BRAIN COMPUTER INTERFACE

INTRODUCTION

Brain computer interface (BCI) is a fast-growing technology in that researchers aim to build a direct communication channel between the computer interface and human brain. BCI is software and hardware communications system which allows humans to interact with others without the involvement of muscles and peripheral devices and with the help of control signals generated from the activity of electroencephalographic.

BRAIN COMPUTER INTERFACE:

Brain computer interface also known as Brain machine interface (BMI) which creates a new channel of non-muscular for transferring human’s intentions to external devices like speech synthesizers, computers, neural prostheses and assistive appliances. This is mainly attractive for persons with severe motor disabilities. This type of interface will improve their life quality and at the same time, BCI will minimize the cost of intensive care (Khalid et al., 2009). A brain computer interface is an artificial intelligence system which can identify some patterns set in brain signals in the five stages they are acquisition of signal, signal or preprocessing enhancement, feature extraction, classification and control interface. The signal acquisition captures and brain signals as well as performs artifact processing and noise reduction.

The preprocessing stage prepares the brain signals in an appropriate form for further processing. In the stage of feature extraction recognizes discriminative information in the signal of brain which will be recorded. Once measured, the brain signal is mapped onto a vector comprising discriminant and effective features from the noticed signals. The extraction of this useful information is a very complicated task. Brain signals are mixed with other signals. These signals come from a finite set of brain activities which overlap in both space and time. The brain signal will not usually stationary and twisted by artifacts like electro-oculography (EOG) or Electromyography (EMG). The feature vector will be sometimes in low dimension, in order to minimize the complexity in the feature extraction stage, but without useful information loss. The classification stage, it classifies the brain signals by considering feature vectors. The good discriminative features choice is important in order to achieve effective recognition for pattern, to decipher the person’s intentions. The control interface stage transforms the classified signals into informative or meaningful commands for any connected device like computer or wheelchair (Wolpaw et al., 2002).

CONCLUSION:

It is concluded that brain computer interface is a control and communication channel which does not depend on the muscles and peripheral nerves. BCI will improve the life quality of the humans and also reduces the intensive care cost.

REFERENCES:

1.      Khalid, M.B.; Rao, N.I.; Rizwan-i-Haque, I.; Munir, S.; Tahir, F. Towards a Brain Computer Interface Using Wavelet Transform with Averaged and Time Segmented Adapted Wavelets. In Proceedings of the 2nd International Conference on Computer, Control and Communication (IC4’09) Karachi, Sindh, Pakistan, February 2009; pp. 1–4.

2.      Wolpaw, J.R.; Birbaumer, N.; McFarland, D.J.; Pfurtscheller, G.; Vaughan, T.M. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 2002, 113, 767–791