4.Armagan, O., Tascioglu, F., Oner, C.: Electromyographic biofeedback in the treatment of the hemiplegic hand: a placebo-controlled study. Am. J. Phys. Med. Rehabil. 82, 856–861 (2003). https://doi.org/10.1097/01.PHM.0000091984.72486.E0CrossRefGoogle Scholar
8.Spicer, R., Anglin, J., Krum, D.M., Liew, S.L.: REINVENT: a low-cost, virtual reality brain-computer interface for severe stroke upper limb motor recovery. In: Proceedings of IEEE Virtual Reality, pp. 385–386 (2017). https://doi.org/10.1109/vr.2017.7892338
10.Kothe, C.: Lab Streaming Layer (LSL). https://github.com/sccn/labstreaminglayer
Posts Tagged biofeedback
This guide describes a sampling of these at-home biofeedback assistive technology (AT) devices that may help users better understand, interpret, and manage depressive effects that involve your brain, heart, and muscles. Biofeedback AT devices are designed to assist with monitoring and voluntarily controlling certain mental and physical functions such as increasing mental focus, regulating breathing, or relaxing muscles to get brainwaves, heartrate, and muscle tension levels back to normal intensities.
By the Biomechanics of Human Movement research group of Ghent University
The repetitive impact that is experienced upon collision with the ground may be related to running injuries but is modifiable. Lower-impact running can be achieved by receiving biofeedback in response to the peak tibial acceleration, but when are runners adapting to lower-impact running? Ten runners with high impact, herein defined as the peak tibial acceleration, ran 25 minutes on an athletic track in the Flanders Sports Arena. They were equipped with a wearable biofeedback system that accurately measures, monitors, and sonifies the impact in real-time. The system consists of 2 lightweight accelerometers, which attach to each leg of the runner, and an application hidden in a backpack for the provision of perceivable music-based biofeedback.
A change-point analysis was used to detect any change in impact (see Figure 2). No changes occurred whilst running without the biofeedback. The impact changed in the biofeedback condition. The major change was a reduction in impact, which occurred after almost 700 strides or after about 8 minutes. However, the time needed to achieve the major reduction varied considerably between the runners.
The simple analysis tool can aid physicians seeking to determine the timing effects of gait retraining by means of biofeedback on impact loading. Because of the rather quick response to the gait retraining due to a strong sensorimotor coupling, Pieter Van den Berghe PhD and colleagues want to highlight the potential of an autonomous biofeedback system that provides real-time and auditory feedback for lower-impact running.
The Biomechanics of Human Movement research group focuses on understanding the neuromechanical interaction of the moving body and the environment to answer questions related to the optimization of learning processes, sports performance, and musculoskeletal loading. A research project focusing on overground running retraining by means of auditory biofeedback was initiated together with the institute for systematic musicology IPEM.
Source: Van den Berghe P, Gosseries M, Gerlo J, Lenoir M, Leman M, De Clercq D. Change-point detection of peak tibial acceleration in overground running retraining. Sensors. 2020;20(6):1720.
Figure 2. The temporal evolution in axial peak tibial acceleration of a participant. More details are given in the article published in Sensors’ special issue, Sensors for Biomechanics Application.
[Abstract + References] sEMG-biofeedback armband for hand motor rehabilitation in stroke patients: a preliminary pilot longitudinal study – IEEE Conference Publication
[ARTICLE] Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review – Full Text PDF
Digital biofeedback systems (DBSs) are used in physical rehabilitation to improve outcomes by engaging and educating patients and have the potential to support patients while doing targeted exercises during home rehabilitation. The components of feedback (mode, content, frequency and timing) can influence motor learning and engagement in various ways. The feedback design used in DBSs for targeted exercise home rehabilitation, as well as the evidence underpinning the feedback and how it is evaluated, is not clearly known. To explore these concepts, we conducted a scoping review where an electronic search of PUBMED, PEDro and ACM digital libraries was conducted from January 2000 to July 2019. The main inclusion criteria included DBSs for targeted exercises, in a home rehabilitation setting, which have been tested on a clinical population. Nineteen papers were reviewed, detailing thirteen different DBSs. Feedback was mainly visual, concurrent and descriptive, frequently providing knowledge of results. Three systems provided clear rationale for the use of feedback. Four studies conducted specific evaluations of the feedback, and seven studies evaluated feedback in a less detailed or indirect manner. Future studies should describe in detail the feedback design in DBSs and consider a robust evaluation of the feedback element of the intervention to determine its efficacy.
