BRAINTRAIN will improve and adapt the methods of real-time fMRI neurofeedback (fMRI-NF) for clinical use, including the combination with electroencephalography (EEG) and the development of standardised procedures for the mapping of brain networks that can be targeted with neurofeedback.
Its core component will be the exploration of the efficacy of fMRI-NF in selected mental and neurodevelopmental disorders that involve motivational, emotional and social neural systems. The ultimate goals of BRAINTRAIN are therefore to :
- Develop new or optimize existing imaging technologies,
- Validate their application as a therapeutic tool to mental and behavioural disorders by integrating imaging data with complementarity knowledge resulting bioinformatics and clinical data,
- Allow the diagnosis of mental disorders at the pre-symptomatic stage or early during development,
- Better measure disease progression.
- Develop transfer technologies for fMRI-NF through EEG and serious games.
BRAINTRAIN is innovative in the development of new real-time imaging technologies e.g. new sequences, image reconstruction methods and data analysis software. This will also be the first clinical testing of fMRI-NF in a set of disorders with extraordinary socioeconomic and public health impact.
The project started in November 2013 and will last four years. It is coordinated by Cardiff University (Professor David Linden, Wales, UK).
BRAINTRAIN is a European research network (Collaborative Project) supported by the European Commission under the Health Cooperation Work Programme of the 7th Framework Programme, under the Grant Agreement n°602186.
We develop a brain-machine interface for the hand-motor rehabilitation of stroke patients. The interface provides both visual and proprioceptive feedback to the user based upon the successful generation of cortical motor commands. We discuss the details of the proposed system and provide a summary of the preliminary experiment. The experiment investigates the importance of simultaneous visual and proprioceptive feedback to the delivery of motor commands from the affected motor cortex of the patients. We also discuss a case study involving a chronic stroke patient who trained with the system for 14 days to recover functional movement in the hand. The results obtained by this study suggest that the developed system is effective at accelerating the recovery of motor function in stroke patients with hand paralysis.
Date of Conference: 13-16 Dec. 2016
Date Added to IEEE Xplore: 19 January 2017
Electronic ISBN: 978-9-8814-7682-1
Print on Demand(PoD) ISBN: 978-1-5090-2401-8
Source: Digital mirror box: An interactive hand-motor BMI rehabilitation tool for stroke patients – IEEE Xplore Document
Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user’s movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.
Stroke is a leading cause of adult disability around the world. A large number of stroke survivors are left with a unilateral arm or leg paralysis. After completing conventional rehabilitation therapy, a significant number of stroke survivors are left with limited reaching and grasping capabilities .
Source: Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient – IEEE Xplore Document
Estimation of fatigue is a required criteria in the field of physiology. The estimation of muscle fatigue and its development in the brain signals can provide a level of endurance among athletes and limits of a persons in doing physical tasks. In this paper a technique for detecting and estimating the fatigue development using regression parameters for EEG signals is discussed. The study of 14 subjects was undertaken and analysed for the fatigue development using Auto-Regression(AR) model. The behaviour of the error function obtained is analysed for the prediction of the stages and limits of muscle fatigue development.
Muscle fatigue is a phenomenon associated with the muscle contraction. It is understood as the reduction in the ability of maximal force generation by the muscle with time, during its stressing, as the muscle contraction keeps on increasing. The nervous system’s limitation to generate sustainable signals and the reduction of ability of muscle fiber to contract are two major factors contributing to fatigue development . Fatigue development limits the performance and capability of the individual in sports, long stretch driving conditions and in rigourous day to day activities. Hence a parameter that can estimate the fatigue levels and provide a break point for maximum fatigue can be useful for physiology and in other areas such as labour. People working under mines can be monitored for the fatigue break point and the overall productivity of such areas can be increased by proper analysis. The fatigue development in a person can be analysed via number of methods based on physiological changes. These include Electroencephalogram (EEG), Elec-tromyography(EMG), and Heart Rate Variability(HRV). Zadry et.al.  reported the increase in alpha band power level of EEG with time for fatigue development . Ali et.al. also reported increase in RMS values of different bands in EEG . Few studies measure brain activity in light repetitive task using EEG  to measure drowsiness or fatigue on drivers   and night work  . The EEG analysis for overall fatigue has been the focus of research, but research for specific muscle fatigue detection has been limited. The EEG based detection of fatigue has the advantage of quantitative based assessment. But, for real time application perspective faster computational power and signal processing methods are required. One of the challenges based on EEG based approach is the disturbances and contamination of the signal from eyes blinking action, muscle noise by movements and instrumental noises like line noise, electronic interferences . Another problem is imposed by the inter-variability and intra-variability in EEG dynamics accompanying loss of alertness .
