Valentina Bono1, Saptarshi Das2, Wasifa Jamal3, Koushik Maharatna4. 1. Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom. Electronic address: vb2a12@ecs.soton.ac.uk. 2. Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom. Electronic address: sd2a11@ecs.soton.ac.uk. 3. Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom. Electronic address: wj4g08@ecs.soton.ac.uk. 4. Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom. Electronic address: km3@ecs.soton.ac.uk.
Abstract
BACKGROUND: Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. NEW METHOD: In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. RESULTS: Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR)-an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. COMPARISON WITH EXISTING METHOD(S): Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. CONCLUSIONS: Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts.
BACKGROUND: Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. NEW METHOD: In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. RESULTS: Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR)-an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. COMPARISON WITH EXISTING METHOD(S): Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. CONCLUSIONS: Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts.
Authors: Carlos Magno Medeiros Queiroz; Gustavo Moreira da Silva; Steffen Walter; Luciano Brinck Peres; Luiza Maire David Luiz; Samila Carolina Costa; Kelly Christina de Faria; Adriano Alves Pereira; Marcus Fraga Vieira; Ariana Moura Cabral; Adriano de Oliveira Andrade Journal: Front Comput Neurosci Date: 2022-07-26 Impact factor: 3.387