Brain computer interface

Adult subject studies can be divided into two broad categories response to basic sensory stimulation and response to more complex cognitive tasks. Recently, there have also been reports applying the NIR spectroscopy to brain computer interface research [18] and studyinh the correlation of hemodynamic response to computational cognitive models [94]. 

S. Coyle, T. Ward, C. Markham, and G. McDarby. On the suitability of near-infrared (nir) systems for next-generation brain-computer interfaces. Physiological Measurement, 25 815-822, 2004. 

Leuthardt E.C., Schalk G., Wolpaw J.R., Ojemann J.G., and Moran D.W. 2004. A brain-computer interface using electrocorticographic signals in humans. J. Neural. Eng. 1 63-71. 

D.R. Kipke, R.J. Vetter, J.C. Williams, and J.F. Hetke, Silicon-substrate intracortical microelectrode arrays for long-term recording of neuronal spike activity in cerebral cortex. In 2nd International Meeting on the Brain-Computer Interfaces for Communication and Control. Rensselaerville, Institute of Electrical and Electronics Engineers Inc., New York, 2002. 

Biosignal-brain EEG Enables communication and control with brain signal patterns. Used in the expanding field of brain-computer interfaces... 

Wolpaw, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., and Vaughan, T., 2002. Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791. 

At the forefront of engineering innovation in healthcare are ways of interfacing devices such as prosthetic limbs to the peripheral nervous systems and brain [10], These can, for example, aim to allow movement of a prosthetic limb with subconscious control of a type similar to that of natural limbs. Such developments raise issues regarding the nature and status of the delegated action, which is partly controlled by a microprocessor. Provision of non-invasive brain-computer interfaces to aid patients with degenerative conditions such as amyotrophic lateral syndrome (Tocked-in syndrome ) may require very detailed knowledge of how the brain processes information and how this is affected by the condition. 

J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan (2(XX)), Brain-computer interface technology A review of the first international meeting, IEEE Transactions on Rehabilitation Engineering 8 164-173. 

Collinger JL, Boninger ML, Bruns TM, Curley K, Wang W, and Weber DJ, Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. Journal of Rehabilitation Research and Development, vol. 50 2, pp. 145-160, 2013. 

Brain-con trolled interfaces (BGls), a subset of NP, represent a new method of communication based on brain-generated neural activity. Still in an experimental phase, they offer hope to patients with severe motor dysfunction. These interlaces capture neural activity mediating a sulject s intention to act and translate it into command signals transmitted to a computer (brain-computer interface) or robotic limb. Independent of peripheral nerves and muscles. 

Keywords— Comparative analysis. Optimal performance. Motor imagery, EEG, Brain computer interface. 

Brain-computer interface technology uses the signals from the brain to control external devices. BCI consists of the invasive type and the non-invasive type. The non-invasive type has several advantages, such as its straightforward usability and the ease with which brain signals can be obtained. Non-invasive BCI measures the EEG from the scalp and evaluates the mental activity of human subjects. The point is the connection between the characteristics of EEG and mental activity [1]. 

Comparative Analysis of the Optimal Performance Evaluation for Motor Imagery Based EEG-Brain Computer Interface... 

This work was supported by the Korea Food and Drug Administration. [08142-Medical Device-370, Development of Brain-Computer Interface Medical Instrument Evaluation Technology based on EEG for a Neural Prosthesis, 2008.06 - 2009.04]. 

Dornhege G, Mill an J. del R, Hinterberger T, McFarland D, and M uller K.-R, editors (2006) Towards Brain-Computer Interfacing, MIT Press... 

Guger C, Ramoser H, and Pfurtscheller G (2000) Real-Time EEG Analysis with Subject-Specific Spatial Patterns for a Brain-Computer Interface (BCI), IEEE Transactions on RehabiUtation Engineering, Vol. 8, No. 4, Dec. 2000, pp. 447-456... 

Guger C, Edlinger G, Harkam W, Niedermayer I, Pfurtscheller G (2003) How Many People are Able to Operate an EEG-Based Brain-Computer Interface (BCI) , IEEE Transactions on neural systems and rehabilitation engineering, vol. 11, Issue. 2, Jun. 2003, 145-147. 

Blankertz B., Losch F., Krauledat M., Dornhege G., Curio G., Muller K.-R., The Berlin Brain-Computer Interface Accurate Performance From First-Session in BCI-Nalve Subjects , IEEE Transactions on Biomedical Engineering, Volume 55, Issue 10, Oct. 2008, pp. 2452-2462... 

F Lotte, M Congedo, A Lecuyer, F Lamarche and B Amaldi, A review of classification algorithms for EEG-based brain-computer interfaces , Journal of Neural Engineering, vol. 4,2007, R1 R13. 

The non-invasive BMIs which require signal amplification and classification are known as brain computer interface (BCI) [3], A BCI is a communication system which is not depend on brain s normal output pathways and EEG signal is the most common signal used in BCI systems [4] [5]. BCI classification algorithms combine machine... 

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