Topic:Brain computer interfacingone by SURAJ.CK(8547383878),ARJUN ECE S5
The brain has been clearly understood to be the seat of the mind for less than four centuries and the history of brain-computer-interfaces (BCI) starts with Hans berger's discovery of the electrical activity of human brain and the development of electroencephalograpy (EEG).It includes the use of various techniques to either directly or indirectly image the structure, function, or pharmacology of the brain. It is a relatively new discipline within medicine and neuroscience.The brain is composed of millions of neurons. These neurons work together in complex logic and produce thought and signals that control our bodies. When the neuron fires, or activates, there is a voltage change across the cell, (~100mv) which can be read through a variety of devices. When we want to make a voluntary action, the command generates from the frontal lobe. Signals are generated on the surface of the brain. These electric signals are different in magnitude and frequency. By monitoring and analyzing these signals we can understand the working of brain. A brain-machine interface (BMI) in its scientific interpretation is a combination of several hardware and software components trying to enable its user to communicate with a computer by intentionally altering his or her brain waves. The task of the hardware part is to record the brainwaves– in the form of the EEG signal – of a human subject, and the software has to analyze that data. In other words, the hardware consists of an EEG machine and a number of electrodes scattered over the subject‘s skull. The EEG machine, which is connected to the electrodes via thin wires, records the brain-electrical activity of the subject, yielding a multi-dimensional (analog or digital) output. The values in each dimension (also called channel) represent the relative differences in the voltage potential measured at two electrode sites. The software system has to read, digitize (in the case of an analog eeg machine), and preprocess the Eeg data (separately for each channel), ―understand‖ the subject‘s intentions, and generate appropriate output. To interpret the data, the stream of EEG values is cut into successive segments, transformed into a standardized representation, and processed with the help of a classifier. There are several different possibilities for the realization of a classifier; one approach – involving the use of an artificial neural network (ANN) – has become the method of choice in recent yearsBci’s provide a new and possibly only communication channel for people suffering from severephysical disabilities but having intact cognitive functions. For example these devices could help intreating (or rather overcoming) paraplegia or amyotrophia.somewhat related to this topic is the field of neuroprosthetics which deals with constructing andsurgically implanting devices used for replacing damaged areas of the brain and more generally forneural damages of any kind. A bci could enable the attachment of robotic limbs without the use of the organism’s original nervous system so bci offers paralyzed patients improved quality of life.Apart from the medical applications it is also employed in the mental typewriting,Rapid visual recognition, working memory encoding, Artificial silicon retina transplantation,error and conflict perception.