| DOI | Trouver le DOI : https://doi.org/10.1109/IEMTRONICS51293.2020.9216365 |
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| Auteur | Rechercher : Hajra, Sujoy Ghosh; Rechercher : Gopinath, Shishir; Rechercher : Liu, Careesa C.; Rechercher : Pawlowski, Gabriela; Rechercher : Fickling, Shaun D.; Rechercher : Song, Xiaowei; Rechercher : D'Arcy, Ryan C.N. |
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| Format | Texte, Article |
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| Conférence | 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), September 9-12, 2020, Vancouver, BC, Canada |
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| Sujet | biomedical signal processing; neurotechnology; electroencephalography (EEG); neural signal processing; empirical mode decomposition (EMD); artifact removal; electrodes; noise reduction; arrays; time series analysis; correlation; transforms |
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| Résumé | Background: Brain function assessments based on event-related potentials (ERPs) derived from electroencephalography (EEG) are increasingly being conducted in realistic out-of-the-laboratory settings for clinical and non-clinical uses. For rapid testing and practical limitations, such applications require the use of low-density electrode arrays. A major impediment to their use in these applications is the lack of denoising techniques capable of removing artefactual contamination and isolating the ERPs features of interest within low-density arrays. Methods: A novel denoising technique combining empirical mode decomposition (EMD) with template matching procedure is developed and applied to individual-channel data, and the results of this new approach are compared to the results of a conventional (independent component analysis) denoising approach. Both whole-epoch morphological comparisons and specific ERP feature amplitude comparisons were undertaken at the group and individual level for a variety of ERPs indexing sensory (N100), attention (P300) and language processing (N400) using data from 31 healthy adults. Results: The new denoising technique successfully enables the capture of ERPs ranging from low-level sensation to attention to language processing (all p<; 0.05). Intra-class correlation analysis reveals high degree of similarity in the time series waveforms derived from the new and the conventional denoising approaches for all ERPs (highest r=0.89, all p<; 0.001). Analysis of specific ERP features of interest reveals no significant differences between the ERP amplitudes of the waveforms generated using the two techniques, and Pearson correlation suggests a high degree of similarity at the individual level (0.88 for N100, 0.78 for P300, and 0.80 for N400, all p<; 0.05). Conclusion: The new denoising technique is capable of operating on individual-channel EEG data, and produces results that are similar to those produced by conventional denoising techniques that use data from large whole-head electrode arrays. This new approach may thus enable more widespread use of ERP techniques in real world settings with low-density electrode arrays. |
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| Date de publication | 2020-09-12 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 7eeb7f2b-4c9e-40f1-a8da-13fb2baad63c |
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| Enregistrement créé | 2022-02-14 |
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| Enregistrement modifié | 2022-02-14 |
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