Abstract
Stroke is one of the leading causes of permanent disability in adults. The literature suggests that rehabilitation is key to early motor recovery. However, conventional therapy is labor and cost intensive. Robotic and functional electrical stimulation (FES) devices can provide a high dose of repetitions and as such may provide an alternative, or an adjunct, to conventional rehabilitation therapy. Brain-computer interfaces (BCI) could augment neuroplasticity by introducing mental training. However, mental training alone is not enough; but combining mental with physical training could boost outcomes. In the current case study, a portable rehabilitative platform and goal-oriented supporting training protocols were introduced and tested with a chronic stroke participant. A novel training method was introduced with the proposed rehabilitative platform. A 37-year old individual with chronic stroke participated in 6-weeks of training (18 sessions in total, 3 sessions a week, and 1 h per session). In this case study, we show that an individual with chronic stroke can tolerate a 6-week training bout with our system and protocol. The participant was actively engaged throughout the training. Changes in the Wolf Motor Function Test (WMFT) suggest that the training positively affected arm motor function (12% improvement in WMFT score).
Highlights
Stroke is the leading causes of permanent disability in adults in the world (Krebs et al, 1998; Mozaffarian et al, 2016)
During the Brain-computer interfaces (BCI) model training, the EEG data collected was sent to three types of feature extraction algorithm and cross-validated with three types of classifiers
For the participant in this study, the Common Spatial Pattern (CSP) feature algorithm together with Linear Discriminant Analysis (LDA) classifier returned the highest cross-validation accuracy of 80.1%
Summary
Stroke is the leading causes of permanent disability in adults in the world (Krebs et al, 1998; Mozaffarian et al, 2016). Rehabilitation can be a long process requiring hard labor with high cost (Mozaffarian et al, 2016). These drawbacks motivated researchers to find solutions to minimize the human labor and decrease rehabilitation cost (Freeman et al, 2009; Poli et al, 2013). Most robotic devices are capable of passively delivering a high number of training repetitions to the stroke-affected limb (Freeman et al, 2009; Loureiro et al, 2011; Ren et al, 2013; Herrnstadt et al, 2015; Proietti and Crocher, 2016). There is a need for rehabilitation interventions that provide intensive task-specific repetitions with mental engagement to achieve the best possible rehabilitation outcomes
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