EEGBased Brain Controlled Robo and Home Appliances

EEG-Based Brain Controlled Robo and Home Appliances

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-47 Number-3
Year of Publication : 2017
Authors : Ms Nanditha, Smt. Christy Persya A
DOI :  10.14445/22315381/IJETT-V47P227

Citation 

Ms Nanditha, Smt. Christy Persya A "EEG-Based Brain Controlled Robo and Home Appliances", International Journal of Engineering Trends and Technology (IJETT), V47(3),161-169 May 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Brain Computer Interface (BCI) systems are the tools which are proposed to help the damaged people who are impotent of making a motor response to interface with a computer using brain signal. The aim of BCI is to translate brain activity into digital form which performs as a command for a computer. The BCI application can be used in different areas like Education, Industrial, Gaming and Medical areas. In my project, EEGbased Brain controlled Robotic, and Home automation using IOT has been developed using BCI with the help of NeuroSky technology. eSense is a NeuroSky`s quick fix algorithm for distinguishing mental states. The ThinkGear technology in NeuroSky mindwave headset fetches out the user brainwave signal and removes the muscle movement and atmosphere noise. For the remaining signals, the eSense algorithm is then appealed, which results in the elucidated eSense meter values. The fetched brain signals are transmitted to the Microcontroller via HC-05 Bluetooth module. The robotic module designed consists of Arduino Microprocessor coupled with DC motor to perform the control. The attention level was used to monitor the direction of the robotic and meditation level was used to monitor the home appliances using IOT. The wireless BCI system could allow the paralyzed people to control their robotic and home appliances without any difficulty, provided it is more increased, portable and wearable.

 References

[1] Hong Zeng, Member, IEEE, and Aiguo Song, Senior Member, IEEE, “Optimizing Single-Trial EEG Classification by Stationary Matrix Logistic Regression in Brain–Computer Interface”, Neural Networks and Learning Systems, IEEE Transactions, Vol. 27, Nov 2016.
[2] B. Bijitha, Nandakumar Paramparambath "Brain-Computer Interface Binary Classification using Regularized CSP and Stacked Concepte", International Journal of Engineering Trends and Technology (IJETT), V38(5),271-275 August 2016.
[3] Renji V.Mathew, Jasmin Basheer "An EEG Based Vehicle Driving Safety System Using Automotive CAN Protocol", International Journal of Engineering Trends and Technology (IJETT), V26 (4), 212-216 August 2015.
[4] W. Samek, Member, IEEE, Motoaki Kawanabe, and Klaus- Robert M¨uller, Member, IEEE, “Divergence-Based Framework for Common Spatial Patterns Algorithms”, Biomedical Engineering, IEEE, Vol. 7, Apr 2014.
[5] Dwipjoy Sarkar , Atanu Chowdhury. "A Real Time Embedded System Application for Driver Drowsiness and Alcoholic Intoxication Detection", International Journal of Engineering Trends and Technology (IJETT), V10 (9),461-465 April 2014.
[6] M. Arvaneh, Student Member, IEEE, C. Guan, Senior Member, IEEE, Kai Keng Ang, Member, IEEE, and Chai Quek, Senior Member, IEEE, “Optimizing Spatial Filters by Minimizing Within-Class Dissimilarities in Electroencephalogram-Based Brain–Computer Interface”, Neural Networks and Learning Systems, IEEE Transactions, Vol. 24, April 2013.
[7] Haihong Zhang, Member, IEEE, Huijuan Yang, Member, IEEE, and Cuntai Guan, Senior Member, IEEE, “EEG-Based Brain–Computer Interface using Bayesian Learning for Spatial Filtering”, Neural Networks and Learning Systems, IEEE Transactions, Vol. 24, July 2013.
[8] W. Samek, C. Vidaurre, K. R. Muller, and M. Kawanabe, “Stationary common spatial patters for brain-computer interfacing”, J. Neural Eng., Vol. 9, 2012.
[9] Wei Wu*, Student Member, IEEE, Xiaorong Gao, Member, IEEE, Bo Hong, Member, IEEE, and Shangkai Gao, Fellow, IEEE, “Classifying Single-Trial EEG During Motor Imagery by Iterative Spatio-Spectral Patterns Learning (ISSPL)”, Biomedical Engineering, IEEE Transactions, Vol. 55, June 2008.
[10] Benjamin Blankertz, Klaus-Robert Müller, Dean Krusienski, Gerwin Schalk, Jonathan R. Wolpaw, Alois Schlögl, Gert Pfurtscheller, José del R. Millán, Michael Schröder, Niels Birbaumer, “The BCI Competition III: Validating Alternative Approaches to Actual BCI Problems”, Neural Systems and Rehabilitation Engineering, IEEE Transactions, Vol. 8, 2006.

Keywords
Brain Computer Interface, EEG, eSense Technique, Robotic, home Appliances.