Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Shamsabad Farahani Shoorangiz Shams; Arefi Mohammad Mahdi; ZAERI AMIR HOSSEIN

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Majlesi Journal of Electrical Engineering Year:2020 | Volume:14 | Issue:4 Page(s): 133-144

Download Full-Text

View:

148

Download:

Cites:

Information Journal Paper

Title

Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks

Author(s)

Shamsabad Farahani Shoorangiz Shams | Arefi Mohammad Mahdi | ZAERI AMIR HOSSEIN | Issue Writer Certificate

Keywords

Artifacts

Bees Algorithm (BA)

Electroencephalography

Optimization

Radial Basis Function Neural Network (RBFNN)

Wavelet Transform (WT)

Abstract

Electroencephalography (EEG) is a major clinical tool to diagnose, monitor and manage neurological disorders which is mostly affected by Artifacts. Given the importance and the need for an automated method to remove Artifacts, in this paper some intelligent automated methods are proposed which are composed of three main parts as extraction of effective input, filtering and filter Optimization. Wavelet transform is utilized to extract the effective input, and the wavelet approximation coefficients are used as an effective input signal. In addition, Radial Basis Function Neural Network (RBFNN) has been used for filtering. The appropriate number of RBFs has been selected using extensive simulations, and the optimal value of spread parameter has been achieved by Bees algorithm (BA). Finally, the proposed artifact removal schemes have been evaluated on some real contaminated EEG signals in Mashad Ghaem hospital database. The results show that the proposed artifact removal schemes are able to effectively remove Artifacts from EEG signals with little underlying brain signal distortion.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Shamsabad Farahani, Shoorangiz Shams, Arefi, Mohammad Mahdi, & ZAERI, AMIR HOSSEIN. (2020). Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks. MAJLESI JOURNAL OF ELECTRICAL ENGINEERING, 14(4), 133-144. SID. https://sid.ir/paper/771620/en

Vancouver: Copy

Shamsabad Farahani Shoorangiz Shams, Arefi Mohammad Mahdi, ZAERI AMIR HOSSEIN. Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks. MAJLESI JOURNAL OF ELECTRICAL ENGINEERING[Internet]. 2020;14(4):133-144. Available from: https://sid.ir/paper/771620/en

IEEE: Copy

Shoorangiz Shams Shamsabad Farahani, Mohammad Mahdi Arefi, and AMIR HOSSEIN ZAERI, “Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks,” MAJLESI JOURNAL OF ELECTRICAL ENGINEERING, vol. 14, no. 4, pp. 133–144, 2020, [Online]. Available: https://sid.ir/paper/771620/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops