A Noval approach for EEG signal artefact removal using Deep convolutional Algorithm
Main Article Content
Abstract
Brain activity is analyzed with the help of EEG signals. It has small amplitude,
thus it is influenced by the various artefacts. It is highly needed that the artefacts should
get eliminated from the EEG signals by efficacious processing. This paper explores the
technicality of deep learning in order to remove the artefacts. For which Pre-processing
and feature extraction is to be carried out initially for the EEG signals. Here the wavelet
transform is applied to extract the wavelet features, which are scattered to the projected
classifier which is called killer whale fractional calculus optimization (KWFCO). The
technique is carried out with experimentation for removing artefacts like EMG, EOG,
ECG and random noise on the EEG signal. The proposed technique's simulation results
have been presented, and they have been found to perform well with improvement in MSE
and SNR.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.