Walking deficits in people post-stroke are often multiple and idiosyncratic in nature. Limited patient and therapist resources necessitate prioritization of deficits such that some may be left unaddressed. More efficient delivery of therapy may alleviate this challenge. Here, we look to determine the utility of a novel principal component-based visual feedback system that targets multiple, patient-specific features of gait in people post-stroke.
Ten individuals with stroke received two sessions of visual feedback to attain a walking goal. This goal consisted of bilateral knee and hip joint angles of a typical ‘healthy’ walking pattern. The feedback system uses principal component analysis (PCA) to algorithmically weight each of the input features so that participants received one stream of performance feedback. In the first session, participants had to explore different patterns to achieve the goal, and in the second session they were informed of the goal walking pattern. Ten healthy, age-matched individuals received the same paradigm, but with a hemiparetic goal (i.e. to produce the pattern of an exemplar stroke participant). This was to distinguish the extent to which performance limitations in stroke were due neurological injury or the PCA based visual feedback itself.
Principal component-based visual feedback can differentially bias multiple features of walking toward a prescribed goal. On average, individuals with stroke typically improved performance via increased paretic knee and hip flexion, and did not perform better with explicit instruction. In contrast, healthy people performed better (i.e. could produce the desired exemplar stroke pattern) in both sessions, and were best with explicit instruction. Importantly, the feedback for stroke participants accommodated a heterogeneous set of walking deficits by individually weighting each feature based on baseline walking.
People with and without stroke are able to use this novel visual feedback to train multiple, specific features of gait. Important for stroke, the PCA feedback allowed for targeting of patient-specific deficits. This feedback is flexible to any feature of walking in any plane of movement, thus providing a potential tool for therapists to simultaneously target multiple aberrant features of gait.
Gait impairment following stroke often presents with multiple deficits. Some of the most common deficits include decreased paretic leg knee flexion during swing, hip circumduction, step length asymmetry, pelvic tilt, and decreased ankle dorsiflexion [1,2,3,4,5]. Unfortunately, resources (e.g. patient time/finances, therapist time, insurance coverage, etc), are limited, making it difficult to address all existing deficits in a single episode of care. Consequently, therapists are confronted with the challenge of using their clinical judgement to prioritize deficits, serially targeting those that they believe will most improve walking function and independence. Addressing one deficit in isolation of the others may introduce unintended compensations that further impair gait. Indeed, when manipulating a lower-limb movement pattern, lower-limb sagittal plane kinematics (e.g. hip/knee angles) are closely coupled [6,7,8].Thus, there remains a need for both the systematic prioritization of gait deficits and improvement in the efficiency of training so as to simultaneously address multiple patient-specific deficits.
Real-time visual feedback of gait kinematics has proven useful in altering targeted features of gait in healthy and neurological populations [9,10,11,12,13,14]. For example, Cherry-Allen et al. used visual feedback of joint angles to increase peak knee angle in people post-stroke . Moreover, visual feedback has been effective in improving gait speed, stride length, and stride width in people post-stroke [16,17,18]. Still, research protocols using visual feedback of kinematic gait parameters have two prominent issues when looking to improve individual patient deficits: 1) they are focused on altering one feature of walking while leaving others unconstrained and 2) they are predicated on the assumption that the targeted parameter is the most prominent deficit for the entire group of patients included in the particular study. Given the heterogeneity of deficits following stroke, it would be most beneficial to have a system that can accommodate a wide array of walking patterns.
We developed a novel method to generate individualized, yet simple, visual feedback for re-training walking on a treadmill. An innovative element of this process is the use of principal component analysis (PCA) to display a simple ‘summary’ of a multi-dimensional movement pattern that continuously updates on a screen in front of participants as they walk. PCA has applied to motion data in a number of previous studies to characterize whole-body movement in both healthy [19, 20] and pathological populations [21,22,23,24,25]. The question that we ask here is whether this novel, PCA-based visual feedback system can address multiple, patient-specific deficits simultaneously. For stroke patients, we established a goal walking pattern that included four kinematic dimensions (bilateral hip and knee joint angles) of an average ‘healthy’ walking pattern. Each of the four kinematic dimensions was individually weighted based on a participant’s baseline deficits (defined as the difference between baseline walking and the goal walking pattern). Thus, weights varied across participants and were specific to their deficit.
