Posts Tagged Neurofeedback

[WEB SITE] BrainTrain

 

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 :

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  • 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.

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[ARTICLE] Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis – Full Text

Abstract

Background

The use of Brain–Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain’s capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training.

Methods

In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence.

Results

Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience.

Conclusions

Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information about the motor and affective state of the user that has the potential to be used to predict MI-BCI training outcome based on user’s profile. Finally, we propose a BCI paradigm in VR, which gives the possibility of motor priming for patients with low level of motor control.

Continue —> Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 2 MI-BCI training conditions. (a) VRMP: the user has to perform motor priming by mapping his/her hand movements into the virtual environment. (b) VR: the user has to perform training through simultaneous motor action observation and MI, before moving to the MI task were he/she has to control the virtual hands through MI. (c) Control: MI training with standard feedback through arrows-and-bars

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[Abstract] Review of functional near-infrared spectroscopy in neurorehabilitation – Neurophotonics – SPIE

Abstract

We provide a brief overview of the research and clinical applications of near-infrared spectroscopy (NIRS) in the neurorehabilitation field. NIRS has several potential advantages and shortcomings as a neuroimaging tool and is suitable for research application in the rehabilitation field.

As one of the main applications of NIRS, we discuss its application as a monitoring tool, including investigating the neural mechanism of functional recovery after brain damage and investigating the neural mechanisms for controlling bipedal locomotion and postural balance in humans. In addition to being a monitoring tool, advances in signal processing techniques allow us to use NIRS as a therapeutic tool in this field.

With a brief summary of recent studies investigating the clinical application of NIRS using motor imagery task, we discuss the possible clinical usage of NIRS in brain–computer interface and neurofeedback.

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Source: Review of functional near-infrared spectroscopy in neurorehabilitation | Neurophotonics | SPIE

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[ARTICLE] MEG-based neurofeedback for hand rehabilitation – Full text HTML

Abstract

Background: Providing neurofeedback (NF) of motor-related brain activity in a biologically-relevant and intuitive way could maximize the utility of a brain-computer interface (BCI) for promoting therapeutic plasticity. We present a BCI capable of providing intuitive and direct control of a video-based grasp.

Methods: Utilizing magnetoencephalography’s (MEG) high temporal and spatial resolution, we recorded sensorimotor rhythms (SMR) that were modulated by grasp or rest intentions. SMR modulation controlled the grasp aperture of a stop motion video of a human hand. The displayed hand grasp position was driven incrementally towards a closed or opened state and subjects were required to hold the targeted position for a time that was adjusted to change the task difficulty.

Results: We demonstrated that three individuals with complete hand paralysis due to spinal cord injury (SCI) were able to maintain brain-control of closing and opening a virtual hand with an average of 63 % success which was significantly above the average chance rate of 19 %. This level of performance was achieved without pre-training and less than 4 min of calibration. In addition, successful grasp targets were reached in 1.96 ± 0.15 s. Subjects performed 200 brain-controlled trials in approximately 30 min excluding breaks. Two of the three participants showed a significant improvement in SMR indicating that they had learned to change their brain activity within a single session of NF.

Conclusions: This study demonstrated the utility of a MEG-based BCI system to provide realistic, efficient, and focused NF to individuals with paralysis with the goal of using NF to induce neuroplasticity.

Continue —> JNER | Full text | MEG-based neurofeedback for hand rehabilitation

 

Fig. 1. Schematic of the BCI used to translate SMR into proportional control of grasping. Beginning in the upper left, first, the power spectrum of data recorded from 36 sensorimotor MEG sensors (shown on a top-down view of the MEG helmet) are computed using 300 ms sliding windows. A mask is applied to these features to remove any components that did not exhibit desynchronization during calibration. Then a linear decoder applies weights (W) to the neural signal (N) to compute a hand velocity value (VH ). The velocity output from the decoder is scaled (g) to ensure movement speeds are appropriate for the task. The previous hand position (an image from the video sequence) is then updated more closed or more opened within the ROM based on the scaled velocity command. The picture representing the desired aperture is chosen from 25 possible images. A progressive change in the images appeared to participants as a grasping movie with a 76 ms refresh rate.
Foldes et al. Journal of NeuroEngineering and Rehabilitation 2015 12:85   doi:10.1186/s12984-015-0076-7
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[VIDEO] MRI scanning to make you feel better | Rainer Goebel – TEDxAmsterdam 2014

…Goebel and his team have developed an advanced software system for the real-time analysis of functional MRI brain scans. He scans the brain and analyzes brain activity in the regions of the brain related to the problem of the patient. The patient is shown this neuro-feedback real-time through a brain-computer interface. Through this feedback, a severely depressed person can visualize how his brain activity influences the way he feels and the way he can control these emotions by personally activating or de-activating activity in relevant parts of his brain, with astonishing results. Goebel also shows us the different neurological responses of different people, from one of the happiest men in the world to a girl with locked-in syndrome…

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ARTICLE: Functional recovery from chronic writers cramp by brain-computer interface rehabilitation: a case report – Full Text

…Dystonia is often currently treated with botulinum toxin injections to spastic muscles, or deep brain stimulation to the basal ganglia. In addition to these pharmacological or neurosurgical measures, a new noninvasive treatment concept, functional modulation using a brain-computer interface, was tested for feasibility. We recorded electroencephalograms (EEGs) over the bilateral sensorimotor cortex from a patient suffering from chronic writer’s cramp. The patient was asked to suppress an exaggerated beta frequency component in the EEG during hand extension…

μέσω BMC Neuroscience | Full text | Functional recovery from chronic writer¿s cramp by brain-computer interface rehabilitation: a case report.

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