Source: Fatigue detection and estimation using auto-regression analysis in EEG – IEEE Xplore Document
The objective of this work was to design and experiment a robotic hand rehabilitation device integrated with a wireless EEG system, going towards patient active participation maximization during the exercise. This has been done through i) hand movement actively triggered by patients muscular activity as revealed by electromyographic signals (i.e., a target hand movement for the rehabilitation session is defined, the patient is required to start the movement and only when the muscular activity overcomes a predefined threshold, the patient-initiated movement is supported); ii) an EEG-based biofeedback implemented to make the user aware of his/her level of engagement (i.e., brain rhythms power ratio Beta/Alpha). The designed system is composed by the Gloreha hand rehabilitation glove, a device for electromyographic signals recording, and a wireless EEG headset. A strong multidisciplinary approach was the base to reach this goal, which is the fruitful background of the Think and Go project. Within this project, research institutes (Politecnico di Milano), clinical centers (INRCA-IRCCS), and companies (ab medica s.p.a., Idrogent, SXT) have worked together throughout the development of the integrated robotic hand rehabilitation device. The integrated device has been tested on a small pilot group of healthy volunteers. All the users were able to calibrate and correctly use the system, and they reported that the system was more challenging to be used with respect to the standard passive hand mobilization session, and required more attention and involvement. The results obtained during the preliminary tests are encouraging, and demonstrate the feasibility of the proposed approach.
Source: Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback – IEEE Xplore Document
One of the recent trends in gait rehabilitation is to incorporate bio-signals, such as electromyography (EMG) or electroencephalography (EEG), for facilitating neuroplasticity, i.e. top-down approach. In this study, we investigated decoding stroke patients’ gait intention through a wireless EEG system. To overcome patient-specific EEG patterns due to impaired cerebral cortices, common spatial patterns (CSP) was employed. We demonstrated that CSP filter can be used to maximize the EEG signal variance-ratio of gait and standing conditions. Finally, linear discriminant analysis (LDA) classification was conducted, whereby the average accuracy of 73.2% and the average delay of 0.13 s were achieved for 3 chronic stroke patients. Additionally, we also found out that the inverse CSP matrix topography of stroke patients’ EEG showed good agreement with the patients’ paretic side.
Source: IEEE Xplore Document – Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG
Background: Previous reports have suggested that action observation training (AOT) is beneficial in enhancing the early learning of new motor tasks; however, EEG-based investigation has received little attention for AOT.
Objective: The purpose of this study was to illustrate the effects of AOT on hand dexterity and cortical activation in patients with post-stroke hemiparesis.
Method: Twenty patients with post-stroke hemiparesis were randomly divided into either the experimental group (EG) or control group (CG), with 10 patients in each group. Prior to the execution of motor tasks (carrying wooden blocks from one box to another), subjects in the EG and CG observed a video clip displaying the execution of the same motor task and pictures showing landscapes, respectively. Outcome measures included the box and block test (BBT) to evaluate hand dexterity and EEG-based brain mapping to detect changes in cortical activation.
Results: The BBT scores (EG: 20.50 ± 6.62 at pre-test and 24.40 ± 5.42 at post-test; CG: 20.20 ± 6.12 at pre-test and 20.60 ± 7.17 at post-test) revealed significant main effects for the time and group and significant time-by-group interactions (p < 0.05). For the subjects in the EG, topographical representations obtained with the EEG-based brain mapping system were different in each session of the AOT and remarkable changes occurred from the 2nd session of AOT. Furthermore, the middle frontal gyrus was less active at post-test than at pre-test.
