Posts Tagged Brain computer interfaces

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


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


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[Abstract] A motor rehabilitation BCI with multi-modal feedback in chronic stroke patients (P5.300)


Objective: Apply BCI technology to improve stroke rehabilitation therapy

Background: Brain-computer interfaces (BCI) measure brain activity to generate control signals for external devices in real-time. BCIs are especially well suited for motor rehabilitation. Motor imagery BCIs can analyze patients’ sensorimotor regions and control conditionally gated feedback devices that allow the patient to regain motor functions.

Design/Methods: Patients with sub-acute stroke were trained for 25 30-minute sessions in which they imagined left or right hand movement. A computer avatar indicated which hand the patient should imagine moving (80 trials left hand; 80 trials right). The BCI system analyzed EEG in real time, deciphered intention for left or right hand movement, and triggered functional electrical stimulation that elicited movement in the corresponding hand and in the computer avatar only when the patient produced the correct corresponding EEG pattern. Motor function improvements were assessed with a 9-hole PEG test.

Results: In a chronic stroke patient the 9-hole PEG test showed an improvement in affected left hand movement from 1 min 30 seconds to 52 sec after 24 training sessions (healthy right hand: 26 sec). BCI accuracy increased from 70% to 98.5 % across sessions. Mean accuracy for the first 3 sessions was 81%; 88% for the last 3. Before training, the patient could not lift his affected arm. After training the patient could reach his mouth to feed himself.

Conclusions: BCI accuracy is an objective marker of a patient’s participation in the task; 50% means that patient doesn’t follow (or cannot follow) the task. This patient’s continued improvement and high final accuracy indicates motivated participation. Most importantly, there was objective improvement in motor function within only 25 training sessions. We attribute these results to the conditionally gated reward from the BCI (inducing Hebbian plasticity), and mirror neuron system activation by the avatar.

Disclosure: Dr. Guger has received personal compensation for activities with g.tec Medical Engineering GmbH as an employee. Dr. Coon has nothing to disclose. Dr. Swift has nothing to disclose.

Source: A motor rehabilitation BCI with multi-modal feedback in chronic stroke patients (P5.300)

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[ARTICLE] Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power – Full Text



Transcranial direct current stimulation (tDCS) is a technique for brain modulation that has potential to be used in motor neurorehabilitation. Considering that the cerebellum and motor cortex exert influence on the motor network, their stimulation could enhance motor functions, such as motor imagery, and be utilized for brain-computer interfaces (BCIs) during motor neurorehabilitation.


A new tDCS montage that influences cerebellum and either right-hand or feet motor area is proposed and validated with a simulation of electric field. The effect of current density (0, 0.02, 0.04 or 0.06 mA/cm2) on electroencephalographic (EEG) classification into rest or right-hand/feet motor imagery was evaluated on 5 healthy volunteers for different stimulation modalities: 1) 10-minutes anodal tDCS before EEG acquisition over right-hand or 2) feet motor cortical area, and 3) 4-seconds anodal tDCS during EEG acquisition either on right-hand or feet cortical areas before each time right-hand or feet motor imagery is performed. For each subject and tDCS modality, analysis of variance and Tukey-Kramer multiple comparisons tests (p <0.001) are used to detect significant differences between classification accuracies that are obtained with different current densities. For tDCS modalities that improved accuracy, t-tests (p <0.05) are used to compare μ and βband power when a specific current density is provided against the case of supplying no stimulation.


The proposed montage improved the classification of right-hand motor imagery for 4 out of 5 subjects when the highest current was applied for 10 minutes over the right-hand motor area. Although EEG band power changes could not be related directly to classification improvement, tDCS appears to affect variably different motor areas on μ and/or β band.


The proposed montage seems capable of enhancing right-hand motor imagery detection when the right-hand motor area is stimulated. Future research should be focused on applying higher currents over the feet motor cortex, which is deeper in the brain compared to the hand motor cortex, since it may allow observation of effects due to tDCS. Also, strategies for improving analysis of EEG respect to accuracy changes should be implemented.


Transcranial direct current stimulation (tDCS) is a noninvasive technique for brain stimulation where direct current is supplied through two or more electrodes in order to modulate temporally brain excitability [12]. This technique has shown potential to improve motor performance and motor learning [345]. Hence, it could be applied in motor neurorehabilitacion [1]. However, tDCS effects vary depending on several factors, such as the size or position of the stimulation electrodes and the current intensity that is applied [6] or the mental state of the user [7]. Therefore, it should be considered that outcomes of tDCS studies are the result of different affected brain networks that may be involved in attention and movements, among other processes.

Volitional locomotion requires automatic control of movement while the cerebral cortex provides commands that are transmitted by neural projections toward the brainstem and the spinal cord. This control involves predictive motor operations that link activity from the cerebral cortex, cerebellum, basal ganglia and brainstem in order to modify actions at the spinal cord level [8]. In general, this set of structures can be considered to form a motor network that allow voluntary movement.

