Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.

EEG-based systems have been the most widely used in the field of Brain-Computer Interfaces (BCI) for two decades. Plenty of applications have been proposed from games to rehabilitation systems. Until recently, EEG recording devices were too expensive for an end-user. Today, several low-cost alternatives have appeared on the market. The most sophisticated of these low-cost devices is the Emotiv Epoc headset. Some studies reported that this device is suitable for customers in terms of performance. However, none of the previous studies reported to what extent the Emotiv headset is working well compared to a medical system. The aim of this paper is thus to scientifically compare a medical system and the Emotiv Epoc headset by determining their respective performances in the context of a P300 BCI paradigm. In this study, seven healthy subjects performed P300 experiments and two different conditions were studied: sitting on a chair and walking on a treadmill at constant speed. Results show that the Emotiv headset, although able to record EEG data and not only artifacts, is sometimes significantly worse than a medical system. Those results suggest that the design of a specific low-cost EEG recording systems for rehabilitation purposes at a low price is still required.

This paper presents MINDWALKER, which is an ambitious EC funded research project coordinated by Space Applications Services aiming at the development of novel Brain Neural Computer Interfaces (BNCI) and robotics technologies, with the goal of obtaining a crutch-less assistive lower limbs exoskeleton, with non-invasive brain control approach as main strategy. Complementary BNCI control approaches such as arms electromyograms (EMG) are also researched. In the last phase of the project, the developed system should undergo a clinical evaluation with Spinal Cord Injured (SCI) subjects at the Fondazione Santa Lucia, Italy. I. INTRODUCTION INDWALKWER [1] is funded by EC under an ICT research programme named e-Inclusion, that aims at improving inclusion in social life of European individuals, in particular those with reduced mobility (due to e.g. disability).The research question that initiated this project can be stated following this way: could a lower limbs assistive exoskeleton system allow SCI subjects to recover mobility, relying on convenient, non-invasive BNCI control signals acquisition -EEG based as far as possible, and without the need for stability improvement accessories such as crutches (that cannot be used by quadriplegic subjects, and that prevent paraplegic subjects from using their arms and hands Manuscript received April 30 th , 2012. MINDWALKER is supported in part by the European Commission through the FP7 Programme, with project reference ICT-2009-247959 (Health, e-Inclusion). MINDWALKER is member of the Future BNCI European network.

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