Hybrid Brain-Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of 'more normal' brain and muscular activity. It is well known that the regaining of motor function after stroke is characterized by several changes in muscular activation patterns, such as motor overflow, co-activation of agonist and antagonist muscles and spasticity. Hybrid BCIs include peripheral signals such as those derived from electromyography (EMG) as a control feature. These have mostly been developed to improve the classification performance of the system e.g. in assistive BCIs, with little or no focus on which properties of the EMG signals should be considered in a rehabilitative context.
This proposal aims at developing a hybrid BCI prototype in which the control features will be derived from a combined Electroencephalographic (EEG) and EMG connectivity pattern estimated online during upper limb attempts and used to drive a feedback to the user through Functional Electrical Stimulation (FES) when 'correct' movements are detected.
To achieve this goal, pre-recorded EEG and EMG data of 20 healthy subjects and 20 stroke patients during simple upper limb movements/attempts will be analysed to characterize physiological and pathological patterns. The best individual control features able to detect the physiological efferent drive and the dysfunctional patterns will be selected, a hybrid classification approach will be built and the ability of these hybrid features to classify the movements in real-time will be tested. A novel BCI prototype will be developed and used to control a FES device. Upon success, this prototype could pave the way towards a novel hybrid BCI system for post-stroke rehabilitation based on a global vision of the physiological/pathological patterns involved in the movement that has to be recovered.
This BCI-based protocol will allow to exploit the patient's residual or recovered motor abilities, delivering a feedback that is not only functionally meaningful (e.g., via virtual reality or passive movement of the paretic limb by a robot), but also tailored to reorganize the targeted neural circuits by providing rich sensory inputs via the pathways natural afferent. In this way, the recovery of upper limb function after stroke can be maximize thanks to this innovative hybrid BCI approach with potentially high impact on the stroke survivor's quality of life.
This project will go beyond the state of the art by extending the concept of CMC itself to a complex pattern of synchronization between brain and muscular activations thanks to a multivariate approach for connectivity estimation. The resulting measure will be a connectivity pattern describing in real-time the communication between the brain and the periphery; EMG recordings from multiple muscles will allow to distinguish between correct and incorrect movements, providing a rehabilitation instrument congruent to neurophysiological principles.
Indeed, the success in the implementation of this hybrid BCI prototype would have a wide impact on:
1) Post-stroke rehabilitation of patients with residual or recovered arm movements, that could be supported by a non-invasive hybrid BCI that controls devices that assist movement (such as FES), whose decision to send the feedback will be based on a global vision of the physiological patterns involved in the movement that has to be recovered.
2) The use of a complex pattern of cortico-muscular activation to discriminate different movements, building a classifier based on a hybrid feature that takes into account not only both the cerebral and muscular activity involved in the movement, but their interconnection. Moreover, adding EMG parameters related to the movement quality to control the output, this system will encourage only physiological activity. The performances of this approach will be evaluated to test its reliability, also compared with the existing classification method. This could be relevant, not just for motor rehabilitation, but also for many technological applications for motor substitution in which `natural control' (i.e., that resembling physiological control) of prosthetic devices is cutting-edge.
3) The consolidation of the role of BCIs in rehabilitation, increasing BCI-based opportunities for upper limb stroke rehabilitation in order to follow patients along recovery and giving him a feedback tailored on his rehabilitative stage.