Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation said:
Brain Machine Interface (BMI) Technologies
BMI systems infer the user’s intent from neural data acquired from the brain, and transform it into output variables to control screen cursors, prosthetic devices, assistive orthotic devices, etc., in real time. One of the first implementations of a brain-computer interface consisted of using an event-related potential (ERP) associated with the classical oddball paradigm to identify letters in the alphabet, which helped the user communicate through words [10]. Since then, the school of thought that primarily considered neural interfaces to be applicable only in the completely paralyzed and/or individuals who are “locked-in” and cannot communicate verbally has definitively changed, and BMIs are getting integrated into mainstream rehabilitation. The reasons for this are primarily: (a) ability to measure brain signals non-invasively that can be effectively transformed into control signals, using methods such as electroencephalography (EEG) [11, 12], magnetoencephalography (MEG) [13], and functional near-infrared spectroscopy (fNIRS) [14, 15]; (b) improvements in technology that allow relatively fast analysis of large-scale, multi-dimensional data sets; and (c) increased understanding of neuroplastic mechanisms of motor learning and adaptation [16, 17] and functional motor recovery [18], which has further catalyzed use of brain-derived neural signals in rehabilitative BMIs.
BMIs have the potential to greatly improve clinical rehabilitation regimens by using extant neurological signals to drive and enhance functional recovery by actively engaging the user in rehabilitation, while simultaneously allowing for monitoring and quantification of internal states and neural plasticity over time. Also, the process of learning to use the BMI, i.e., learning to control the device/perform the task at hand using neural “thought” signals is a process of cognitive-motor learning, which is of benefit in recruiting existing neuroplastic mechanisms. Besides, successful task performance is fed back in the loop, recruiting error-correcting and reward-related feedback mechanisms. Taken together, these recent BMI applications for training-induced plasticity have made it an important rehabilitative tool, rather than a mere substitutive tool for the severely impaired patients.
Clinician Benefits with Brain Machine Interfaces
Motivation is an important psychosocial factor that can greatly affect neurological rehabilitation outcomes [19]. Therefore, active user engagement and positive reinforcement provided through both neural signals as well as task goal accomplishment, e.g., using an upper limb orthotic device that allows a stroke patient to move a paretic arm to grasp an object, a task that can otherwise not be performed by the patient, can significantly enhance patient motivation. The impact of this on enhancing rehabilitation outcomes could be profound, and is generally underscored. Secondly, BMIs can allow physical therapists and other rehabilitation clinicians to have continuous access to neural monitoring during treatment. This allows for personalizing treatment to each individual based on his/her functional abilities at a level of granularity that is otherwise impossible. Most importantly, these neural markers can be used to guide changes in treatment parameters, i.e., increasing/decreasing task difficulty or challenges, as well as allowing for task modifications. In other words, neural data can be used for neurological rehabilitation in the same manner as VO2 max or electrocardiogram (EKG) is used for cardiac rehabilitation, i.e., as a window into the internal physiological state that informs the clinician to appropriately modify exercise levels. This can significantly help clinicians and patients alike by helping make treatment protocols personally adaptive, as well as minimizing injury due to fatigue.
Brain–Machine Interfaces in Stroke Rehabilitation
The use of BMIs in stroke neurorehabilitation has become popular in recent times, given their benefits of guiding and enhancing neuromotor learning. Neural control signals may be obtained for a BMI via implanted electrode arrays (including electrocorticography, i.e., ECoG) or through techniques that measure neural activity on the scalp directly (e.g., EEG and MEG) or indirectly (e.g., blood oxygenation levels through functional magnetic resonance imaging i.e., fMRI and fNIRS). For the purpose of this review, BMI techniques employing measurements of scalp neural activity are discussed, as this is non-invasive and more relevant to stroke rehabilitation.
In this context, mu-rhythm, i.e., 8–13 Hz oscillatory activity observed over the central sensory-motor scalp areas in EEG and MEG, has been found to be quite successful as a neural control signal for BMIs [13, 20, 21••, 22]. Event-related desynchronization (ERD) or reduction in amplitude of this oscillatory band activity in response to a stimulus/Go cue has been used to control the impaired upper limb orthotic devices with some success in stroke patients. Patients improved in achieving task successes over multiple training sessions [13], which further substantiated the notion that BMIs can recruit extant neuroplasticity in chronic stroke patients. More recently, a larger-scale controlled clinical study demonstrated that stroke patients with minimal hand function who received ERD-driven BMI training as an adjunct to physical therapy to control a hand-orthotic device showed functional improvements in Fugl–Meyer assessment scores, compared to those who received sham BMI training (non-neural control of orthosis) [21]. Furthermore, the functional improvements in these patients were also significantly correlated with hand electromyographic activity, thereby providing evidence of peripheral neuromuscular plasticity driven by BMI training. These findings provide great promise for the future of BMI use in clinical stroke rehabilitation. Further, since motor imagery is used by patients with paresis or paralysis in order to generate neural signals simulating movement in the brain, this provides an additional avenue to engage neuroplastic mechanisms in stroke patients [23].
BMIs can also be coupled with functional electrical stimulation (FES) in order to allow more intentional control of FES of relevant muscles. It is postulated that neurally driven FES can engage Hebbian mechanisms of associative learning and consequently increase synaptic plasticity. A recent study [24] has shown the feasibility of using mu-rhythm ERD to drive FES of the tibialis anterior (TA) in a stroke patient. Interestingly, the authors found increased EMG activity in the TA, along with increased dorsiflexion, following BMI-FES rather than FES alone. This is very promising, as improving TA muscle control and dorsiflexion range of motion (ROM) can significantly impact gait training in stroke patients and improve functional recovery.
Recently, BMI coupled with virtual reality (VR) environments have also gained popularity in the context of stroke rehabilitation. Virtual environments have been very useful to train functional upper limb pointing movements in stroke patients [25, 26, 27]. Therefore, adding a neural interface to VR training can help engage patients early on in the stages of functional recovery when volitional movement may be more limited. The benefits would include increased recruitment of cortical motor networks through motor imagery used to control the BMI, as well as engaging motor learning mechanisms through repetitive training. Researchers have developed and tested a prototypical VR system in healthy individuals that involves controlling a virtual ‘avatar’ using a motor-imagery based BMI [28]. The use of such BMI-based VR rehabilitation in early stages of stroke recovery could significantly alter the trajectory of functional recovery in patients, thereby enhancing quality of life and potentially reducing needs for long-term rehabilitation and associated costs.
