Removal of Artifact from the Brain Signal Using Discrete Cosine Transform-Reference-Cited by-同舟云学术

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Removal of Artifact from the Brain Signal Using Discrete Cosine Transform

Published:2021 Issue: Volume: Page:239-249
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
Short-container-title:

Author:

Behera Sandhyalati,Mohanty Mihir Narayan

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-8752-8_25

Reference19 articles.

1. Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2, 1–11.

2. Molla, M. K. I., Islam, M. R., Tanaka, T., & Rutkowski, T. M. (2012). Artifact suppression from EEG signals using data adaptive time domain filtering. Neurocomputing, 97, 297–308.

3. Sameni, R., & Gouy-Pailler, C. (2014). An iterative subspace denoising algorithm for removing electroencephalogram ocular artifacts. Journal of Neuroscience Methods, 225, 97–105.

4. Ghosh-Dastidar, S., Adeli, H., & Dadmehr, N. (2008). Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Transactions on Biomedical Engineering, 55, 512–518.

5. Bayram, K. S., Kızrak, M. A., & Bolat, B. (2013). Classification of EEG signals by using support vector machines. In 2013 IEEE INISTA (pp. 1–3). IEEE.

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1. Use of Modified S-Transform for EEG Artifact Removal;2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT);2023-06-09

2. Design of optimized adaptive filter for artifact removal from EEG signal;Journal of Statistics & Management Systems;2023

3. Human stress observation from brain signal using ensemble classifier;INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INNOVATIVE TECHNOLOGIES IN AGRICULTURE”;2023

4. A Machine Learning Approach for Artifact Removal from Brain Signal;Computer Systems Science and Engineering;2023

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