Posts Tagged Neurofeedback

[Abstract] Preliminary results of testing the recoveriX system on stroke patients 

Abstract

Motor imagery based brain-computer interfaces (BCI) extract the movement intentions of subjects in real-time and can be used to control a cursor or medical devices. In the last years, the control of functional electrical stimulation (FES) devices drew researchers’ attention for the post-stroke rehabilitation field. In here, a patient can use the movement imagery to artificially induce movements of the paretic arms through FES in real-time.

Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10 to 24 sessions lasting about 40 min each with the recoveriX® system. The patients had to imagine 80 left and 80 right hand movements. The electroencephalogram (EEG) data was analyzed with Common Spatial Patterns (CSP) and linear discriminant analysis (LDA) and a feedback was provided in form of a cursor on a computer screen. If the correct imagination was classified, the FES device was also activated to induce the right or left hand movement.

In at least one session, all patients were able to achieve a maximum accuracy above 96%. Moreover, all patients exhibited improvements in motor function. On one hand, the high accuracies achieved within the study show that the patients are highly motivated to participate into a study to improve their lost motor functions. On the other hand, this study reflects the efficacy of combining motor imagination, visual feedback and real hand movement that activates tactile and proprioceptive systems.

Source: O174 Preliminary results of testing the recoveriX system on stroke patients – Clinical Neurophysiology

Advertisements

, , , , , , , , , , , ,

Leave a comment

[Abstract+References] High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports 

Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients’ home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.

