Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients—A Feasibility Study

Abstract

Introduction: Numerous recent publications have explored Brain Computer Interfaces (BCI) systems as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Recent work has shown that BCI therapy can lead to better outcomes than conventional therapy. BCI combined with other techniques such as Functional Electrical Stimulation (FES) and Virtual Reality (VR) allows to the user restore the neurological function by inducing the neural plasticity through improved real-time detection of motor imagery (MI) as patients perform therapy tasks. Methods: Fifty-one stroke patients with upper extremity hemiparesis were recruited for this study. All participants performed 25 sessions with the MI BCI and assessment visits to track the functional changes before and after the therapy. Results: The results of this study demonstrated a significant increase in the motor function of the paretic arm assessed by Fugl-Meyer Assessment (FMA-UE), FMA-UE = 4.68 points, P < 0.001, reduction of the spasticity in the wrist and fingers assessed by Modified Ashworth Scale (MAS), MAS-wrist =-0.72 points (SD = 0.83), P < 0.001, MAS-fingers =-0.63 points (SD = 0.82), P < 0.001. Other significant improvements in the grasp ability were detected in the healthy hand. All these functional improvements achieved during the BCI therapy persisted 6 months after the therapy ended. Results also showed that patients with Motor Imagery accuracy (MI) above 80% increase 3.16 points more in the FMA than patients below this threshold (95% CI; [1.47-6.62], P = 0.003). The functional improvement was not related with the stroke severity or with the stroke stage. Conclusion: The BCI treatment used here was effective in promoting long lasting functional improvements in the upper extremity in stroke survivors with severe, moderate and mild impairment. This functional improvement can be explained by improved neuroplasticity in the central nervous system.

Summary of over Fifty Years with Brain-Computer Interfaces—A Review

“…Their main purpose was to enable direct, non-muscular communication for handicapped people and later came solutions destined for gaming (pure entertainment), were followed by issuing various inexpensive, consumer-grade headsets [3,10,160,161,230,303,304].…”

“…Potentially, the BCI users might be individuals who are severely disabled by disorders such as inter alia [3,55,[304][305][306] BCI validation and dissemination, 3.…”

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

“…It has been shown previously that neural plasticity, when quantified with transcranial magnetic stimulation (TMS), can be induced using BCI-triggered electrical stimulation and passive movements from rehabilitation robots/exoskeletons for the cortical projections of the lower limb muscles [ 7 , 8 , 9 , 10 , 11 ], but this has not been shown for the cortical projections of the upper limb muscles, although functional improvements in stroke patients have been reported for the upper limbs (see, e.g., Refs. [ 4 , 5 , 27 , 28 ]). Therefore, the aim of this study is to investigate if a BCI-triggered exoskeleton can induce neural plasticity in the cortical projections of the forearm muscles that control wrist extension.…”

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

“…Through this direct communication between the subject’s brain and an external device, BCI applications aim to improve the quality of life of people with motor or cognitive disabilities [ 1 , 2 ]. Nevertheless, assisting the disabled is not the only objective of BCI, but also the rehabilitation or recovery of their motor and cognitive functions [ 3 , 4 ].…”

“…These events are called sensorimotor rhythms (SMR) and can be used as control signals by BCI applications. Hence, some studies have proposed the use of a MI-based NFT paradigm (MI–NFT), instead of classical NFT, as a promising approach to achieve the desired modulation [ 3 , 4 , 30 ]. The MI–NFT paradigm is broadly extended among studies focusing on neurorehabilitation of post-stroke patients [ 3 , 4 , 31 , 32 , 33 , 34 , 35 ].…”