An Empirical Analysis of Neurofeedback Using PID Control Systems

Neurofeedback systems can be modeled as closed loop control systems with negative feedback. However, little work to date has investigated the potential of this representation in gaining a better understanding of the actual dynamics of neurofeedback towards explaining subjects' performance. In this paper, we analyze neurofeedback training data through a PID control model. We first show that PID model fitting can produce curves that are qualitatively aligned to the measured BCI signal. Secondly, we examine how brain activity during neurofeedback can be related to common characteristics of control systems. For this, we formalized a pre-existing neurofeedback EEG experiment using a SimulinkR model that captures both the neural activity and the external algorithm that was utilized to generate the feedback signal. We then used a regression model to fit individual trial data to PID coefficients for the control model. Our results suggest that successful trials tend to be associated to higher average values of Ki, which represents the error-reducing component of the PID controller. It hints that convergence in successful neurofeedback is progressive but complete in approaching the target

Augmented Virtual Reality Meditation : Shared Dyadic Biofeedback Increases Social Presence Via Respiratory Synchrony

Peer reviewe

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets

Biofeedback's effect on sports performance: a meta-analysis and analysis of moderators

Thesis (Ed.D.)--Boston UniversitySince the late 1970s, research and applied work has focused on the use of biofeedback as a technique to assist in the development of sports performance through different means, including improvement of sports skills, reduction of injuries, and improvement of muscle strength, among others. However, there is no scientific work statistically comparing these implementations using biofeedback. A meta-analysis was designed towards this gap in the literature; 33 investigations were gathered and statistically compared. Dependent variables, (e.g. the type of biofeedback, and the number of biofeedback sessions) were treated as moderators and their effect on the overall analysis were calculated. A random effect model was used due to the presence of heterogeneity across studies (I^2 = 54.95 (p<0.001, 95% CI), that included variations on the studies' compared outcomes. The meta-analysis' overall result showed a significant effect of biofeedback interventions on sports performance through a strong effect size, d = 0.72, with a high significance Z= 6.77, p<0.001, (95% confidence interval (CI) 0.51 - 0.93). Significant moderators' effects were found indicating that studies using EMG modality (d= 0.891, 95% CI 0.60 -1.18,p < 0.001, Z= 6.05), studies with the number of sessions higher than 8 (d = 0.84, 95% CI 0.40 -1.27, p<0.001, Z= 3.77), studies targeting outcomes indirectly linked to sports performance (d = 0.91, 95% CI 0.59- 1.22, p<0.001, Z = 5.64), and studies using biofeedback along with other interventions (d = 0.90, 95% CI 0.48 -1.32, p<0.001, Z= 4.18) had higher effect on the overall analysis. The meta-analysis findings are an important reference for researchers and practitioners using biofeedback, because they indicate that biofeedback interventions have a positive effect on sports performance. Moreover, the meta-analysis point to methodological factors playing an important role on interventions using biofeedback, as studies that had a greater effect were those with methods using EMG biofeedback modality, studies with more than eight biofeedback sessions, studies focusing on outcome measures indirectly related to sports performance, and studies that included biofeedback interventions along with other interventions

Classification of electroencephalogram (EEG) for lower limb movement of post stroke patients using artificial neural network (ANN)

Nowadays, many neurological conditions happen suddenly, such as stroke or spinal cord injury. This can cause chronic gait function impairment due to functional deficits in motor control. Current physiotherapy techniques such as functional electrical stimulation (FES) can be used to reconstruct some skills needed for movements of daily life. However, FES system provides only a limited degree of motor function recovery and has no mechanism for reflecting a patient’s motor intentions, hence requires novel therapies. Brain-Computer Interfaces (BCI) provides the means to decode mental states and activate devices according to user intentions. However, conventional BCI cannot be used fully, due to the lack of accuracy, and need some improvement. In addition to that, the integration of BCI with lower extremity FES systems has received less attention compared to the BCI-FES systems with upper extremity. The discussion of this thesis was divided into two parts, which were the BCI part as input and the functional electrical stimulator (FES) controller part as the output for this system. For BCI part, the main processes involved are brainwave signals classification and mapping process. Here the signal has been classed will be applied to match the appropriate rehabilitation exercise. Whereas for the FES part, the signal from the mapping system will be controlled by the controller to ensure that the target knee angle is achieved to make the rehabilitation process more effective. As a conclusion, patients can be classified into two classes based on their alpha and beta signals status and these must undergone rehabilitation sessions according to their post-stroke level. So the results proved that the ANN model developed was able to classify the post-stroke severity. Also, the result had proven that the BCI fuzzy-based mapping system in this study was able to work perfectly into mapping the post-stroke patient with a suitable exercise according to their post-stroke level