Conclusions: These findings support that AOT may be beneficial in altering cortical activation patterns and hand dexterity.
Source: Effects of action observation therapy on hand dexterity and EEG-based cortical activation patterns in patients with post-stroke hemiparesis – Topics in Stroke Rehabilitation –
Following stroke, the brain undergoes various stages of recovery where the central nervous system can reorganize neural circuitry (neuroplasticity) both spontaneously and with the aid of behavioural rehabilitation and non-invasive brain stimulation. Multiple neuroimaging techniques can characterize common structural and functional stroke-related deficits, and importantly, help predict recovery of function. Diffusion tensor imaging (DTI) typically reveals increased overall diffusivity throughout the brain following stroke, and is capable of indexing the extent of white matter damage. Magnetic resonance spectroscopy (MRS) provides an index of metabolic changes in surviving neural tissue after stroke, serving as a marker of brain function. The neural correlates of altered brain activity after stroke have been demonstrated by abnormal activation of sensorimotor cortices during task performance, and at rest, using functional magnetic resonance imaging (fMRI). Electroencephalography (EEG) has been used to characterize motor dysfunction in terms of increased cortical amplitude in the sensorimotor regions when performing upper-limb movement, indicating abnormally increased cognitive effort and planning in individuals with stroke. Transcranial magnetic stimulation (TMS) work reveals changes in ipsilesional and contralesional cortical excitability in the sensorimotor cortices. The severity of motor deficits indexed using TMS has been linked to the magnitude of activity imbalance between the sensorimotor cortices.
In this paper we will provide a narrative review of data from studies utilizing DTI, MRS, fMRI, EEG and brain stimulation techniques focusing on TMS and its combination with uni and multi-modal neuroimaging methods to assess recovery after stroke. Approaches that delineate the best measures with which to predict or positively alter outcomes will be highlighted.
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In this paper we propose a full upper limb exoskeleton for motor rehabilitation of reaching, grasping and releasing in post-stroke patients. The presented system takes into account the hand pre-shaping for object affordability and it is driven by patient’s intentional control through a self-paced asynchronous Motor Imagery based Brain Computer Interface (MI-BCI). The developed antropomorphic eight DoFs exoskeleton (two DoFs for the hand, two for the wrist and four for the arm) allows full support of the manipulation activity at the level of single upper limb joint. In this study, we show the feasibility of the proposed system through experimental rehabilitation sessions conducted with three chronic post-stroke patients. Results show the potential of the proposed system for being introduced in a rehabilitation protocol.
Source: IEEE Xplore Abstract – A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI
Noninvasive transcranial brain stimulation (NTBS) is widely used to elucidate the contribution of different brain regions to various cognitive functions. Here we present three modeling approaches that are informed by functional or structural brain mapping or behavior profiling and discuss how these approaches advance the scientific potential of NTBS as an interventional tool in cognitive neuroscience.
(i) Leveraging the anatomical information provided by structural imaging, the electric field distribution in the brain can be modeled and simulated. Biophysical modeling approaches generate testable predictions regarding the impact of interindividual variations in cortical anatomy on the injected electric fields or the influence of the orientation of current flow on the physiological stimulation effects.
(ii) Functional brain mapping of the spatiotemporal neural dynamics during cognitive tasks can be used to construct causal network models. These models can identify spatiotemporal changes in effective connectivity during distinct cognitive states and allow for examining how effective connectivity is shaped by NTBS.
(iii) Modeling the NTBS effects based on neuroimaging can be complemented by behavior-based cognitive models that exploit variations in task performance.
For instance, NTBS-induced changes in response speed and accuracy can be explicitly modeled in a cognitive framework accounting for the speed–accuracy trade-off. This enables to dissociate between behavioral NTBS effects that emerge in the context of rapid automatic responses or in the context of slow deliberate responses. We argue that these complementary modeling approaches facilitate the use of NTBS as a means of dissecting the causal architecture of cognitive systems of the human brain.
via Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level.