Stroke, SHT
Stroke / craniocerebral trauma
Neurofeedback for stroke, SHT - based on learning theory, supportive
Who is Neuroflex suitable for?
For people who have suffered a traumatic brain injury or stroke and want to improve cognitive functions, attention and motor control.
Do you know that?
Fatigue, difficulty concentrating or reduced coordination of movement make it difficult to return to everyday life? Rehabilitation therapies help, but sometimes reach their limits.
What is neurofeedback?
Targeted training of beta and SMR activity as well as motor-relevant mu-rhythmics reorganizes cortical networks and promotes plasticity.
How does neurofeedback help?
Studies show improvements in attention, a reduction in pathological slow activity and an increase in fine motor skills, especially when neurofeedback is integrated early in the rehabilitation process.
Procedure of the training, which combines mobile neurofeedback and in-practice training
Start - initial consultation, trial training, qEEG
Phase 1 - Initial phase
(10-15 sessions)
Phase 2 - In-depth phase
(further 20-30 sessions)
Phase 3 - Transfer phase incl. everyday training
Studies on neurofeedback for TBI and stroke
In her conference contribution
Prasad G, Herman P, Coyle D, Mcdonough S, Crosbie J. Using motor imagery based brain-computer interface for post-stroke rehabilitation. In: Proceedings of the 4th International IEEE/EMBS Conference on Neural Engineering. 2009:258-262.
Prasad and colleagues (2009) describe a motor imagery-based brain-computer interface (BCI) for the rehabilitation of stroke patients. The idea is to trigger specific brain activities through mental images of movement (e.g. imagining a grasp), which are recorded by the BCI system and generate feedback in real time. This feedback is intended to guide patients to relearn or improve motor functions.
Key contents and results:
Motor imagery as a key component
The patients imagined active hand or arm movements without actually performing them. These mental simulations activate relevant motor areas and can be detected by EEG signals.
BCI processing
The system uses EEG sensors to measure brain activity. Specific patterns (e.g. in the µ/beta band) are extracted and converted into control signals for a feedback system.
The feedback can, for example, be a visual or virtual object that moves in proportion to the correct brain activity, enabling a learning process.
Rehabilitation approach
The training aims to reorganize affected areas of the brain by repeatedly reinforcing motor imagination. In this way, motor pathways are promoted and residual functions are maintained or improved.
This can support the recovery of motor skills, particularly in the early stages after a stroke.
Preliminary effectiveness
Initial tests with stroke patients showed that they were able to use the BCI system successfully; progress in motor control was also visible.
However, the authors emphasize that further studies with larger numbers of test subjects are needed to validate the long-term effectiveness and optimal training protocols.
Conclusion:
Motor imagery-based BCIs represent a promising approach in stroke rehabilitation, as they specifically encourage the recovery of motor skills through mental exercises. The results of the pilot studies presented indicate positive effects, but more extensive studies are required to further substantiate the approach and its sustainable effect.
In this overview article
Wang W, Collinger JL, Perez MA, et al. Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. Phys Med Rehabil Clin N Am. 2010;21(1):157-178.
PubMed:
DOI-Link (ScienceDirect/Elsevier):
https://doi.org/10.1016/j.pmr.2009.07.003
Wang and colleagues (2010) discuss various forms of neural interface technology (NIT) and their potential in rehabilitation after neurological injury. The authors consider both invasive and non-invasive brain-computer interfaces and emphasize how they can be used to trigger and promote neuroplasticity in a targeted manner.
Core contents and significance for stroke and traumatic brain injury
Objectives and neuroplasticity
The authors explain that the brain can reorganize itself after an injury – such as a stroke or traumatic brain injury. This adaptation process (neuroplasticity) is to be accelerated and strengthened through targeted training with neural interfaces.
Specifically, this involves technologies that capture brain signals and translate them either into external devices (e.g. prostheses) or into direct feedback (e.g. visual feedback). The aim is to teach patients to gradually restore lost motor or sensory functions.
Methods and fields of application
Non-invasive methods: EEG-based brain-computer interfaces can be used, for example, to detect motor imagery and translate it into movements. This is particularly relevant for stroke patients and people with traumatic brain injury, as they often have severe motor deficits.
Invasive approaches: Implanted electrodes in the brain (e.g. in cases of severe paralysis) establish a direct connection to motor areas. This option is considered in severe cases where conventional rehabilitation measures only have a limited effect.
Practical rehabilitation approaches
The work shows examples of how test subjects with a stroke or traumatic brain injury use NIT to control an external device (e.g. a robotic arm). During this process, specific areas of the brain are repeatedly activated, resulting in new neuronal connections.
Continuous feedback (visual, auditory or tactile) is important here, as it informs the patient directly whether they have successfully implemented their motoric idea. This initiates a learning process that specifically “trains” the neuronal circuits.
State of research and challenges
According to the authors, although initial clinical results are promising (e.g. improved arm and hand function), large-scale studies are still lacking to prove efficacy and optimal protocols for different patient groups (stroke vs. traumatic brain injury).
Technical aspects (such as signal processing, robustness) and individual factors (motivation, extent of brain damage) pose further challenges.
Conclusion
The article by Wang et al. emphasizes that modern brain-computer interfaces, whether invasive or non-invasive, have strong potential for rehabilitation after stroke and traumatic brain injury. The key is to stimulate targeted neuroplasticity in injured or inactive brain regions through repeated training. While the results of the studies to date are encouraging, the authors emphasize that larger, standardized and systematic studies are needed to provide evidence of long-term success and the best protocols for different patient groups.
Do you have any questions? I am here for you.
Wolfgang Maier
MA in Special Education HfH
MAS in Neuropsychology UZH
Wolfgang Maier
MA in Special Education HfH
MAS in Neuropsychology UZH