Different parts of the cerebral cortex participate in the performance of self-initiated movement, like the supplementary motor (SMA), the primary motor (M1) and premotor (PM) areas. It is known that M1 is activated during motor execution. Excitatory effects of M1 have been studied with anodal stimulation [6], finding that activation of this region is related to higher motor evoked potentials (MEPs) and an increment of force movement on its associated body part area [910]. Moreover, M1 seems to be critical in the early phase of consolidation of motor skills during procedural motor learning [11], i.e., the implicit skill acquisition through the repeated practice of a task [12].

In addition, the SMA and PM influence M1 in order to program opportune precise motor commands when movement pattern is modified intentionally, based on information from temporoparietal cortices regarding to the body’s state [8]. The SMA contributes in the generation of anticipatory postural adjustments [13]. Consequently, its facilitatory stimulation seems to increase anticipatory postural adjustments amplitudes, to reduce the time required to perform movements during the learning task of sequential movements, and to produce early initiation of motor responses [141516]. These studies suggest the possibility of using SMA excitation during treatments for motor disorders, since hemiparesis after stroke involves the impairment of anticipatory motor control at the affected limb [17]. In addition, some studies propose the participation of the SMA in motor memory and both implicit and explicit motor learning [18192021], i.e, when new information is acquired without intending to do so and when acquisition of skill is conscious [22], respectively. Complimentary to the role of SMA, the PM is crucial for sensory-guided movement initiation and the consolidation of motor sequence learning during sleep [823], while its facilitation with anodal tDCS seems to enhance the excitability from the ipsilateral M1 [24], which may be useful for treatment of PM disorders.

As previously mentioned, the cerebellum is also involved in locomotion through the regulation of motor processes by influencing the cerebral cortex, among other neural structures. During adaptive control of movement, as in the gait process, it seems that loops that interconnect reciprocally motor cortical areas to the basal ganglia and cerebellum allow predictive control of locomotion and they exhibit correlation with movement parameters [825]. Regarding to studies about cerebellar stimulation, there is still not enough knowledge about the effects of tDCS on different neuronal populations and the afferent pathways, so results are variable among studies and their interpretation is more complex than for cerebral tDCS [26]. Furthermore, the topographical motor organization of the cerebellum is not clear yet [27]. Nevertheless, most studies base their experimental procedure on the existence of decussating cerebello-cerebral connections, even if there are also ipsilateral cerebello-cerebral tracts or inter-hemispheric cerebellar connections [28]. Hence, a cerebellar hemisphere is stimulated to affect cerebellar brain inhibition (CBI), which refers to the inherent suppression of cerebellum over the contralateral M1 [29]. For example, the supply of anodal and cathodal stimulation over the right cerebellum in [30] resulted in incremental and decremental CBI on the left M1, respectively. In contrast, there are some studies that suggest this expectation may be not always appropriate. In [31] it was shown that inhibitory transcranial magnetic stimulation (a stimulation technique that provides magnetic field pulses on the brain [32]) over the lateral right cerebellum led to procedural learning decrement for tasks performed with either the right or left hand, whereas inhibition of lateral left cerebellar hemisphere decreased learning only with the left hand. In addition, results from [33] showed that cathodal cerebellar tDCS worsened locomotor adaptation ipsilaterally. These two studies may provide a reference for using cerebellar inhibition for avoiding undesired brain activity changes during motor rehabilitation, such as compensatory movement habits that might contribute to maladaptative plasticity and hamper the goal of achieving a normal movement pattern [34]. […]

Continue —> Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1 tDCS montage. Scheme of tDCS electrodes position in reference to EEG electrodes and inion (left), and placement of tDCS electrodes on the EEG cap (right). Electrodes 1,2 and 3 are highlighted in red, green and blue, respectively

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[Abstract] Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient


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.

I. Introduction

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 [1].

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

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[WEB SITE] NeuroRehabLab | Exploring the human brain through Virtual Environment interaction


The NeuroRehabLab is an interdisciplinary research group of the University of Madeira that investigates at the intersection of technology, neuroscience and clinical practice to find novel solutions to increase the quality of life of those with special needs. We capitalize on Virtual Reality, Serious Games, and Brain-Computer Interfaces to exploit specific brain mechanisms that relate to functional recovery to approach motor and cognitive rehabilitation by means of non-invasive and low-cost technologies.

more —> NeuroRehabLab | Exploring the human brain through Virtual Environment interaction

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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



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.


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.


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.


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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[ARTICLE] The Cybathlon promotes the development of assistive technology for people with physical disabilities – Full Text



The Cybathlon is a new kind of championship, where people with physical disabilities compete against each other at tasks of daily life, with the aid of advanced assistive devices including robotic technologies. The first championship will take place at the Swiss Arena Kloten, Zurich, on 8 October 2016.

The idea

Six disciplines are part of the competition comprising races with powered leg prostheses, powered arm prostheses, functional electrical stimulation driven bikes, powered wheelchairs, powered exoskeletons and brain-computer interfaces. This commentary describes the six disciplines and explains the current technological deficiencies that have to be addressed by the competing teams. These deficiencies at present often lead to disappointment or even rejection of some of the related technologies in daily applications.


