A Brain-Computer Interface (BCI) is a network device that converts brain activity into a desired mechanical action. A modern BCI action would entail the utilization of a brain-activity analyzer and neural networking algorithm to collect, interpret, and translate complicated brain signals for a machine. A robotic arm, a voice box, or any automated assistive equipment, such as prosthetics, wheelchairs, and iris-controlled screen cursors, could be among these machines. Artificial intelligence (AI) and machine learning (ML) in particular enable a better knowledge of brain activity as well as improved brain-computer interface (BCI) connection mechanisms. Early diagnosis and accurate non-pharmacological treatment of neurological diseases and disorders can be achieved by incorporating AI/ML into the data collecting and monitoring phases of neuromodulation or neurofeedback. Furthermore, machine learning allows for the analysis of huge amounts of patient data in order to improve the efficacy of neuromodulation and neurofeedback.
Title : Cerebral vascular calcium signaling in diabetic alzheimer's disease-related dementias
Yong Xiao Wang, Albany Medical College, United States
Title : Development of imaging based biomarkers for neurovascular abnormalities in neurodegenerative diseases
Jun Hua, Johns Hopkins University School of Medicine, United States
Title : Deep learning-based risk assessment of cognitive impairment using health examination data
Kaoru Sakatani, The University of Tokyo, Japan
Title : Him, that person and me
Simon C Barton, Stroke Survivor, United States
Title : Evaluation of the neuroprotective potential of indicaxanthin from opuntia ficus indica fruit against dysmetabolism-related neurodegeneration both in vivo and in vitro
Mario Allegra, University of Palermo, Italy
Title : Psychosocial considerations in management of corticobasal degeneration
Esraa Askar, Forest Hills Hospital, United States