1. Jones TA, Adkins DL. Motor system reorganization after stroke: stimulating and training toward perfection. Physiology (Bethesda). 2015;30:358370. Google Scholar Medline
2. Murray CJL, Vos T, Lozano R, . Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:21972223. Google Scholar CrossRef, Medline
3. Hankey GJ, Jamrozik K, Broadhurst RJ, Forbes S, Anderson CS. Long-term disability after first-ever stroke and related prognostic factors in the Perth Community Stroke Study, 1989-1990. 2002;33:10341040. Google Scholar
4. Jeannerod M. Neural simulation of action: a unifying mechanism for motor cognition. Neuroimage. 2001;14:103109. Google Scholar CrossRef, Medline
5. Cicinelli P, Marconi B, Zaccagnini M, Pasqualetti P, Filippi MM, Rossini PM. Imagery-induced cortical excitability changes in stroke: a transcranial magnetic stimulation study. Cereb Cortex. 2006;16:247253. Google Scholar CrossRef, Medline
6. Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol. 2009;8:741754. Google Scholar CrossRef, Medline
7. Page SJ, Levine P, Leonard AC. Effects of mental practice on affected limb use and function in chronic stroke. Arch Phys Med Rehabil. 2005;86:399402. Google Scholar CrossRef, Medline
8. Crosbie JH, McDonough SM, Gilmore DH, Wiggam MI. The adjunctive role of mental practice in the rehabilitation of the upper limb after hemiplegic stroke: a pilot study. Clin Rehabil. 2004;18:6068. Google Scholar Link
9. Liu KP, Chan CC, Lee TM, Hui-Chan CW. Mental imagery for promoting relearning for people after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2004;85:14031408. Google Scholar CrossRef, Medline
10. Grosse-Wentrup M, Mattia D, Oweiss K. Using brain-computer interfaces to induce neural plasticity and restore function. J Neural Eng. 2011;8:025004. Google Scholar CrossRef
11. Buch E, Weber C, Cohen LG, . Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. 2008;39:910917. Google Scholar CrossRef, Medline
12. Broetz D, Braun C, Weber C, Soekadar SR, Caria A, Birbaumer N. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil Neural Repair. 2010;24:674679. Google Scholar Link
13. Caria A, Weber C, Brötz D, . Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology. 2011;48:578582. Google Scholar CrossRef, Medline
14. Ramos-Murguialday A, Broetz D, Rea M, . Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol. 2013;74:100108. Google Scholar CrossRef, Medline
15. Shindo K, Kawashima K, Ushiba J, . Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. J Rehabil Med. 2011;43:951957. Google Scholar CrossRef, Medline
16. Pichiorri F, Morone G, Petti M, . Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015;77:851865. Google Scholar CrossRef, Medline
17. Zich C, Debener S, Kranczioch C, Bleichner MG, Gutberlet I, De Vos M. Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery. Neuroimage. 2015;114:438447. Google Scholar CrossRef, Medline
18. Debener S, Minow F, Emkes R, Gandras K, De Vos M. How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology. 2012;49:16171621. Google Scholar CrossRef, Medline
19. Kranczioch C, Zich C, Schierholz I, Sterr A. Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation. Int J Psychophysiol. 2014;91:1015. Google Scholar CrossRef, Medline
20. De Vos M, Kroesen M, Emkes R, Debener S. P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier. J Neural Eng. 2014;11:036008. Google Scholar CrossRef
21. Debener S, Emkes R, De Vos M, Bleichner M. Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Sci Rep. 2015;5:16743. Google Scholar CrossRef, Medline
22. Renard Y, Lotte F, Gibert G, . OpenViBE : an open-source software platform to design, test, and use brain-computer interfaces in real and virtual. Presence. 2010;19:3553. Google Scholar CrossRef
23. Ward NS, Brown MM, Thompson AJ, Frackowiak RSJ. Neural correlates of outcome after stroke: a cross-sectional fMRI study. Brain. 2003;126:14301448. Google Scholar CrossRef, Medline
24. Ward NS, Brown MM, Thompson AJ, Frackowiak RSJ. Neural correlates of motor recovery after stroke: a longitudinal fMRI study. Brain. 2003;126:24762496. Google Scholar CrossRef, Medline
25. Fugl-Meyer AR, Jääskö L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand J Rehabil Med. 1975;7:1331. Google Scholar Medline
26. Pandyan AD, Johnson GR, Price CI, Curless RH, Barnes MP, Rodgers H. A review of the properties and limitations of the Ashworth and modified Ashworth Scales as measures of spasticity. Clin Rehabil. 1999;13:373383. Google Scholar Link
27. Johansen-Berg H, Rushworth MFS, Bogdanovic MD, Kischka U, Wimalaratna S, Matthews PM. The role of ipsilateral premotor cortex in hand movement after stroke. Proc Natl Acad Sci U S A. 2002;99:1451814523. Google Scholar CrossRef, Medline
28. Feydy A, Carlier R, Roby-Brami A, . Longitudinal study of motor recovery after stroke: recruitment and focusing of brain activation. Stroke. 2002;33:16101617. Google Scholar CrossRef, Medline
29. Cramer SC, Nelles G, Benson RR, . A functional MRI study of subjects recovered from hemiparetic stroke. Stroke. 1997;28:25182527. Google Scholar CrossRef, Medline
30. Chollet F, DiPiero V, Wise RJ, Brooks DJ, Dolan RJ, Frackowiak RS. The functional anatomy of motor recovery after stroke in humans: a study with positron emission tomography. Ann Neurol. 1991;29:6371. Google Scholar CrossRef, Medline
31. Caramia MD, Iani C, Bernardi G. Cerebral plasticity after stroke as revealed by ipsilateral responses to magnetic stimulation. Neuroreport. 1996;7:17561760. Google Scholar CrossRef, Medline
32. Calautti C, Leroy F, Guincestre JY, Marié RM, Baron JC. Sequential activation brain mapping after subcortical stroke: changes in hemispheric balance and recovery. Neuroreport. 2001;12:38833886. Google Scholar CrossRef, Medline
33. Zich C, De Vos M, Kranczioch C, Debener S. Wireless EEG with individualized channel layout enables efficient motor imagery training. Clin Neurophysiol. 2015;126:698710. Google Scholar CrossRef, Medline
34. Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G, Müller K-R. The Berlin brain-computer interface: accurate performance from first-session in BCI-naïve subjects. IEEE Trans Biomed Eng. 2008;55:24522462. Google Scholar CrossRef, Medline
35. Blokland Y, Spyrou L, Thijssen D, . Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia. IEEE Trans Neural Syst Rehabil Eng. 2014;22:222229. Google Scholar CrossRef, Medline
36. Zich C, Debener S, Kranczioch C, Chen L-C, De Vos M. Lateralization patterns for movement execution and imagination investigated with concurrent EEG-fMRI and EEG-fNRIS. In: Müller-Putz GR, Huggins JE, Steyrl D, eds. Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future, Pacific Grove, California, USA. Graz, Austria: Verlag der Technischen Universität Graz; 2016:101. Google Scholar
37. Zich C, Debener S, Thoene A-K, Chen L-C, Kranczioch C. Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures. Neurobiol Aging. 2017;49:183197. Google Scholar CrossRef, Medline

Source: High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case ReportsClinical EEG and Neuroscience – Catharina Zich, Stefan Debener, Clara Schweinitz, Annette Sterr, Joost Meekes, Cornelia Kranczioch, 2017

, , , , , , , , ,

Leave a comment

[ARTICLE] Effects of neurofeedback on the short-term memory and continuous attention of patients with moderate traumatic brain injury: A preliminary randomized controlled clinical trial – Full Text

Abstract

Purpose

There are some studies which showed neurofeedback therapy (NFT) can be effective in clients with traumatic brain injury (TBI) history. However, randomized controlled clinical trials are still needed for evaluation of this treatment as a standard option. This preliminary study was aimed to evaluate the effect of NFT on continuous attention (CA) and short-term memory (STM) of clients with moderate TBI using a randomized controlled clinical trial (RCT).