The primary objective of this study was to evaluate the efficacy of our novel visual feedback in altering gait post-stroke. Thus, to contrast performance of participants with chronic stroke who received a control goal walking pattern (i.e. stroke-to-control), we evaluated the performance of healthy, age-matched controls who receive a hemiparetic goal walking pattern (i.e. control-to-stroke) using the same visual feedback. This contrast allows us to further validate our method by investigating the extent to which performance in stroke-to-control was limited by neurological injury compared to limitations imposed by the method itself. We hypothesized that participants in both groups would be able to use this summary visual feedback to simultaneously alter multiple aspects of their gait (albeit to varying extends depending on their impairment) toward the prescribed goal pattern while walking on a treadmill.
Ten adults with chronic stroke (3 female; age: 59.0 ± 7.4 yr) and ten group age-matched neurologically intact adults (7 female; age: 57.3 ± 6.8 yr) were recruited for this experiment. All participants with chronic stroke met inclusion and exclusion criteria (Table 1). All participants provided written, informed consent before taking part in the experiment. The experimental protocol was approved by the Johns Hopkins Medicine Institutional Review Board.
Participants with chronic stroke underwent clinical examination prior to the experiment. To quantify motor impairment we administered the lower extremity subscale of the Fugl-Meyer test (FM-LE) . This test includes 17 items scored on an ordinal scale (0–2) with 34 possible points and higher scores representing less impairment. We measured self-selected and fastest comfortable over ground walking speeds by having participants walk two passes at each speed across a six-meter electronic walkway (Zeno Walkway, ProtoKinetics, Havertown, PA). Baseline knee and hip flexion angles, used to determine study eligibility, were measured using motion capture while participants walked on the treadmill at their self-selected speed. Participants who customarily wore an ankle-foot orthosis continued using these items throughout the study.
We also tested for sensory impairment in participants with chronic stroke. For proprioception testing, participants were supine with their eyes closed. The examiner stabilized the proximal joint segment and passively moved the distal segment to a position above or below the neutral starting position (neutral position was midway through the joint’s range of motion). The participant reported whether the position of the specified joint was above or below the starting position. Paretic hip, knee, and ankle joints were each tested at six different positions (18 total probes). Participants with stroke also completed The Star Cancellation Test, a screening tool that detects the presence of unilateral spatial neglect . Scores less than 44/54 stars cancelled is suggestive of unilateral spatial neglect.
We assessed cognitive function in both participants with chronic stroke and control participants using the Montreal Cognitive Assessment (MoCA) . Scores greater than 26/30 possible points reflect normal cognitive function.
We recorded participants’ kinematics using an Optotrak Certus motion capture system (Northern Digital, Waterloo, ON) as they walked on a split-belt treadmill (Woodway, Waukesha, WI) with a separate belt for each leg. This type of treadmill allowed us to detect right and left foot contacts via distinct force plates under each belt, but the belt speeds were equal throughout all experiments. Kinematic data were collected at 100 Hz from 12 infrared-emitting diodes placed bilaterally on the foot (fifth metatarsal head), ankle (lateral malleolus), knee (lateral epicondyle), hip (greater trochanter), pelvis (iliac crest), and shoulder (acromion process; Fig. 1a).
[ARTICLE] Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback – Full Text
Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes.
This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients’ performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients.
12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40 min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40 min per day, 5 days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training.
Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients.
Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.
Stroke is the leading cause of acquired disability throughout the world, with increasing survival rates as medical care and treatment techniques improve . Post-stroke disability often affects mobility, balance, and walking . The majority of stroke survivors rank walking recovery among their top rehabilitation goals [3,4,5]. Furthermore, the ability to walk is one of the most important determining factors for returning home after stroke .
