A Brief Summary of EEG Artifact Handling
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Abstract (arXiv)
The applications of Electroencephalogram (EEG) have been extended to out of laboratory and clinics recently due to the advancements in the technical capabilities. There are various advantageous of EEG, making it a preferable method for a wide range of applications; it is a noninvasive method, it is portable, it offers good time resolution and sufficient spatial resolution, besides there are low cost EEG systems available for a commercial use. Since the early uses of EEG, mainly as monitoring of diseases and pathologies, sleep staging and event related potential researches, it has been intertwined with undesired signal types which we call as artifacts. These pose great challenges in the practice of EEG based methods such as averaging for monitoring and diagnosis of diseases, and single-trial signal analysis for a relatively recent application in brain-computer interfaces. However, many techniques have been developed and under study for better detection and mitigation of these adverse events. The main artifact types and their handling are discussed in this brief summary.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Jan 2020.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2024An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detectioncited by 1
- 2025MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordingscited by 0
- 2026Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devicescited by 0
- 2026Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices10 Jun 2026
- 2025MR-EEGWaveNet: Multiresolutional EEGWaveNet for Seizure Detection from Long EEG Recordings23 May 2025
- 2024An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection1 Jan 2024