Proceedings ArticleDOI
Time Warping Solutions for Classifying Artifacts in EEG
Srihari Maruthachalam,Mari Ganesh Kumar,Hema A. Murthy +2 more
- Vol. 2019, pp 4537-4540
TLDR
This paper devise algorithms for detection and classification of artifacts into head nod, jaw movement and eye-blink is performed using two different varieties of time warping; namely, linear time warped, and dynamic time Warping.Abstract:
The most common brain-computer interface (BCI) devices use electroencephalography (EEG). EEG signals are noisy owing to the presence of many artifacts, namely head movement, and facial movements like eye blinks or jaw movements. Removal of these artifacts from EEG signals is essential for the success of any downstream BCI application. These artifacts influence different sensors of the EEG. In this paper, we devise algorithms for detection and classification of artifacts. Classification of artifacts into head nod, jaw movement and eye-blink is performed using two different varieties of time warping; namely, linear time warping, and dynamic time warping. The average accuracy of 85% and 90% is obtained using the former, and the later, respectively.read more
Citations
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Brain Activity Investigation byEEGProcessing: Wavelet Analysis, Kurtosis andRenyi's Entropy forArtifact Detection
Giuseppina Inuso,Carlo Morabito +1 more
TL;DR: A multiresolution analysis, based on EEG wavelet processing, to extract the cerebral EEG rhythms and a method based on Renyi's entropy and kurtosis to automatically identify the Wavelet components affected by artifacts.
Proceedings ArticleDOI
Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals
TL;DR: This work proposes a fully automated and unsupervised eyeblink detection algorithm, Blink that self-learns user-specific brainwave profiles for eye-blinks, and does away with any user training or manual inspection requirements.
References
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Proceedings Article
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