Efficient mathematical procedural model for brain signal improvement from human brain sensor activities

Автор: Rajib Chowdhury, A.F.M. Saifuddin Saif

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 10 vol.10, 2018 года.

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Human brain signals obtained by the human brain sensor electrodes measure the cerebral activities on the human brain. The main aim of our research is to improve the human brain activities based on the human brain signal. The entire procedure contains three steps. The first step is to acquire the brain signal, then develop this brain signal with the proposed method and finally improve the human brain activities with this modified brain signal. The entire procedure will proceed in a proposed Neuroheadset device embedded with necessary sensors using the non-invasive technique. This device will help to acquire the brain signal, modify this signal and improve the brain activities with this modified brain signal. In this research, we illustrated the first two steps like signal acquisition and signal modification. In the experiment, we used Electroencephalogram as an efficient non-invasive signal acquisition technique for acquiring the brain signal and also introduced a proposed method to modify this signal. This method helped to improve the human brain signal using the required times of the iteration process. In the experiment level, several iteration processes have been done to get above 90% improvement rate of the brainwaves. In this research, the improved signal has been considered based on the generated brain signal in various aspects like human intelligence, memory and also the capability of better feelings.

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Artificial intelligence, human brain sensor activities, human brain signal, proposed method, proposed neuroheadset device

Короткий адрес: https://sciup.org/15016003

IDR: 15016003   |   DOI: 10.5815/ijigsp.2018.10.05

Список литературы Efficient mathematical procedural model for brain signal improvement from human brain sensor activities

  • Raja Majid Mehmood, Ruoyu Du and Hyo Jong Lee. Optimal feature selection and deep learning ensembles method for emotion recognition from human brain EEG sensors. IEEE Access, 2017, 5 (99):1-1.
  • Raja Majid Mehmood and Hyo Jong Lee. EEG based emotion recognition from human brain using Hjorth parameters and SVM. International Journal of Bio-Science and Bio-Technology, 2015, 7 (3):23-32.
  • Suwicha Jirayucharoensak, Setha Pan-Ngum, and Pasin Israsena. EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Hindawi Publishing Corporation, The Scientific World Journal, 2014, Volume 2014, Article ID 627892.
  • John Atkinsona and Daniel Campos. Improving BCI-based emotion recognition by combining EEG feature selection and Kernel classifiers. Expert Systems with Applications: An International Journal, 2016, Volume 47, Issue C, 35-41.
  • Boris Gutmann, Andreas Mierau, Thorben Hülsdünker, Carolin Hildebrand, Axel Przyklenk, Wildor Hollmann, and Heiko Klaus Strüder. Effects of physical exercise on individual resting state EEG alpha peak frequency. Hindawi Publishing Corporation, Neural Plasticity, 2015, Volume 2015, Article ID 717312.
  • Amar R. Marathe, Vernon J. Lawhern, Dongrui Wu, David Slayback, and Brent J. Lance. Improved neural signal classification in a rapid serial visual presentation task using active learning. IEEE Trans. on Neural Systems and Rehabilitation Engineering, 2016, 24 (3):333-343.
  • Raja Majid Mehmood and Hyo Jong Lee. Exploration of prominent frequency wave in EEG signals from brain sensors network. International Journal of Distributed Sensor Networks, 2015, Volume 2015, Article ID 386057.
  • Rajeshree Mahendra Patil, D.M. Kate and A.P. Thakare. Design and implementation of brain computer interface for wheelchair control. International Research Journal of Engineering and Technology (IRJET), 2016, Volume: 03, Issue 02.
  • Victor Shih, Ludan Zhang, Christian Kothe, Scott Makeig and Paul Sajda. Predicting decision accuracy and certainty in complex brain-machine interactions. IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2016.
  • Muhammad N. Fakhruzzaman, Edwin Riksakomara, and Hatma Suryotrisongko. EEG wave identification in human brain with EMOTIV EPOC for motor imagery. Science Direct, Procedia Computer Science, 72 (2015):269 – 276.
  • Norizam Sulaiman, Cheng Chee Hau, Amran Abdul Hadi, Mahfuzah Mustafa and Shawal Jadin. Interpretation of human thought using EEG signals and Lab VIEW. IEEE International Conference on Control System, Computing and Engineering, 2014, 28 - 30, Penang, Malaysia.
  • T. Kameswara Rao, M. Rajyalakshmi and T. V. Prasad. An exploration on brain computer interface and its recent trends. International Journal of Advanced Research in Artificial Intelligence (IJARAI), 2012, 1 (8):17-22.
  • M. Rajya Lakshmi, T. V. Prasad and V. Chandra Prakash. Survey on EEG signal processing methods. International Journal of Advanced Research in Computer Science and Software Engineering, 2014, Volume 4, Issue 1, January 2014 ISSN: 2277 128X.
  • Chuang Li, Han Yuan, Diamond Urbano, Yoon-Hee Cha and Lei Ding. ICA on sensor or source data: A comparison study in deriving resting state networks from EEG. Engineering in Medicine and Biology Society (EMBC), 2017, 39th Annual International Conference of the IEEE, 14 September 2017.
  • Munyaradzi C. Rushambwa and Asaithambi Mythili. Impact assessment of mental subliminal activities on the human brain through neuro feedback analysis. 3rd International Conference on Biosignals, images and instrumentation (ICBSII), 16-18 March 2017, Chennai.
  • Jalal Karam, Salah Al Majeed, Christofer N. Yalung and Lela Mirtskhulava. Neural network for recognition of brain wave signals. International Journal of Enhanced Research in Science, Technology & Engineering, 2016, Vol. 5 Issue 10, ISSN: 2319-7463.
  • David C Jangraw, Jun Wang, Brent J Lance, Shih-Fu Chang, and Paul Sajda. Neurally and ocularly informed graph-based models for searching 3D environments, J. Neural Eng. 11 (2014) 046003 (12pp).
  • Sabbir Ibn Arman, Arif Ahmed, and Anas Syed. Cost-effective EEG signal acquisition and recording system. International Journal of Bioscience, Biochemistry and Bioinformatics, 2012, 2 (5):301-304.
  • Durgesh K. Srivastava and Lekha Bhambhu. Data classification using support vector machine. Journal of Theoretical and Applied Information Technology, 2010, 12 (1): 1-7.
  • Himani Bhavsar and Mahesh H. Panchal. A review on support vector machine for data classification. International Journal of Advance Research in Computer Engineering & Technology (IJARCET), 2012, Volume 1, Issue 10, ISSN: 2278-1323.
  • Adrian G. Bors. Introduction of the radial basis function (RBF) networks. Online Symposium for Electronics Engineers, 2001, 1 (1):1-7.
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