Latest AI and machine learning research in neurology for healthcare professionals.
High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational...
Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising.Here, we pre...
Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack...
Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. ...
Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of em...
Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral ...
Deep learning-based neural decoders have emerged as the prominent approach to enable dexterous and intuitive control of neuroprosthetic hands. Yet few...
During development, biological neural networks produce more synapses and neurons than needed. Many of these synapses and neurons are later removed in ...
Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an importa...
Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging technique...
Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to ef...
An ambulatory elder with SCI, AIS C, balance deficits, and right ankle-foot-orthosis participated. RobUST-intervention comprised six 90 min-sessions o...
At present, people spend most of their time in passive rather than active mode. Sitting with computers for a long time may lead to unhealthy condition...
Robotic thymectomy is the most innovative surgical approach for treating disease of the anterior mediastinum. Robotic surgery offers low postoperative...
In this study, an online transfer TSK fuzzy classifier O-T-TSK-FC is proposed for recognizing epilepsy signals. Compared with most of the existing tra...
Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification...
The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiolog...
Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation i...
Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) application...