Artifact Removal Methods in EEG Recordings: A Review

Article Properties
Abstract
Cite
Mathe, Mariyadasu, et al. “Artifact Removal Methods in EEG Recordings: A Review”. Proceedings of Engineering and Technology Innovation, vol. 20, 2021, pp. 35-56, https://doi.org/10.46604/peti.2021.7653.
Mathe, M., Mididoddi, P., & Krishna, B. T. (2021). Artifact Removal Methods in EEG Recordings: A Review. Proceedings of Engineering and Technology Innovation, 20, 35-56. https://doi.org/10.46604/peti.2021.7653
Mathe, Mariyadasu, Padmaja Mididoddi, and Battula Tirumala Krishna. “Artifact Removal Methods in EEG Recordings: A Review”. Proceedings of Engineering and Technology Innovation 20 (2021): 35-56. https://doi.org/10.46604/peti.2021.7653.
Mathe M, Mididoddi P, Krishna BT. Artifact Removal Methods in EEG Recordings: A Review. Proceedings of Engineering and Technology Innovation. 2021;20:35-56.
Refrences
Title Journal Journal Categories Citations Publication Date
10.1109/JBHI.2014.2370646
10.1109/ACCESS.2019.2957401
10.1109/BIBM.2016.7822742
Removal of muscle artefacts from few‐channel EEG recordings based on multivariate empirical mode decomposition and independent vector analysis Electronics Letters
  • Technology: Electrical engineering. Electronics. Nuclear engineering
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
 14 2018
Removal of Artifacts from EEG Signals: A Review

 Sensors
  • Technology: Chemical technology
  • Science: Chemistry: Analytical chemistry
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Science: Mathematics: Instruments and machines
  • Science: Chemistry: Analytical chemistry
  • Science: Chemistry
 283 2019
Citations
Title Journal Journal Categories Citations Publication Date
Electroencephalogram Signal Classification and Artifact Removal with Deep Networks and Adaptive Thresholding Journal of Shanghai Jiaotong University (Science) 2023
Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

 International Journal of Engineering and Technology Innovation
  • Technology: Engineering (General). Civil engineering (General)
 1 2023
A Novel End-to-end Network Based on a bidirectional GRU and a Self-Attention Mechanism for Denoising of Electroencephalography Signals Neuroscience
  • Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
  • Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
  • Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
 5 2022
Citations Analysis
Category Category Repetition
Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry1
Technology: Engineering (General). Civil engineering (General)1
The category Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A Novel End-to-end Network Based on a bidirectional GRU and a Self-Attention Mechanism for Denoising of Electroencephalography Signals and was published in 2022. The most recent citation comes from a 2023 study titled Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds. This article reached its peak citation in 2023, with 2 citations. It has been cited in 3 different journals. Among related journals, the International Journal of Engineering and Technology Innovation cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year