Automatic Feature Selection for Sensorimotor Rhythms Brain-Computer Interface Fusing Expert and Data-Driven Knowledge.

Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods. Despite the advances in BCI that ML can be credited with, the performance of BCI solutions is still not up to the mark, posing a major barrier to the widespread use of this technology. This paper proposes a novel, automatic feature selection method for BCI able to leverage both data-dependent and expert knowledge to suppress noisy features and highlight the most relevant ones thanks to a fuzzy logic (FL) system.

Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery.

Functional electrical stimulation (FES) can support functional restoration of a paretic limb post-stroke. Hebbian plasticity depends on temporally coinciding pre- and post-synaptic activity. A tight temporal relationship between motor cortical (MC) activity associated with attempted movement and FES-generated visuo-proprioceptive feedback is hypothesized to enhance motor recovery.

Complete Heart Block and Ventricular Asystole Caused by Vagus Nerve Stimulation Therapy.

Left vagus nerve stimulation (VNS) is an advanced therapeutic option for refractory, drug-resistant epilepsy. A 45-year-old woman with a history of refractory catamenial focal epilepsy since age 16, treated with a five-drug antiepileptic regimen and VNS (implanted eight and one-half years prior), presented with dyspnea, chest discomfort, and lightheadedness. During observation, symptoms recurred and were associated with bradycardia (<20 bpm) and a complete atrioventricular node (AVN) block.

Learning to control a BMI-driven wheelchair for people with severe tetraplegia.

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings.