EEG Signal Classification and Human Sensation Recognition Using Machine Learning Techniques

Authors

  • Manini Monalisa Pradhan Dept. of Electronics and Telecommunications, Utkalmani Gopobandhu Institute of Engineering, Rourkela, India

Keywords:

EEG, PSD, SMO, RBF, BN, photoplethysmyograph

Abstract

Electroencephalogram (EEG) signals is generally contains enormous measures of huge number of data with uncountable classifications. The EEG signals turn ought more difficult amid assessing task if the captured data is over a long period. The powerful methodologies are important to secure the hidden and significant data delivered by the action in human cerebrum that covered inside the signals. In this manner, relevant strategies are produced to developed classify data. There are bunches of past researches and works related to the previously mentioned task, both feature extraction and classification have not been all around created in accomplishing more prominent precision. In this paper, methodologies were proposed for the emotions arrangement of EEG which can give high precision. The investigation of this thesis addresses and explores the accompanying issues: the best appropriate feature and full of emotion strategies for highlight extraction of EEG.

 

References

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Published

2021-02-28

How to Cite

[1]
M. M. Pradhan, “EEG Signal Classification and Human Sensation Recognition Using Machine Learning Techniques”, Int. J. Sci. Res. Comp. Sci. Eng., vol. 9, no. 1, pp. 66–71, Feb. 2021.

Issue

Vol. 9 No. 1 (2021): February

Section

Research Article

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