This article presents a significant advancement in the field of deep brain stimulation (DBS) through the development of a closed-loop system that integrates reinforcement learning (RL) and neural simulation techniques.…
read moreParkinson’s disease (PD) and essential tremor are two major causes of pathological tremor among people over 60 years old. Due to the side effects and complications of traditional tremor management…
read moreThe deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between…
read moreObjective: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For…
read moreGraph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been…
read moreRehabilitation robots are expected to save time, money, and expand access to some physical therapies. Moreover, robots can perform certain actions that human therapists cannot which may open up novel…
read moreHand function assessments in a clinical setting are critical for upper limb rehabilitation after spinal cord injury (SCI) but may not accurately reflect performance in an individual’s home environment. When…
read moreDriver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to…
read moreSubjective clinical rating scales represent the gold-standard diagnosis of motor function following stroke, however in practice they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution.…
read moreFall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, it is not easy to obtain large-scale annotated fall events…
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