Recovery of walking mainly occurs within the first 11 weeks after a stroke ; indeed, further recovery after that time is rare . Overall, between 30 and 40% of stroke survivors are not able to regain a functional gait after rehabilitation [4, 8]. These data have stimulated advances in many different innovative technological approaches to improve the gait rehabilitation efficacy.
Modern concepts favour task-specific repetitive rehabilitation approaches , with high intensity  and early multisensory stimulation . These requirements are met by robot assisted gait training (RAGT) approaches. Recent studies on stroke patients have reported that when conventional therapy and RAGT are combined, compared to conventional therapy alone, gait recovery significantly improves  and patients are more likely to recover independent walking . In particular, non-ambulatory patients in the sub-acute phase are the group most likely to benefit from this type of training .
This high interest in robotic therapy has attracted attention to human robot interactions in the rehabilitation framework, and a consensus is forming on the importance of top-down approaches in rehabilitation, particularly when dealing with robotic devices . The critical aspects of top-down approaches are multifarious and include motivation, active participation , learning skills  and error-driven-learning , evidencing the key aspects of biofeedback information to guide and improve patient robot interactions.
Thus, biofeedback is, at present, the main approach to guide top-down control mechanisms, which represents a powerful tool to drive recovery. To this aim, the patient has to be aware of the differences between on-line performance and the desired performance . In this scenario, many different error signals can be used, and at present, no indication exists for their specific effects on performances [18, 19]. Many biological parameters have been used to feed biofeedback information to patients in different stroke gait rehabilitation scenarios .
In general, in spite of the information content, biofeedback has been associated with improved outcomes in several gait pathologies [21,22,23,24]. Among diverse types of biofeedback, the most generally employed in gait rehabilitation paradigms have been electromyographic (EMG), kinematic as well as robot generated indexes , although no comparisons have been made among these approaches.
At present, many robotic devices for gait rehabilitation in stroke are commercially available . Two main classes can be identified, those based on body weight support systems (BWSS) and over ground exoskeletons. Overall, BWSS are the most widely used in rehabilitation centres, with Lokomat, Gait Trainer and GEO systems being the most popular. The present study focuses on the biofeedback content effects during Lokomat gait training in stroke survivors. Commercially available Lokomat biofeedback tools are based either on navigational or robot-generated information. The latter approach focuses on the forces that assist patients to follow the predefined gait pattern due to force transducers built into the robot drives .
Generally effectiveness of Lokomat training is assessed with gait functional outcome measures. Specific data about spasticity effects of Lokomat training are rare, and mainly focused on spinal cord injury (SCI) patients and on ankle muscles. In this framework few studies addressed positive effects of Lokomat training on reducing spasticity and improving volitional control of the spastic ankle in persons with incomplete SCI , and on reducing the abnormal modulation of neuromuscular properties that arises as secondary effects after SCI [28, 29]. To our knowledge, as concern stroke population, a single study compared conventional rehabilitation versus Lokomat add-on training selecting spasticity as a secondary outcome, demonstrating no significant robotic gait training effects .
Furthermore, no studies have either analysed the use of an electromyographic -based biofeedback (EMGb) of hip, knee and ankle muscles during training with the Lokomat robot, or compared the impact of different biofeedback types on Lokomat robotic gait training. To this end, we designated a randomized controlled trial, because this type of study is the most rigorous and robust research method of determining whether a cause-effect relation exists between an intervention and an outcome . In this pilot study we compared two different types of biofeedback: a robot generated joint torque biofeedback (Rb) versus a novel on-line EMGb. Thus, a randomized cross-over clinical trial using the Lokomat RAGT device, was conducted focusing on patients’ performances, personal experience and robot forces data in sub-acute non ambulatory patients. In particular the main outcome measure was considered the lower limb spasticity. Considering that in stroke population, spasticity may affect quality-of-life and can be highly detrimental to daily function , we also analysed patients’ personal experience related to training gait with the Lokomat system.[…]
Continue —> Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback | Journal of NeuroEngineering and Rehabilitation | Full Text
[Abstract + References] Electromyography as a Suitable Input for Virtual Reality-Based Biofeedback in Stroke Rehabilitation – Conference paper
Virtual reality (VR)-based biofeedback of brain signals using electroencephalography (EEG) has been utilized to encourage the recovery of brain-to-muscle pathways following a stroke. Such models incorporate principles of action observation with neurofeedback of motor-related brain activity to increase sensorimotor activity on the lesioned hemisphere. However, for individuals with existing muscle activity in the hemiparetic arm, we hypothesize that providing biofeedback of muscle signals, to strengthen already established brain-to-muscle pathways, may be more effective. In this project, we aimed to understand whether and when feedback of muscle activity (measured using surface electromyography (EMG)) might more effective compared to EEG biofeedback. To do so, we used a virtual reality (VR) training paradigm we developed for stroke rehabilitation (REINVENT), which provides EEG biofeedback of ipsilesional sensorimotor brain activity and simultaneously records EMG signals. We acquired 640 trials over eight 1.5-h sessions in four stroke participants with varying levels of motor impairment. For each trial, participants attempted to move their affected arm. Successful trials, defined as when their EEG sensorimotor desynchronization (8–24 Hz) during a time-limited movement attempt exceeded their baseline activity, drove a virtual arm towards a target. Here, EMG signals were analyzed offline to see (1) whether EMG amplitude could be significantly differentiated between active trials compared to baseline, and (2) whether using EMG would have led to more successful VR biofeedback control than EEG. Our current results show a significant increase in EMG amplitude across all four participants for active versus baseline trials, suggesting that EMG biofeedback is feasible for stroke participants across a range of impairments. However, we observed significantly better performance with EMG than EEG for only the three individuals with higher motor abilities, suggesting that EMG biofeedback may be best suited for those with better motor abilities.
The article describes an innovative mechatronic device for the hand rehabilitation, which enables diagnostics, comprehensive exercises and reporting of the results of rehabilitation of individual fingers of people who have lost their full efficiency as a result of past illnesses (i.a. stroke) and orthopedic injuries. The basic purpose of the device is to provide controlled, active exercises of the individual fingers, to widen the range of their movements, and to increase their precision of movement. The developed mechatronic device works with original software for PCs containing a diagnostic module, reporting module and a set of virtual reality exercises using biofeedback. The device uses auditory and visual biofeedback, and electromyography (EMG).
A neuromuscular interface (NI) that can be employed to operate external robotic devices (RD), including commercial ones, was proposed. Multichannel electromyographic (EMG) signal is used in the control loop. Control signal can also be supplemented with electroencephalography (EEG), limb kinematics, or other modalities. The multiple electrode approach takes advantage of the massive resources of the human brain for solving nontrivial tasks, such as movement coordination. Multilayer artificial neural network was used for feature classification and further to provide command and/or proportional control of three robotic devices. The possibility of using biofeedback can compensate for control errors and implement a fundamentally important feature that has previously limited the development of intelligent exoskeletons, prostheses, and other medical devices. The control system can be integrated with wearable electronics. Examples of technical devices under control of the neuromuscular interface (NI) are presented.
Development of neurointerface technology is a topical scientific focus, with the demand for such systems driven by the need for humans to communicate with numerous electronic computing and robotic devices (RD), for example, in medical applications such as prosthetic limbs and exoskeletons. At present, multichannel recording of neuromuscular activity and the development of neurointerface applications that implement unique mechanisms for high-dimensional data processing are areas of major interest.
One of the most suitable signals aiming at controlling external RDs is electromyographic (EMG) activity. Multichannel signals from the human peripheral nervous system have been previously successfully used to control external devices and novel methods of EMG acquisition and control strategies have recently been implemented [1–8]. When controlling anthropomorphic RD, the human pilot independently coordinates and plans the trajectory of motion using the massive computing power of the human brain [9, 10]. The use of afferent neural pathways allows the activation of biological feedback; using this principle is fundamentally important to the development of rehabilitation exoskeletons, prostheses, and various other medical applications.
The disadvantages of using EMG interfaces in rehabilitation are the presence of muscle fatigue and insufficient residual muscle activity. On the other hand electroencephalographic (EEG) interfaces proved to be the best due to a direct link to the nervous system by measurement of brain activity during therapy [11, 12]. The brain mechanisms that enable humans to facilitate the control of external devices remain largely unknown. However, despite this knowledge gap, appropriate collection, detection, and classification can enable brain-controlled signals from the human body to be utilized for highly efficient and even intelligent control of multiparameter RDs. But brain-machine interfaces (BMI) have some limitations such as low reliability and accuracy when it comes to complex functional task training.