Methods

In this preliminary RCT, seventeen eligible patients with moderate TBI were randomly allocated in two intervention and control groups. All the patients were evaluated for CA and STM using the visual continuous attention test and Wechsler memory scale-4th edition (WMS-IV) test, respectively, both at the time of inclusion to the project and four weeks later. The intervention group participated in 20 sessions of NFT through the first four weeks. Conversely, the control group participated in the same NF sessions from the fifth week to eighth week of the project.

Results

Eight subjects in the intervention group and five subjects in the control group completed the study. The mean and standard deviation of participants’ age were (26.75±15.16) years and (27.60±8.17) years in experiment and control groups, respectively. All of the subjects were male. No significant improvement was observed in any variables of the visual continuous attention test and WMS-IV test between two groups (p≥0.05).

Conclusion

Based on our literature review, it seems that our study is the only study performed on the effect of NFT on TBI patients with control group. NFT has no effect on CA and STM in patients with moderate TBI. More RCTs with large sample sizes, more sessions of treatment, longer time of follow-up and different protocols are recommended.


Introduction

Traumatic brain injury (TBI) means an injury to the brain that is caused by an external physical force. It is well known that TBI is an important cause of mortality and morbidity and it is reported that each year about 1.7 million people sustain a TBI in USA. Some of them die (about 50,000) and some other experience long-term disability (80,000 to 90,000).12 ;  3 The severity of TBI can be categorized based on the Glasgow comma scale (GCS) at the time of injury as follows: mild (13-15), moderate (9-12) and severe (<9).4 TBI usually affect the brain function such as cognitive status, executive function, memory, data processing, language skills and attention.5 It has heterogeneous aspects and based on the injury location and type. It can have different presentations. Hence it is considered as a difficult one to treat.6

The brain plasticity could help it in rehabilitation phase to restore its normal function after any trauma or disease. But the amount of this ability is poorly understood. Some studies approved that neurofeedback therapy (NFT) can promote neuroplasticity.7 In the method of neurofeedback (NF), as a non-pharmacological intervention, the feedback to brain waves which are representative of subconscious neural activity can be observed by the client and then he/she will be able to control and change them.8 ;  9 There are some evidences that show NFT can be useful in some other diseases like Obsessive-compulsive disorder,10 attention-deficit/hyperactivity disorder11 and also refractory epilepsy.12 There are also some published studies about the effect of NFT on patients with TBI. Surmeli in 2007 investigated the effect of NFT on 24 patients with mild TBI and reported that NFT can result in significant improvement in test of variables of attention, beck depression inventory and minnesota multiphasic personality inventory.13 In a study in 2014, with evaluation of two patients with moderate head injury and without control group, it is reported that electroencephalogram biofeedback can lead to increase the cognitive scores and improve the concussion symptoms and finally concluded that NFT can be effective on the changes in the structural and functional connectivity among patients with moderate TBI.14

Although these published papers reported a positive effect of NFT on the TBI patients, we have not enough data about the standard treatment protocol with NF, and literature still needs more original studies like randomized controlled clinical trial to suggest NF as a treatment option among patients with TBI regarding the two following functions of cognitive status: short-term memory (STM) and continuous attention (CA).6

In this preliminary study, we tried to evaluate the effect of NFT on CA and STM of patients with moderate TBI using a randomized controlled clinical trial. […]

Continue —> Effects of neurofeedback on the short-term memory and continuous attention of patients with moderate traumatic brain injury: A preliminary randomized controlled clinical trial

, , , , , , ,

Leave a comment

[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 :

tete ampoule v3

  • 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.

Visit Site

, , , , , , ,

Leave a comment

[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

, , , , , , ,

Leave a comment

[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.

NPH_3_3_031414_f001.png

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

, , , , , , ,

Leave a comment

[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
Download authors’ original image

 

, , , , , , , , ,

Leave a comment

[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…

, , , , , , , ,

Leave a comment

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.

, , , , , , , ,

Leave a comment

%d bloggers like this: