Artifact Removal and of EEG Signal Classification for Brain Computer Interface (BCI) using Back Propagation
Rajashekhar U.1, Neelappa2, Rajesh3
1Rajashekhar U., Research Scholar, Department of Electronics & Communication Engineering, Government Engineering College, Kushalnagar, Karnataka and affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
2Neelappa, Department of Electronics & Communication Engineering, Government Engineering College, Kushalnagar and affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
3Rajesh, Department of Electronics & Communication Engineering, East Point college of Engineering and Technology Bengalore, Karnataka, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1275-1282 | Volume-8 Issue-6, March 2020. | Retrieval Number: E5965018520/2020©BEIESP | DOI: 10.35940/ijrte.E5965.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A brain-computer interface (BCI) provides a communication passage between the brain and an external stratagem. The Brain and its EEG signals are acquired from the BCI along its control signals and its widely used mechanism in the field of the biomedical fields. In this research work, an artifacts are removed algorithm in the EEG is developed and simulated in the MATLAB 2017a software tool. EEG signals from patients are recoded while recording some of the artificial signals added to it, which are instigated by using eye blinks, eye movement, muscle, and cardiac noise, and also non-biological sources. Using suitable filters these artificial signals can be removed. This paper aims to remove the artificial signals from EEG signals and parameters like mean, standard. Deviation are calculated and compared with other methods such as LAMICA and FASTERs. In the paper, it is also the proposed arrangement of EEG signals for the discovery of typical and anomalous exercises utilizing Wavelet change and Artificial Neural Network (ANN) Classifier is considered. Here, the framework utilizes the back proliferation with feed-forward for order which pursues the ANN grouping. Accuracy of the classification is calculated and compared with other states of art publications and found that it is better.
Keywords: Electroencephalogram (EEG), Brain-Computer Interface (BCI), Artifacts, Independent Component Analysis (ICA), Blind signal separation (BSS).
Scope of the Article: Classification.