pressure biofeedback and camera based systems however the evidence for these is limited. Biofeedback is generally delivered using visual displays, acoustic or haptic signals, however more recently virtual reality (VR) or exergaming technology have been used as biofeedback signals. VR and exergaming technology have been primarily investigated in post-CVA rehabilitation, however, more recent work has shown this type of biofeedback to be effective in improving exercise technique in musculoskeletal populations. While a number of studies in this area have been conducted, further large scale studies and reviews investigating different biofeedback applications in different clinical populations are required.

Temporal Synchronisation of Data Logging in Racing Gameplay

Contemporary game play is a complex environment where user interacts with many elements. Data logging in a game play is a common practice to study a player's in-game behaviour. Identified behavioural knowledge is then applied to enhance the game elements. Analyzing the user behaviour is a multivariate process and therefore requires more information than mere logging of game context. Multi modal data channels such as biofeedback signals are increasingly used to study the game play so that more detailed understanding of the user behaviour can be established. Precise synchronisation in capturing of multiple streams is essential to produce accurate and meaningful information. This paper presents a generic technique to accurately synchronize multiple data streams captured during a gaming session. It is demonstrated by applying it to a driving game Simulation. Observable correlation between the in-game data and psycho-physiological signals are presented to demonstrate the accuracy and granularity of the synchronisation.

Smart prevention device for foot infection

Each year 30 millions of foot ulcers occur worldwide and 56% of them will become infected. These infected foot ulcer represents the leading cause of lower-limb amputation in the world and shortens dramatically the lifetime of people. The reduction of high plantar pressure in patients with peripheral neuropathy such as diabetic people is mandatory for the prevention of foot ulcers and amputations. The device Power Insoles is an electronic shoe insole that monitor plantar pressure throughout the day, and prevent apparition of foot ulcers by informing the patient through biofeedback signals on a smartphone.

Effects of Electromyography Biofeedback-Assisted Relaxation on Pain in Patients With Advanced Cancer in a Palliative Care Unit

Most patients with advanced cancer experience pain. However, many cancer patients do not find satisfaction with conventional treatment of pain relief. This study examined the effect of electromyography (EMG) biofeedback-assisted relaxation on cancer-related pain in advanced cancer patients. We hypothesized that changes in EMG activity in frontal muscles underlie the efficacy of EMG biofeedback-assisted relaxation. This was a randomized control study. The experimental group (n = 12) received 6 EMG biofeedback-assisted relaxation sessions over a 4-week period, whereas the control group (n = 12) received conventional care. The primary efficacy measure was the level of pain, measured by the Brief Pain Inventory. Findings from this study show that relaxation training supplemented with visual and auditory EMG biofeedback signals is effective in reducing cancer-related pain in advanced cancer patients, possibly through a mechanism of attenuation of physiological arousal. Electromyography biofeedback-assisted relaxation training may be used along with medications for effective pain management in patients with advanced cancer.

Increasing cortical activity in auditory areas through neurofeedback functional magnetic resonance imaging

We report a functional magnetic resonance imaging method to deliver task-specific brain activities as biofeedback signals to guide individuals to increase cortical activity in auditory areas during sound stimulation. A total of 11 study participants underwent multiple functional magnetic resonance imaging scan sessions, while the changes in the activated cortical volume within the primary and secondary auditory areas were fed back to them between scan sessions. On the basis of the feedback information, participants attempted to increase the number of significant voxels during the subsequent trial sessions by adjusting their level of attention to the auditory stimuli. Results showed that the group of individuals who received the feedback were able to increase the activation volume and blood oxygenation level-dependent signal to a greater degree than the control group.