BackgroundBrain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke.Methods32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data.ResultsStroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset.ConclusionsThis exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.

Being able to navigate, recall important locations, and find the way home are critical skills, essential for survival for both humans and animals. These skills can be examined in the laboratory using the Morris water maze, often considered the gold standard test of animal navigation. In this task, animals are required to locate and recall the location of an escape platform hidden in a pool filled with water. Because animals can not see the platform directly, they must use various landmarks in the environment to escape. With recent advances in technology and virtual reality (VR), many tasks originally used in the animal literature can now be translated for human studies. The virtual water maze task is no exception. However, a number of issues are associated with these mazes, including cost, lack of flexibility, and lack of standardization in terms of experimental designs and procedures. Here we present a virtual water maze system (NavWell) that is readily downloadable and free to use. The system allows for the easy design of experiments and the testing of participants on a desktop computer or fully immersive VR environment. The data from four independent experiments are presented in order to validate the software. From these experiments, a set of procedures for use with a number of well-known memory tests is suggested. This potentially can help with the standardization of navigational research and with navigational testing in the clinic or in an educational environment. Finally, we discuss the limitations of the software and plans for its development and future use.

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Stroke disrupts motor, sensory, and cognitive systems in survivors. Unlike in physical impairments, assessment of cognitive function is often inadequate, as no standardized procedure to monitor cognitive recovery post stroke exists. We evaluated a number of novel task-orientated tools designed to assess subtle cognitive deficits (including memory, attention, and executive functioning) in a sample of stroke patients. Although unimpaired on MMSE (Mini-Mental State Examination)-based indices of cognition, memory, and intelligence, stroke survivors were significantly impaired on tasks testing visual attention, spatial/relational processing, and associative memory. We recommend a standardized multidomain cognitive assessment and propose that cognitive deficits post stroke require in-depth assessment to inform patient-orientated rehabilitation.