MC

Current Trends in Science and Technology

an Open Access Publication ISSN: 0976-9730 | 0976-9498

Engineering and Technology

A Survey on Brain Computer Interface Technology

Ghodekar Prachi S., Patel Maherin M., Pokharkar Pooja M.,Dere Shruti R.
Jaihind College of Engineering,Kuran Savitribai Phule University of Pune Under guidance of Proff.Khatri A.A., Proff.Aryan Singh
Online First: February 26, 2018
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Abstract

BCI is fastest developing technology in advance computing.The main purpose behind BCI is to form the connection between homosapien brain and the technology.This is very effective technology which receives and control logical devices which works on familier part of the body.This technich mainlydesign for abnormal peoples.using this technology disable person developes themselves as like normal person.By studying this we introduce human brain with its anotomy.


BCI technology developes a theory of hardware and software.BCI measures the activities of human brain.BCI evaluates the signal which control the brain.It convert signal generated by brain to machine signals.


Concept of BCI gives us knowledge of various technologies like invasive and non-invasive.person gives instruction in signals which are recive by BCI.This techniques has widest scope.It has many neuroscience application.

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Feb 26, 2018
Published
Feb 26, 2018
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References

1. Pei, X., Barbour, D., Leuthardt, E., Schalk, G.: Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans. J. Neural. Eng. 8(4), 1–11 (2011) 2. Azar, A.T., Balas, V.E., Olariu, T.: Classification of EEG-based brain-computer interfaces. advanced intelligent computational technologies and decision support systems. Stud. Comput. Intell. 486, 97–106 (2014) 4. Buckner, R., Andrews-Hanna, J., Schacter, D.: The brain’s default network: anatomy. 3. Chapin JK, Moxon KA, Markowitz RS, Nicolelis MA. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci 1999;2:664–670. Chatrian GE. 4. Bayliss JD, Ballard DH. A virtual reality testbed for brain–computer interface research. IEEE Trans Rehabil Eng 2000;8:188–190. 5. Fadzal C, Mansor W, Khuan L. Review of brain computer interface application in diagnosing dyslexia. In: Control and System Graduate Research Colloquium (ICSGRC), 2011 IEEE. IEEE; 2011. p. 124–28. 6. HOFFMANN, U., GARCIA, G., VESIN, J.-M., DISERENS, K., AND EBRAHIMI, T. A boosting approach to P300 detection with application to brain-computer interfaces. In 2nd International IEEE EMBS Conference on Neural Engineering (2005), pp. 97 –100. 7. ABOOTALEBI, V., MORADI, M. H., AND KHALILZADEH, M. A. A new approach for EEG feature extraction in P300-based lie detection. Computer Methods and Programs in Biomedicine 94 (April 2009), 48–57. 8. A. Erfanian, F. Oveisi and A. Shadvar, “Feature Extraction by Mutual Information Based on Minimal-Redundancy-Maximal-Relevance Criterion and Its Application to Classifying EEG Signal for BrainComputer Interfaces”, Iran University of Science and Technology, Iran. 9. A. Kübler and N. Birbaumer, Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin Neurophysiol, 119, Nov., 2658–2666, (2008). 10. E.W. Sellers and E. Donchin, A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol: Off J Int Feder Clin Neurophysiol, 117, Mar., 538–548, (2006).
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References

1. Pei, X., Barbour, D., Leuthardt, E., Schalk, G.: Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans. J. Neural. Eng. 8(4), 1–11 (2011)
2. Azar, A.T., Balas, V.E., Olariu, T.: Classification of EEG-based brain-computer interfaces. advanced intelligent computational technologies and decision support systems. Stud. Comput. Intell. 486, 97–106 (2014) 4. Buckner, R., Andrews-Hanna, J., Schacter, D.: The brain’s default network: anatomy.
3. Chapin JK, Moxon KA, Markowitz RS, Nicolelis MA. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci 1999;2:664–670. Chatrian GE.
4. Bayliss JD, Ballard DH. A virtual reality testbed for brain–computer interface research. IEEE Trans Rehabil Eng 2000;8:188–190.
5. Fadzal C, Mansor W, Khuan L. Review of brain computer interface application in diagnosing dyslexia. In: Control and System Graduate Research Colloquium (ICSGRC), 2011 IEEE. IEEE; 2011. p. 124–28.
6. HOFFMANN, U., GARCIA, G., VESIN, J.-M., DISERENS, K., AND EBRAHIMI, T. A boosting approach to P300 detection with application to brain-computer interfaces. In 2nd International IEEE EMBS Conference on Neural Engineering (2005), pp. 97 –100.
7. ABOOTALEBI, V., MORADI, M. H., AND KHALILZADEH, M. A. A new approach for EEG feature extraction in P300-based lie detection. Computer Methods and Programs in Biomedicine 94 (April 2009), 48–57.
8. A. Erfanian, F. Oveisi and A. Shadvar, “Feature Extraction by Mutual Information Based on Minimal-Redundancy-Maximal-Relevance Criterion and Its Application to Classifying EEG Signal for BrainComputer Interfaces”, Iran University of Science and Technology, Iran.
9. A. Kübler and N. Birbaumer, Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin Neurophysiol, 119, Nov., 2658–2666, (2008).
10. E.W. Sellers and E. Donchin, A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol: Off J Int Feder Clin Neurophysiol, 117, Mar., 538–548, (2006).
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