A possible solution to these problems is the combined use of the advantages of both types of interfaces. Such interfaces are called hybrid, for example, hybrid BMI (hBMI); the use of EMG input here can lead to a more accurate classification of EEG patterns [13–15]. However, the task of developing an EMG interface is still relevant.
Considering the problem of motion recognition and decoding of EMG signals, note that there are several generally applicable methods of software signal processing: linear discriminant analysis (LDA) , support vector machines (SVM) , artificial neural networks (ANN) , fuzzy algorithms [22, 23], etc.
Despite significant progress in the field of machine learning and its application in medical tasks , algorithms are still based on applying ANN technologies and solving optimization problems. Creation of a universal algorithm that can adapt to different conditions in a technical control system was proven theoretically impossible, at least in the context of existing theories [25, 26]. Compared to traditionally controlled electronic devices, neurocontrolled devices may offer the advantage of adapting due to human brain plasticity.
The present study focuses on the development of methods and technologies for remote control of RDs in specific applications. The objective was to integrate human bioelectrical signals into a control loop. Online collection and interpretation of multisite EMG signals were performed to control a variety of robotic systems. Technical solutions were developed to associate patterns of muscular activity (and human brain, if possible) with the commands to the controlled object by employing a user-defined translation algorithm. EMG interface solution is driven by multilayer ANN feature classifier. User-defined programmable function translates sensory signals into motor commands to successfully control a variety of existing commercial RDs.[…]
Children are the group most frequently diagnosed with new cases of epilepsy. In the United States, 300,000 children under 14 are affected by the condition. Some may outgrow the disorder, but most will not. The number of senior citizens with epilepsy is also 300,000.
People with epilepsy have a range of treatment options, including alternative therapies.
The illness is a complex condition, however, and all alternative treatment options must be looked at carefully, to ensure they are effective.
It is essential to work with a doctor when making changes in treatment, as every epileptic seizure can cause brain damage, and the effects build up. So, any treatment must work to avoid seizures.
Contents of this article:
Causes of epilepsy
Infections, which can cause scarring on the brain that leads to seizures, are among the more common causes of epilepsy.
In the over 65s, strokes are the most common cause of new seizures. Family history and brain injuries account for other cases.
However, the Epilepsy Foundation say the cause is unknown in 60 percent of people.
Eight natural remedies for epilepsy
People with epilepsy and their doctors are expressing growing interest in alternative therapies.
Although antiepileptic drugs (AEDs) help most people control their symptoms, these do not work for everyone. Furthermore, some people are concerned about the long-term safety of these drugs.
Complementary health practices for epilepsy, such as the eight natural remedies discussed here, are designed for use in combination with AEDs.
After talking to a doctor, and before beginning natural treatments, people with epilepsy should ensure they are working with a well-qualified and informed therapist.
Common complementary treatments for epilepsy include the following:
Cannabis sativa, or marijuana, as it is commonly known, has been used to treat convulsions for centuries. Today, it is attracting increasing attention from people with epilepsy, clinicians, and researchers.
Interest in the use of medical marijuana is particularly strong for the roughly 1 million U.S. residents whose seizures are not controlled by AEDs. Some families with young children, suffering from severe seizures, have moved to one of the 22 states where medical marijuana use is legal.
Charlotte’s Web is a strain of cannabis bred to contain high levels of CBD, a part of the plant showing promise against seizures. It is named after a child whose convulsions dropped from more than 300 a week to 2-3 a month with this treatment.
However, since broad-based, well-designed scientific studies have yet to prove the effectiveness of marijuana in treating epilepsy, doctors do not generally recommend its use.
Diet is one of the earliest forms of treatment for epilepsy and is used with contemporary variations to make it easier for children and adults to adopt.
The ketogenic diet is a high-fat, low-carbohydrate diet that has had some success in reducing seizures in children who cannot tolerate or benefit from AEDs. It requires extensive commitment and monitoring.
The Atkins diet is a high-protein, low-carbohydrate diet that is less restrictive and has shown positive effects.
