Artifacts preprocessing in EEG is remarkably significant to extract reliable neural responses in the downstream analysis. A recently emerging powerful preprocessing tool among the EEG community is Artifacts Subspace Reconstruction (ASR). ASR is an unsupervised machine learning algorithm to identify and correct the transient-like non-stationary noisy samples. ASR is fully automatic, therefore, suitable for online applications. However, the performance of ASR is strongly dependent on the user-defined hyperparameter k. A poor choice of k might lead to severe performance degradation. In this work, we benchmark the performance of ASR against its parameter k. Toward this goal, we used the Temple University Hospital EEG Artifact Corpus (TUAR), which consists of 310 EEG files recorded in clinical settings from epileptic patients. Remarkably, these files are annotated for artifacts by trained personnel with a high inter-rater agreement score (κ > 0.8). Considering these reliable labels as ground truth, ASR has shown the best performance in artifacts cleaning with k ranging between 20 and 40.

Hyperparameter selection for reliable EEG denoising using ASR: a benchmarking study / Kumaravel, Velu Prabhakar; Buiatti, Marco; Farella, Elisabetta. - (2021), pp. 3638-3641. ((Intervento presentato al convegno Machine Learning for EEG Signal Processing (MLESP) 2021 tenutosi a Houston, TX, USA nel 10th December 2021 [10.1109/BIBM52615.2021.9669561].

Hyperparameter selection for reliable EEG denoising using ASR: a benchmarking study

Kumaravel, Velu Prabhakar;Buiatti, Marco;
2021-01-01

Abstract

Artifacts preprocessing in EEG is remarkably significant to extract reliable neural responses in the downstream analysis. A recently emerging powerful preprocessing tool among the EEG community is Artifacts Subspace Reconstruction (ASR). ASR is an unsupervised machine learning algorithm to identify and correct the transient-like non-stationary noisy samples. ASR is fully automatic, therefore, suitable for online applications. However, the performance of ASR is strongly dependent on the user-defined hyperparameter k. A poor choice of k might lead to severe performance degradation. In this work, we benchmark the performance of ASR against its parameter k. Toward this goal, we used the Temple University Hospital EEG Artifact Corpus (TUAR), which consists of 310 EEG files recorded in clinical settings from epileptic patients. Remarkably, these files are annotated for artifacts by trained personnel with a high inter-rater agreement score (κ > 0.8). Considering these reliable labels as ground truth, ASR has shown the best performance in artifacts cleaning with k ranging between 20 and 40.
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Houston, TX, USA
IEEE
978-1-6654-0126-5
Kumaravel, Velu Prabhakar; Buiatti, Marco; Farella, Elisabetta
Hyperparameter selection for reliable EEG denoising using ASR: a benchmarking study / Kumaravel, Velu Prabhakar; Buiatti, Marco; Farella, Elisabetta. - (2021), pp. 3638-3641. ((Intervento presentato al convegno Machine Learning for EEG Signal Processing (MLESP) 2021 tenutosi a Houston, TX, USA nel 10th December 2021 [10.1109/BIBM52615.2021.9669561].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/328597
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