The Cybathlon aims to promote the development of useful technologies that facilitate the lives of people with disabilities. In the long run, the developed devices should become affordable and functional for all relevant activities in daily life.


Competition, Championship ,Prostheses, Exoskeletons ,Functional electrical stimulation, Wheelchairs, Brain computer interfaces


Millions of people worldwide rely on orthotic, prosthetic, wheelchairs and other assistive devices to improve their qualities of life. In the US there live more than 1.6 million people with limb amputations [1] and the World Health Organization estimates the number of wheelchair users to about 65 million people worldwide [2]. Unfortunately, current assistive technology does not address their needs in an ideal fashion. For instance, wheelchairs cannot climb stairs, arm prostheses do not enable versatile hand functions, and power supplies of many orthotic and prosthetic devices are limited. There is a need to further push the development of assistive devices by pooling the efforts of engineers and clinicians to develop improved technologies, together with the feedback and experiences of the users of the technologies.

The Cybathlon is a new kind of championship with the aim of promoting the development of useful technologies. In contrast with the Paralympics, where parathletes aim to achieve maximum performance, at the Cybathlon, people with physical disabilities compete against each other at tasks of daily life, with the aid of advanced assistive devices including robotic technologies. Most current assistive devices lack satisfactory function; people with disabilities are often disappointed, and thus do not use and accept the technology. Rejection can be due to a lack of communication between developers, people with disabilities, therapists and clinicians, which leads to a disregard of user needs and requirements. Other reasons could be that the health status, level of lesion or financial situation of the potential user are so severe that she or he is unable to use the available technologies. Furthermore, barriers in public environments make the use of assistive technologies often very cumbersome or even impossible.

Six disciplines are part of the competition, addressing people with either limb paralysis or limb amputations. The six disciplines comprise races with powered leg prostheses, powered arm prostheses, functional electrical stimulation (FES) driven bikes, powered wheelchairs and powered exoskeletons (Fig. 1). The sixth discipline is a racing game with virtual avatars that are controlled by brain-computer interfaces (BCI). The functional and assistive devices used can be prototypes developed by research labs or companies, or commercially available products. The competitors are called pilots, as they have to control a device that enhances their mobility. The teams each consist of a pilot together with scientists and technology providers, making the Cybathlon also a competition between companies and research laboratories. As a result there are two awards for each winning team in each discipline: a medal for the person who is controlling the device and a cup for the provider of the device (i.e. the company or the lab).

Fig. 1 Arena with four parallel race tracks designed for the exoskeleton competition. The pilots start at the left and have to overcome six obstacles with increasing difficulty level

Continue —> The Cybathlon promotes the development of assistive technology for people with physical disabilities | Journal of NeuroEngineering and Rehabilitation | Full Text

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[ARTICLE] Robotics, stem cells, and brain-computer interfaces in rehabilitation and recovery from stroke: updates and advances.


OBJECTIVE: The aim of this study was to describe the current state and latest advances in robotics, stem cells, and brain-computer interfaces in rehabilitation and recovery for stroke.

DESIGN: The authors of this summary recently reviewed this work as part of a national presentation. The article represents the information included in each area.

RESULTS: Each area has seen great advances and challenges as products move to market and experiments are ongoing.

CONCLUSIONS: Robotics, stem cells, and brain-computer interfaces all have tremendous potential to reduce disability and lead to better outcomes for patients with stroke. Continued research and investment will be needed as the field moves forward. With this investment, the potential for recovery of function is likely substantial.

via Robotics, stem cells, and brain-computer interfaces in rehabilitati… – PubMed – NCBI.

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[ARTICLE] Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke


The idea of using brain computer interfaces (BCI) for rehabilitation emerged relatively recently. Basically, BCI for neurorehabilitation involves the recording and decoding of local brain signals generated by the patient, as he/her tries to perform a particular task (even if imperfect), or during a mental imagery task. The main objective is to promote the recruitment of selected brain areas involved and to facilitate neural plasticity.

The recorded signal can be used in several ways:

  1.  to objectify and strengthen motor imagery-based training, by providing the patient feedback on the imagined motor task, for example, in a virtual environment;
  2.  to generate a desired motor task via functional electrical stimulation or rehabilitative robotic orthoses attached to the patient’s limb – encouraging and optimizing task execution as well as “closing” the disrupted sensorimotor loop by giving the patient the appropriate sensory feedback;
  3.  to understand cerebral reorganizations after lesion, in order to influence or even quantify plasticity-induced changes in brain networks. For example, applying cerebral stimulation to re-equilibrate inter-hemispheric imbalance as shown by functional recording of brain activity during movement may help recovery.

Its potential usefulness for a patient population has been demonstrated on various levels and its diverseness in interface applications makes it adaptable to a large population. The position and status of these very new rehabilitation systems should now be considered with respect to our current and more or less validated traditional methods, as well as in the light of the wide range of possible brain damage. The heterogeneity in post-damage expression inevitably complicates the decoding of brain signals and thus their use in pathological conditions, asking for controlled clinical trials.


via Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke.

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