Low glycemic index treatment (LGIT) is similar but allows for a targeted level of carbohydrate consumption.
Herbs are used for many illnesses by 80 percent of the world’s population. Remedies drawing on Chinese traditions have shown promise in treating epilepsy.
Some herbs, such as chamomile, passionflower, and valerian, may make AEDs more effective and calming.
However, ginkgo, ginseng, and stimulating herbs containing caffeine and ephedrine can make seizures worse.
St. John’s wort can interfere with medications and make seizures more likely, similarly to evening primrose and borage.
Caution is advised when working with all these herbs.
It is important to remember that herbs are not monitored by the U.S. Food and Drug Administration (FDA). If any herbs are used, they should be researched and bought from reputable sources.
Low levels of the B6 vitamin have been known to trigger seizures.
Magnesium, vitamin E, and other vitamins and nutritional supplements, have been identified as either promising or problematic for treating epilepsy.
Along with vitamin B6, magnesium, and vitamin E, which have been found to be helpful in treating epilepsy, doctors have found treatment with manganese and taurine reduced seizures, as well.
Thiamine may help improve the ability to think in people with epilepsy.
When AEDs do not work, some people have successfully used biofeedback to reduce seizures.
With the use of extensive training and a machine that detects electrical activity in the brain, the technique teaches individuals to recognize the warning signs of seizures, and train their brains to prevent a full-blown attack.
There are many different practices that people with epilepsy can follow on their own to help them feel calmer, relax their muscles, get better sleep, and enjoy a better state of mind.
All these actions taken together can help reduce seizures and make it easier for people to manage their epilepsy.
People should be cautious if trying meditation, as this can change the electrical signals in the brain.
Some essential oils used in aromatherapy, such as lavender, chamomile, jasmine, and ylang-ylang, have been found to be effective in preventing seizures when used with relaxation techniques.
However, the Epilepsy Society report that others may provoke seizures. These include spike lavender, eucalyptus, camphor, sage, rosemary, hyssop, and fennel.
Acupuncture and chiropractic
While acupuncture does not seem to be helpful in preventing seizures, people with epilepsy find it can reduce the stress of living with the condition.
There is little evidence on chiropractic care, but it also may be among the natural treatments people with epilepsy find useful.
Education and avoiding triggers
Education and avoidance can have a big impact on quality of life for people with this condition.
Many of those with epilepsy find that their seizures develop in response to specific triggers. This is the case for people with photosensitive epilepsy.
Learning how to avoid situations and stimuli that could spark a seizure can be very helpful. Some children may learn to avoid using video games in dark rooms, for example, or to cover one eye when exposed to flashing lights.
Do natural treatments for epilepsy work?
For many practices, there has not been enough study to give a definite answer to this question, one way or the other.
The following overview of the top natural treatments for epilepsy offers a quick summary of their reported effectiveness:
- Diet: The ketogenic diet, usually prescribed for children whose epilepsy does not respond to AEDs, has been shown to cut their seizures by half and eliminate seizures completely for 10-15 percent of those studied.
- Herbal treatments: Two studies of Chinese herbal compounds found them effective at reducing seizures in children and adults. But some herbs, such as St. John’s wort, can make seizures worse.
- Vitamins: Many studies have linked low levels of vitamin B6, magnesium, and vitamin E to seizures. Treating people with supplemental doses helped reduce the frequency of seizures.
- Biofeedback: Researchers in 10 different studies showed that 74 percent of people whose epilepsy could not be treated with medication, reported fewer seizures after they learned this technique.
- Relaxation: Fewer seizures and a better quality of life were reported by children who took part in trials, according to research.
- Acupuncture and chiropractic: Scientific studies have not found acupuncture to be effective for people with epilepsy. However, positive outcomes were reported for some children with drug-resistant epilepsy who tried chiropractic therapy.
- Education: After learning more about epilepsy, coping strategies for it, and how to take medication, improved quality of life was observed for people of all ages with epilepsy.
Many reports on the effectiveness of complementary treatments for epilepsy come from personal experience, and from studies that are not considered conclusive.
Most importantly, people should always talk to their doctor before trying natural treatments to help ease their symptoms.