Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions

Maybe your grandchildren will be able to use this. With NO stroke strategy or stroke leadership this will take at least 50 years to get to clinical use. Unless you are rich and can pay for your own research.
http://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-015-0082-9

• Enrique Hortal†Email author,
• Daniel Planelles†,
• Francisco Resquin,
• José M. Climent,
• José M. Azorín and
• José L. Pons

†Contributed equally

Journal of NeuroEngineering and Rehabilitation201512:92

DOI: 10.1186/s12984-015-0082-9

©  Hortal et al. 2015

Received: 31 March 2015

Accepted: 8 October 2015

Published: 17 October 2015

Abstract

Background

As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes.

Methods

In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection.

Results

Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively).

Conclusions

The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.

Keywords

BMI EEG Rehabilitation Neurological condition Exoskeleton Functional electrical stimulation Motor imagery Arm movement intention detection

Background

Currently, the number of people suffering from motor disabilities or reduced mobility is increasing. Cerebro-Vascular Accidents (CVAs), i.e. strokes, are ones of the main causes of these problems. The number of people with probability of suffering a CVA is growing worldwide mainly due to the aging population [1]. This value is expected to reach in 2030 an increase of 24.9 % compared to 2010 levels [2]. According to the Spanish Society of Neurology, the number of stroke patients at Spanish hospitals has increased by 40 % over the last 15 years [3]. As reported by the World Health Organization (WHO), 15 million people suffer stroke worldwide each year, and around 5 million of them are permanently disabled [4]. All these facts evidence the necessity of improving not only prevention mechanisms but also rehabilitation procedures for people with these conditions.

Due to certain shortcomings of conventional therapy, rehabilitation systems applied after a CVA have experimented an important improvement in recent years. After conventional therapies, motor impairments as paralysis persist in a large percentage of stroke population. Recovery of motor skills is commonly very low after stroke [5] and, compared to lower limb, improvements of upper limb motor function are even lower [6]. By these facts, novel rehabilitation approach, as robot-aided rehabilitation and functional electrical stimulation (FES) were introduced, with the aim to improve effectiveness of therapy.

Several publications have showed improvements in upper limb motor function after rehabilitation therapies based on robotic devices [7, 8] and FES [9, 10]. Furthermore, the combined use of both technologies has shown promising results in terms of motor recovery after stroke [11, 12]. The main advantage of using the hybrid approach is that, individual limitations are overcome, generating in this way a more robust concept [13]. Robotic devices generally apply external mechanical forces to drive joint movements, while FES-based therapy facilitates exercise execution leaded by the participant’s own muscles. This last approach yields several benefits considering motor recovery, such as muscle strength [14] and cortical excitability [15]. Further, even when stroke participant does not contribute to voluntary movement these advantages are still present. However, the use of FES elicits the fast occurrence of muscle fatigue due to non-physiological recruitment (unnatural) of the motor units. Muscle fatigue decreases the efficacy of therapy and also entails other drawbacks, that is why, effort are always targeted to prolong the appearance of its effects. Moreover, the nonlinear and time variant behavior of the muscles during FES generate a less accurate motor control response. This problem can be addressed by using an exoskeleton, in order to cooperatively aid the movements. The inclusion of robotic device avoids stimulate arm’s muscles to overcome gravity effects, and hence, release the system from patients discomfort generated when arm muscles are constantly stimulated for this purpose. So, the main idea begins the hybrid approach based on reaching movement rehabilitation is that the exoskeleton compensate again gravity and FES assists the patient for movements execution.

Besides physical rehabilitation [16], an important question arises from the neurological level due to the neuroplasticity [17]. In this regard, multiple works focused on this kind of rehabilitation are being developed [18–20]. Brain-Machine Interfaces (BMIs) are conceived as a powerful tool for rehabilitation of CVA patients. By using these interfaces, patients are an active part of the process because the control commands are generated directly from their brain activity. Thus, not only would the rehabilitation improve from the physical point of view, but also from the neurological perspective [21]. With this system, patients are actively involved in their rehabilitation process.

To achieve a greater involvement of the patients, the use of a BMI can represent an important improvement. Several studies based on BMIs have demonstrated that people with disabilities are able to control properly systems such as a wheelchair [22], robots [23] or other devices such as a PC mouse [24] or a web browser [25]. The main objective in these works was to provide a new way to interact with the environment and facilitate daily life activities. However, these systems were not designed to restore the affected capacities of the users. Other works used brain signals to command systems that provide aid in physical and neurological rehabilitation as in [26].

Thanks to neuroscience, it is well known that many brain cognitive processes are located around the cortex. When BMIs are used in motor rehabilitation, parietal and frontal lobes are more interesting than others because they take part in intention, planning and decision of making a movement [27]. Therefore, signals acquired from these lobes can provide more information about the will to imagine or perform a movement. By using their brain signals, patients in rehabilitation could command a device to provide them some voluntary mobility. It is demonstrated that a FES therapy triggered by Electromyography (EMG) has advantages as it integrates the concept of sensorimotor feedback [9]. Using electroencephalography (EEG), follows the same approach, FES simulates normal operation of neural connections, taking the cortical level signals instead of peripheral signals (EMG) to trigger the execution of the task.

In this paper, a BMI allows, through two different methods, the control of a hybrid upper limb exoskeleton. Both methods are based in the analysis of EEG signals. EEG techniques are a non-invasive method which provides a higher patient acceptance, eliminates the health risks of operations and reduces impediments related to ethical issues. The exoskeleton is used to assist the upper limb rehabilitation process by performing extension and flexion elbow movements of the arm applying FES. The methods used in the BMI are based on motor imagery and movement intention detection through the Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS) detection. The accuracy of both methods are analyzed to demonstrate their usability and to determine which of them is better to be used in the rehabilitation therapy.

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