A Detailed Study of EEG based Brain Computer Interface

• Autorzy: Sharma G. , Sharma N. , Singh T. , Agrawal R.

Identyfikatory

DOI

10.15439/2017KM47

• Konferencja: International Conference on Information Technology and Knowledge Management (1 ; 22-23.12.2017 ; New Delhi, India)
• Języki publikacji: EN

Abstrakty

EN

Brain Computer Interface (BCI) generate a direct method to communicate with the outside world. Many patients are not able to communicate. For example:- the patient who are suffered with the several disease like post stroke - the process of thinking, remembering \& recognizing can be challenging. Because of spinal cord injuries or brain stem stroke the patient loss the monitoring power. EEG based brain computer interface (BCI) feature is beneficial to scale the brain movement \& convert them into a instruction for monitoring. In this paper our objective is to study about various applications of EEG based signal of the different disease like spinal cord injury, post stroke and ALS (amyotrophic lateral sclerosis) etc.

Słowa kluczowe

EN

brain computer interface; electroencephalography signal; EEG; amyotrophic lateral sclerosis; ALS; post-stroke; electrocorticography; ECoG

PL

interfejs; mózg; sygnał; elektroencefalografia; EEG; stwardnienie zanikowe boczne; ALS; udar; elektrokortykografia; ECoG

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2018
• Tom: Vol. 14
• Strony: 137--143
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Sharma G.

• Faculty of computer Application, Manav Rachna International Institute of Research and Study, India

autor

Sharma N.

• Faculty of computer Application, Manav Rachna International Institute of Research and Study, India

autor

Singh T.

• Faculty of computer Application, Manav Rachna International Institute of Research and Study, India

autor

Agrawal R.

• Faculty of computer Application, Manav Rachna International Institute of Research and Study, India

Bibliografia

• 1. Joseph N. Mak [Member, IEEE], Jonathan R.Wolpaw,Albany, Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects
• 2. Roxana Toderean 1, Iuliana Chichisan.Application of Support Vector Machine for the Classification of Sensorimotor Rhythms in Brain Computer Interface
• 3. Eric C. Leuthardt, Kai J. Miller, Gerwin Schalk, Rajesh P. N. Rao, and Jeffrey G. Ojemann.Electrocorticography-Based Brain Computer Interface—The Seattle Experience
• 4. Aroosa Umair, Ureeba Ashfaq, and Muhammad Gufran Khan.Recent Trends, Applications, and Challenges of BCI
• 5. Aswinseshadri. K Dr.V. Thulasi Bai. feature selection in brain computer interface using genetics method
• 6. I. N. Angulo-Sherman and D. Guti´errez,. Effect of different feedback modalities in the performance of brain-computer interfaces
• 7. Yijun Wang, Bo Hong*, Xiaorong Gao, and Shangkai Gao.Phase Synchrony Measurement in Motor Cortex for Classifying Single-trial EEG during Motor Imagery
• 8. Lei Qin, Lei Ding And Bin He. Motor imagery classification by means of source analysis for brain computer interface applications.
• 9. Sarah N. Abdulader*, Ayman Atia, Mostafa_sami M. Mostafa. Brain computer interface: Applications and challenges
• 10. Melody M. Moore. Real word application for brain computer Interface technology.
• 11. School of computer engineering, kiit university, bhubaneswar India. Brain computer interface issues on hand movement
• 12. Klaus Robert Miiller Michael Tangermann.Machine learning for real time signal trial EEG analsis from brain computer interfacing to mental state monitoring.
• 13. Jonatha R-wolpaw*and dennis j.mcfaxland. Control of a two-dimensional movement signal by a noninvasive brain computer interface in human
• 14. Christoph guger, alo is schlogl, chritra newper. Rapid prototyping of an EEG based brain computer interface BCI
• 15. Chuanchu Wang,Kok Soon Phua, Kai Keng Ang, Cuntai Guan, Haihong Zhang, Rongeseng Lin.A feasibility study of non-Invasive Motor imaginery. BCI based robotic rehabilitation for stroke patients
• 16. Fabien lotte, Marco Congedo, Anatole Lecuyer, Fabrice Lamarche, Bruno Arnaldi. A review of classification algorithm for eeg based brain computer interface.
• 17. Luzheng Bi, Member, IEEE, Xin-An Fan, and Yili Liu,Member, IEEE.EEG-Based brain -controlled mobile robots: A survey.
• 18. Sergio Machado, Leonardo ferreira Almada and Ramesh Naidu Annavarapu. Progress & prospective in EEG-based brain computer interface: clinical application in neurorehablitation.
• 19. Nik Khadijah Nik Aznan,Yeon-Mo Yang. Applying kalman filter in eeg based brain computer interface for motor imagery classification.
• 20. Rasool Ameri, Aliakbar Pouyan,Vahid Abolghasemi. EEG signal based on sparse representation in brain computer interface application.

Uwagi

1. Preface

2. Technical Session: First International Conference on Information Technology and Knowledge Management