Motor Substitution

Grasping
In Europe alone, an estimated number of 300,000 people are suffering from a spinal cord injury (SCI) with 11,000 new injuries per year (Wyndaele and Wyndaele, 2006). Forty percent of the total population of the SCI patients are tetraplegics. Loss of motor functions, especially grasping, leads to a life-long dependency on care-givers and to a dramatic decrease in quality of life (Anderson, 2004).
Beside SCI persons, other neurological patients also suffer from paralysis of the upper extremities and the related restrictions in terms of independence and life quality. In Germany, 60% of the 150,000 patients affected by a stroke for the first time survive the first year, one-third of them with a hemiplegia (Exner, 2004). In Germany, 10% of the annual 250,000 traumatic brain injury patients live with motor deficits at the upper extremities.
Today, if surgery is not an option, functional electrical stimulation (FES) is the only possibility for partially restoring lost motor functions (Hentz and Le Clercq, 2002). In this context, the term neuroprostheses is used to describe FES systems aiming at the restoration of a weak or lost grasping function of the hand. Some of these neuroprostheses are based on surface electrodes for external stimulation of muscles of the hand and forearm. Examples are the commercially available NESS-H200 System (Bioness Inc., Valencia, USA) (Ijzermann et al., 1996) and other more sophisticated research prototypes (Thorsen et al., 2001; Mangold et al., 2005). The Freehand system (NeuroControl, Cleveland, USA), an implantable neuroprostheses, overcomes the limitations of surface stimulation electrodes concerning selectivity and reproducibility (Keith and Hoyen, 2002). All FES systems for grasp restoration have in common the fact that they can only be used by patients with preserved voluntary shoulder and elbow function, which is the case in patients with an injury of the spinal cord below C5. Only two groups have dealt with the problem of restitution of elbow and shoulder movement. Memberg et al. (2003) used an extended Freehand system, while Handa's group (Kameyama et al., 1999) developed a system based on intramuscular electrodes. Both systems represent exclusive FES systems, which stimulate the appropriate muscle groups not only for dynamic movements but also for maintaining a static posture. Due to the weight of the upper limb and the non-physiologic synchronous activation of the paralyzed muscles through external electrical pulses, rapid muscle fatiguing occurs. An alternative is, as for the case of standing and walking neuroprosthesis, to use a combination of FES with a mechanical orthosis (Goldfarb and Durfee, 1996; Kobetic et al., 2003). A passive, but lockable orthosis stabilizes the knee joint during the stance phase without the need for a continuous co-contraction of antagonistic muscle groups. For the restoration of an elbow function much less torque has to be generated and held, thus supporting the idea that a passive, lockable orthosis combined with a FES-system will be successful in restoration of upper limb function. Up to now such a system does not exist.
Current neuroprosthesis for the restoration of forearm function (hand, finger, and elbow) require the use of residual movements not directly related to the grasping process. Traditional APs like head, mouse, or control devices using tongue or eye movements have not been accepted by patients for control of neuroprosthesis, because these APs hinder their communication ability, which is most important to patients for participation in normal social activities, and the design is not esthetic. It is for this reason that recently some groups have started to explore BCI approaches in the case where no, or only minor, residual motor control is available. For a review see Müller-Putz et al. (2006).
Pioneering work by the groups in Heidelberg and Graz showed for the first time the feasibility of the combination of BCI and a FES-system with surface electrodes (Pfurtscheller et al., 2003). In this study the restoration of a lateral grasp was achieved in a spinal cord injured subject, who suffers from a complete motor paralysis with missing hand and finger function. The patient is able to trigger sequential grasp phases by the imagination of foot movements. After many years of training and use of his BCI, the patient is able to control the system even during conversation with other persons. The same groups did a short-term BCI training of another tetraplegic patient who was provided with a Freehand system in the year 2000. After 3 days of training the patient was able to control the grasp sequence of the implanted neuroprosthesis sufficiently (Müller-Putz et al., 2005). More recently, they introduced a new method for the control of the grasp and elbow function by a BCI (Müller-Putz et al., 2007). The idea is to use a low number of pulse-width coded brain patterns to control sequentially more degrees of freedom. Millán's group used the MI of hand movements to stimulate the same hand for a grasping and writing task (Tavella et al., 2010), so the subjects thought about moving the right arm and the system stimulated the right arm. Furthermore, they used an adaptable passive hand orthosis, which evenly synchronizes the grasping movements and applied forces on all fingers. This orthosis also avoids fatigue in long-term stimulation situations by locking the position of the fingers and switching the stimulation off (Leeb et al., 2010a).
