Earthquake algorithm picks up the brain's vibrations. There are better ways to shake up your brain (Image: Federica Rainò/Getty) Your brain is buzzing. Analysing those natural vibrations might help spot tumours and other abnormalities, and now an algorithm normally used to study earthquakes has been adapted to do just that. The elasticity of different parts of the body is a useful way to tell if something is wrong.
Lumps can be a sign of cancer, of course, and stiffness in certain organs can indicate disease. Ultrasound scans that measure the elasticity of the liver, for example, can show up cirrhosis. It is more difficult to measure the elasticity of the brain. Catheline’s team, and others around the world, have been working on a way to use modified MRI scanners to measure brain elasticity. Shake it up But such devices haven’t made it to the clinic yet, in part because they aren’t very comfortable to use, says Catheline. Now Catheline is trying another approach. The body’s noise.
Synthetic Nervous System. Brain–computer interface. A brain–computer interface (BCI), sometimes called a mind-machine interface (MMI), direct neural interface (DNI), synthetic telepathy interface (STI) or brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[1][2] The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement.
History[edit] Berger's first recording device was very rudimentary. He inserted silver wires under the scalps of his patients. Animal BCI research[edit] A Remote Controlled Rat. Mind melds move from science fiction to science in rats. A Brain-to-Brain Interface for Real-Time Sharing of Sensorimotor Information : Scientific Reports. In our training paradigm, animals learned basic elements of the tasks prior to participating in any BTBI experiments. First, prospective encoder rats were trained to respond to either tactile or visual stimuli until they reached 95% correct trials accuracy. Meanwhile, decoder rats were trained to become proficient while receiving ICMS as a stimulus. A train of ICMS pulses instructed the animal to select one of the levers/nose pokes, whereas a single ICMS pulse instructed a response to the other option.
Decoder rats reached a 78.77% ± 2.1 correct trials performance level. The next phase of training began with the encoder rat performing ~10 trials of the motor or tactile task, which were used to construct a cortical ensemble template, i.e. the mean cortical neuronal activity for one of the responses. In experiment 1 (Figure 1), encoder rats (N = 3) pressed one of two levers after an LED on top of the lever was turned on. Full size image (222 KB) Full size image (314 KB)