Latest AI and machine learning research in neurology for healthcare professionals.
Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral va...
Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition...
Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke p...
BACKGROUND: The possibility to modify the usually pathological patterns of coordination of the upper-limb in stroke survivors remains a central issue ...
For the past few years, we have developed flexible, active, and multiplexed recording devices for high resolution recording over large, clinically rel...
INTRODUCTION: Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implic...
BACKGROUND: Contrary to common belief of clinicians that hemorrhagic stroke survivors have better functional prognoses than ischemic, recent studies s...
BACKGROUND: Epilepsy is a chronic neurological condition, with over 30% of cases unresponsive to treatment. Zebrafish larvae show great potential to s...
Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed ...
A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online int...
OBJECTIVE: To determine whether applying controlled resistance forces to the legs during the swing phase of gait may improve the efficacy of treadmill...
Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an importan...
CONTEXT: The purpose of this report was to describe the improvement in walking ability using the Hybrid Assistive Limb® (HAL®) intervention in the cas...
A low-cost robotic interface was used to assess the visuo-motor performance of patients with Alzheimer's disease (AD). Twenty AD patients and twenty a...
BACKGROUND: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a strok...
Technology-supported training is emerging as a solution to support therapists in their efforts providing high-intensity, repetitive, and task-specific...
Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compressio...
The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal n...
OBJECTIVE: This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascul...
BACKGROUND: The number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to im...