It's worth noting that Fetz's group (Moritz et al., 2008) has recently described an invasive approach to brain-controlled orthosis conceptually similar to previous attempts based on non-invasive BCI mentioned above. In this experiment, a monkey, paralyzed via a nerve block, can regain control of its forearm by using FES and single cell recordings of the motor cortex. This brings us to an important underlying issue in the development of neuroprosthesis, namely the choice of the kind of mental task to use for control. In most work in non-invasive BCI, people use imagination of different limbs (right/left hand, feet) to deliver different commands to the neuroprosthesis for, say, the right hand. However, it seems more natural to rely on the recognition of different imagined movements of the same limb the neuroprosthesis controls. Initial evidence for such a possibility has been recently provided in an offline study where subjects imagined the execution of different wrist movements (Gu et al., 2009).
Finally, a BCI-controlled FES orthosis can be also relevant for motor recovery of the upper extremities in stroke patients. Despite the fact that there is no literature available on the use of such a type of device in this patient population, some studies on the topic of FES training have emerged recently. For example, Hara (2008) claims that user-driven electrical muscle stimulation – but not machine-paced electrical muscle stimulation – improves the motor function of the hemiparetic arm and hand. A new hybrid FES therapy comprising proportional EMG-controlled FES and motor point block for antagonist muscles have been applied with good results in an outpatient rehabilitation clinic for patients with stroke. Additionally, Hara et al. (2008) have shown that a daily task-oriented FES home therapy program can effectively improve wrist and finger extension and shoulder flexion. Furthermore, proprioceptive sensory feedback might play an important role in this kind of therapy. The results of the single-case study from Page et al. (2009) supports these promising results. Moreover, another recent single-case study supports the benefit of a combination of FES and BCI (Daly et al., 2009). However, this use of BCI plus FES in the field of motor recovery has to be investigated more extensively.

Assistive mobility
A second area where BCI technology can support motor substitution is in assisting user's mobility, either directly through brain-controlled wheelchairs (e.g., Millán et al., 2009) or by mentally driving a telepresence mobile robot – equipped with sensors for obstacle detection as well as with a camera and a screen – to join relatives and friends located elsewhere and participate in their activities (Tonin et al., 2010). Several commercial platforms already exist for allowing this kind of interaction: e.g., peoplebot (Mobile Robots Inc., Amherst, USA), iRobot (iRobot Corp., Bedford, USA), robotino (Festo AG, Dietikon, Switzerland).
Underlying all assistive mobility scenarios, there is the issue of shared autonomy. The crucial design question for a shared control system is: who – man, machine or both – gets control over the system, when, and to what extent? Several approaches have been developed, in particular for intelligent wheelchairs. A common aspect in all these approaches is the presence of different assistance modes. These modes can either be different levels of autonomy or different algorithms for different maneuvers. Based on these modes, existing approaches can be classified into two categories. Firstly, there are approaches where mode changes are triggered by a user's action through the operation of an extra switch or button. Examples of smart wheelchairs of this category are SENARIO (Katevas et al., 1997), OMNI (Hoyer, 1995), MAid (Prassler et al., 2001), Wheelesley (Yanco, 1998), VAHM (Bourhis and Agostini, 1998), and SmartChair (Parikh et al., 2004). However, those explicit interventions can be difficult and tiring for the users. These users have problems operating a conventional interface, and adding buttons or functionality for mode selection makes this interface only more complex to operate and less user-friendly. Secondly, there are approaches with implicit mode changes where the shared control system automatically switches from one mode to another without the need for a manual user intervention. The NavChair (Levine et al., 1999; Simpson and Levine, 1999) and the Bremen Autonomous Wheelchair (Röfer and Lankenau, 2000) are examples of this second category. The problem with all these approaches is, however, that the switching is hard-coded and independent of the individual user and his specific handicap. An extensive literature overview of intelligent wheelchair projects can also be found in Simpson (2005).
In the case of brain-controlled robots and wheelchairs, Millán's group has lead the development of a shared autonomy approach in the framework of the European MAIA project that solves the two problems mentioned above. This approach estimates the user's mental intent asynchronously and provides appropriate assistance for navigation of the wheelchair. This approach has shown to drastically improve BCI driving performance (Vanacker et al., 2007; Galán et al., 2008; Millán et al., 2009; Tonin et al., 2010). Despite that asynchronous spontaneous BCIs seem to be the most natural and suitable alternative, there are a few examples of evoked BCIs for the control of wheelchairs (Rebsamen et al., 2007; Iturrate et al., 2009). Both systems are based on P300, a potential evoked by an awaited infrequent stimulus. To evoke the P300, the system flashes the possible predefined target destinations several times in a random order. The subject's choice is the stimulus that elicits the largest P300. Then, the intelligent wheelchair reaches the selected target autonomously. Once there, it stops and the subject can select another destination – a process that takes around 10 s. A similar P300 approach has been followed to control a humanoid robot (Bell et al., 2008).