Functional electrical stimulation compared with ankle-foot orthosis in subacute post stroke patients with foot drop: A pilot study.

Background: The conventional treatment for foot drop includes an ankle-foot orthosis (AFO) or functional electrical stimulation (FES). Goal: To compare gait parameters in patients following a subacute post stroke with foot drop treated with AFO or FES. Method: Twenty one subacute patients with stroke with foot drop were fitted with FES (N = 10) or AFO (N = 11). Evaluations were performed at baseline, following 4 weeks and 12 weeks. Spatiotemporal gait parameters and symmetry, dynamic electromyography, 10-m walk test, 6-min walk test, timed up and go, functional ambulation classification, and perception of improvement in walking were measured. The gait analysis measures were collected without the assistive devices while the functional measures were collected with them. Results: Both groups showed improvement in all of the outcome measures, with no between-groups differences. The swing duration's and step length's symmetry indicated better gait symmetry in the FES group after 12 weeks (p = 0.037, effect size = -0.538 and p = 0.028 effect size = -0.568, respectively). The FES group perceived significant improvement in gait after 4 weeks, while subjects in the AFO group reported to perceive improvement only after 12 weeks. Conclusions: Our findings suggest that FES is at least as effective as traditional AFO and may be more so.

Design and Implementation of a Novel Subject-Specific Neurofeedback Evaluation and Treatment System.

Electroencephalography (EEG)-based neurofeedback (NF) is a safe, non-invasive, non-painful method for treating various conditions. Current NF systems enable the selection of only one NF parameter, so that two parameters cannot be feedback simultaneously. Consequently, the ability to individually-tailor the treatment to a patient is limited, and treatment efficiency may therefore be compromised. We aimed to design, implement and test an all-in-one, novel, computerized platform for closed-loop NF treatment, based on principles from learning theories. Our prototype performs numeric evaluation based on quantifying resting EEG and event-related EEG responses to various sensory stimuli. The NF treatment was designed according to principles of efficient learning, and implemented as a gradual, patient-adaptive 1D or 2D computer game, that utilizes automatic EEG feature extraction. Verification was performed as we compared the mean area under curve (AUC) of the theta band of a dozen subjects staring at a wall or performing the NF. Most of the subjects (75%) increased their theta band AUC during the NF session compared with the trial staring at the wall (p = 0.041). Our system enables multiple feature selection and its machine learning capabilities allow an accurate discovery of patient-specific biomarkers and treatment targets. Its novel characteristics may allow for improved evaluation of patients and treatment outcomes.