|
Brain-Machine Interfaces: From Basic Research to Clinical Applications| title | Brain-Machine Interfaces: From Basic Research to Clinical Applications |
|---|
| start_date | 2022/11/03 |
|---|
| schedule | 16h-17h |
|---|
| online | no |
|---|
| location_info | INS |
|---|
| summary | Dr. Miguel Nicolelis has dedicated his career to investigating how the brains of animals in free behavior encode sensory and motor information. He proposed and demonstrated that animals and humans can use the electrical activities of their brains to control neuroprosthetic apparatus through brain-machine interfaces (BMIs). Over 25 years, Dr. Nicolelis created and perfected a new neurophysiological method known as chronic multisite, multielectrode recording. Using this approach in a variety of animal species, as well as intraoperative procedures in human patients, Dr. Nicolelis has created a new field of investigation, which aims to measure the simultaneous activity and interactions of large populations of neurons throughout the brain. During his trajectory, Dr. Nicolelis discovered several physiological principles that govern the functioning of mammalian brain circuits. The pioneering studies by Dr. Nicolelis are widely recognized for offering potential new therapies to patients suffering from neurological disorders such as severe paralysis, Parkinson's disease, and epilepsy. Numerous neuroscience laboratories in the United States and in Europe, Asia, and Latin America have incorporated the experimental paradigms created by Dr. Nicolelis to study a variety of mammalian neural systems, and his research has influenced basic and applied research in computer science, robotics, and biomedical engineering.
In this conversation, Dr. Nicolelis will talk about his findings and the fascinating perspectives of using brain-machine interfaces in restoring movement and neurorehabilitation of people with neurological disorders. |
|---|
| responsibles | Taverna |
|---|
Workflow history| from state (1) | to state | comment | date |
|---|
| submitted | published | | 2022/11/30 14:12 UTC |
| |
|