Classification of Non-invasive recording of Electroencephalography Brain Signals using Hoeffding tree
DOI:
https://doi.org/10.31642/JoKMC/2018/070103Keywords:
BCI, Electroencephalography, Stons, FICA, JADE, Hoeffding Tree classifierAbstract
there is a considerable advancement in research that concern brain-computer interfaces (BCI). BCI can be defined as a communication system that is developed for allowing individuals experiencing complete paralysis sending commands or messages with no need to send them via normal output pathways of brain. EEG recording are Affected by cardiac noise, blinks, eye movement, in addition to non-biological sources (such as power-line noise).There will be an obstacle if the subject generates an artifact since will violate the specification of BCI as a non-muscular communication channel and the ability of subjects suffering degenerative diseases could be lost and This artifacts(noise) leads to incorrect classification accuracy .The presented study has the aim of being a sufficient reference in BCI system and also emphasize algorithms which are capable of separating and removing the noise that interferes with the task-related Electroencephalography (EEG) signal for the best features . The task is the motions of the index finger of right or left .The separation process based BSS technique ,This separating would be having an effective speeding impact on classifying patterns of EEG. and classified using classifier ( Hoeffding Tree). The proposed algorithm is tested and trained with the use of real recorded signals of EEG . Experiments reveal that the proposed classifier with the stone algorithm leads to high classification results up to the classification accuracy 79%.Downloads
References
L. F. GomeNicolas-Alonso and J. Z-Gil, ―Brain computer interfaces, a review,‖ sensors, vol. 12, no. 2, pp. 1211–1279, 2012. DOI: https://doi.org/10.3390/s120201211
F. A, R. A. El-KhoriMousabi, and M. E. Shoman, ―An integrated classification method for braincomputer interface system,‖ in 2015 Fifth International Conference on Digital Information Processing and Communications (ICDIPC), 2015, pp. 141–146.
S. Kalagi, J. Machado, V. Carvalho, F. Soares, and D. Matos, ―Brain computer interface systems using non-invasive electroencephalogram signal: A literature review,‖ in 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), 2017, pp. 1578–1583. DOI: https://doi.org/10.1109/ICE.2017.8280071
E. Gallego Jutglà, ―New signal processing and machine learning methods for EEG data analysis of patients with Alzheimer’s disease.‖ Universitat de Vic-Universitat Central de Catalunya, 2015.
S. Garg and R. Narvey, ―Denoising & feature extraction of EEG signal using wavelet transform,‖ Int. J. Eng. Sci. Technol., vol. 5, no. 6, p. 1249, 2013.
N. T. H. Anh, T. H. Hoang, V. T. Thang, and T. T. Q. Bui, ―An artificial neural network approach for electroencephalographic signal classification towards brain-computer interface implementation,‖ in 2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2016, pp. 205–210.
S. Bhaduri, A. Khasnobish, R. Bose, and D. N. Tibarewala, ―Classification of lower limb motor imagery using K Nearest Neighbor and Naïve-Bayesian classifier,‖ in 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), 2016, pp. 499–504. DOI: https://doi.org/10.1109/RAIT.2016.7507952
D. Buvaneash and M. R. S. John, ―Brain robot interface using artificial neural network,‖ in IOP Conference Series: Materials Science and Engineering, 2018, vol. 402, no. 1, p. 12017. DOI: https://doi.org/10.1088/1757-899X/402/1/012017
W. Zheng et al., ―Classification of Motor Imagery Electrocorticogram Signals for Brain-Computer Interface,‖ in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019, pp. 530–533. DOI: https://doi.org/10.1109/NER.2019.8716963
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Zainab kadham Obais, Taha Mohammed Hasan
This work is licensed under a Creative Commons Attribution 4.0 International License.
which allows users to copy, create extracts, abstracts, and new works from the Article, alter and revise the Article, and make commercial use of the Article (including reuse and/or resale of the Article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made and the licensor is not represented as endorsing the use made of the work.