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Published on Feb 12, 2016

Abstract

Brain-Computer interface is a staple of science fiction writing. Init's earliest incarnations nomechanism was thought necessary, as the technology seemed so far fetched that no explanation was likely. As more became known about the brain however, the possibility has become more real and the science fiction more technically sophisticated.

Description of Brain-Computer Interface

Recently, the cyberpunk movement has adopted the idea of "jacking in", sliding "biosoft" chips into slots implanted in the skull (Gibson, W. 1984). Although such biosofts are still science fiction, there have been several recent steps toward interfacing the brain and computers. Chief among these are techniques for stimulating and recording from areas of the brain with permanently implanted electrodes and using conscious control of EEG to control computers. Some preliminary work is being done on synapsing neurons on silicon transformers and on growing neurons into neural networks on top of computer chips.The most advanced work in designing a brain-computer interface has stemmed from the evolution of traditional electrodes. There are essentially two main problems, stimulating the brain (input) and recording from the brain (output).

Traditionally, both input and output were handled by electrodes pulled from metal wires and glass tubing.Using conventional electrodes, multi-unit recordings can be constructed from mutlibarrelled pipettes. In addition to being fragile and bulky, the electrodes in these arrays are often too far apart, as most fine neural processes are only .1 to 2 µm apart. Pickard describes a new type of electrode, which circumvents many of the problems listed above. These printed circuit micro-electrodes (PCMs) are manufactured in the same manner of computer chips. A design of a chip is photoreduced to produce an image on a photosensitive glass plate. This is used as a mask, which covers a UV sensitive glass or plastic film.

The interface between the brain and computers, either through interpreting EEGs or through recording directly through PCMs is currently limited by computing strength. Conventional computers are well suited to processing linear data, but only have limited application to more distributed processes such as pattern recognition. In order to address these problems, neural net computers are modeled after the brain's complex system of weighted synapses.

 

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