Neurological and Mental Disorders

Mental disorders can result from disruption of neuronal circuitry, damage to the neuronal and non-neuronal cells, altered circuitry in the different regions of the brain and any changes in the permeability of the blood brain barrier. Early identification of these impairments through investigative means could help to improve the outcome for many brain and behaviour disease states.The chapters in this book describe how these abnormalities can lead to neurological and mental diseases such as ADHD (Attention Deficit Hyperactivity Disorder), anxiety disorders, Alzheimer’s disease and personality and eating disorders. Psycho-social traumas, especially during childhood, increase the incidence of amnesia and transient global amnesia, leading to the temporary inability to create new memories.Early detection of these disorders could benefit many complex diseases such as schizophrenia and depression

Models of collaboration between psychologist and family doctor: a systematic review of primary care psychology

open2noThe prevalence of psychological suffering is greater than the actual request for clinical consultation in Europe (Alonso et al., 2004). In Italy, no more than 5.5% of the population requested psychological assistance during lifetime (Miglioretti et al., 2008). There are different obstacles that prevent the access to mental health services, such as economic restrictions (Mulder et al., 2011), cultural prejudice (Kim et al., 2010), and lack of knowledge about the service providers that can answer to the patient’s psychological needs (Molinari et al., 2012). Therefore, the psychologist is often consulted as a last resort, only after everything else has failed, when problems have become severe, and thus requiring longer, more intensive, and expensive treatments. The introduction of the Primary Care Psychologist, a professional who works together with the family doctor, allows to overcome the above-mentioned problems and intercept unexpressed needs for psychological assistance. This professional role is operating in many countries since several years. In this study, current literature concerning different models of collaboration between physician and psychologist, in Europe and in Italy, was reviewed. A systematic search of Web of Science (ISI), Pubmed, Scopus, and PsychINFO was conducted using the initial search terms Primary Care Psychologist, Family Doctor, Primary Care, Collaborative Practice, and several relevant papers were identified. The review has shown the improved quality of care when mental health care is integrated into primary. Analyzing how different programs are implemented, results indicated that the more efficacious models of Primary Care Psychology are those tailored on the environment’s needs.The results of our systematic review stress the importance of the Primary Care Psychologist implementation also in Italy, to intercept unexpressed psychological needs and enhance clients’ quality of life.openFrancesca, Bianco; Enrico, BenelliBianco, Francesca; Benelli, Enric

Reading the brain’s personality: using machine learning to investigate the relationships between EEG and depressivity

Electroencephalography (EEG) measures electrical signals on the scalp and can give information about processes near the surface of the brain (cortex). The goal of our research was to create models that predict depressivity (mapping to personality in general, not just sickness) and to find potential biomarkers in EEG data. First, to provide our models with cleaner EEG data, we designed a novel single-channel physiology-based eye blink artefact removal method and a mains power noise removal method. Then, we assessed two main machine learning model types (classification- and regression-based) with a total of eighteen sub-types to predict the depressivity of participants. The models were generated by combining four signal processing techniques with a) three classification techniques, and b) three regression techniques. The experimental results showed that both types of models perform well in depressivity prediction and one regression-based model (Reg-FFT-LSBoost) showed a significant depressivity prediction performance, especially for female group. More importantly, we found that a specific EEG frequency band (the gamma band) made major contributions to depressivity prediction. Apart from that, the alpha and beta band may make modest contributions. Specific locations (T7, T8, and C3) made major contributions to depressivity prediction. Frontal locations may also have some influence. We also found that the combination of both eye states’ EEG data showed a better depressivity prediction ability. Compared to the eyes closed data, the EEG data obtained from the state of eyes open were more suitable for assessing depressivity. In brief, the outcomes of this research provided the possibilities for translating the EEG data for depressivity measure. Furthermore, there are possibilities to extend the research to apply to other mental disorders’ prediction, such as anxiety

Using models of baseline gameplay to design for physical rehabilitation

Modified digital games manage to drive motivation in repetitive exercises needed for motor rehabilitation, however designing modifications that satisfy both rehabilitation and engagement goals is challenging. We present a method wherein a statistical model of baseline gameplay identifies design configurations that emulate behaviours compatible with unmodified play. We illustrate this approach through a case study involving upper limb rehabilitation with a custom controller for a Pac-Man game. A participatory design workshop with occupational therapists defined two interaction parameters for gameplay and rehabilitation adjustments. The parameters' effect on the interaction was measured experimentally with 12 participants. We show that a low-latency model, using both user input behaviour and internal game state, identifies values for interaction parameters that reproduce baseline gameplay under degraded control. We discuss how this method can be applied to systematically balance gamification problems involving trade-offs between physical requirements and subjectively engaging experiences.Comment: 19 pages, 10 figure