Quality Assessment of Single-Channel EEG for Wearable Devices
The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise whi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/3/601 |
| _version_ | 1798005610869948416 |
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| author | Fanny Grosselin Xavier Navarro-Sune Alessia Vozzi Katerina Pandremmenou Fabrizio De Vico Fallani Yohan Attal Mario Chavez |
| author_facet | Fanny Grosselin Xavier Navarro-Sune Alessia Vozzi Katerina Pandremmenou Fabrizio De Vico Fallani Yohan Attal Mario Chavez |
| author_sort | Fanny Grosselin |
| collection | DOAJ |
| description | The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel. |
| first_indexed | 2024-04-11T12:41:59Z |
| format | Article |
| id | doaj.art-637cd66d40d9426c950c24bfe9d02c34 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:41:59Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-637cd66d40d9426c950c24bfe9d02c342022-12-22T04:23:27ZengMDPI AGSensors1424-82202019-01-0119360110.3390/s19030601s19030601Quality Assessment of Single-Channel EEG for Wearable DevicesFanny Grosselin0Xavier Navarro-Sune1Alessia Vozzi2Katerina Pandremmenou3Fabrizio De Vico Fallani4Yohan Attal5Mario Chavez6Sorbonne Université, UPMC Univ. Paris 06, INSERM U-1127, CNRS UMR-7225, Institut du Cerveau et de la Moelle Épinière (ICM), Groupe Hospitalier Pitié Salpêtrière-Charles Foix, 75013 Paris, FrancemyBrainTechnologies, 75010 Paris, FrancemyBrainTechnologies, 75010 Paris, FrancemyBrainTechnologies, 75010 Paris, FranceSorbonne Université, UPMC Univ. Paris 06, INSERM U-1127, CNRS UMR-7225, Institut du Cerveau et de la Moelle Épinière (ICM), Groupe Hospitalier Pitié Salpêtrière-Charles Foix, 75013 Paris, FrancemyBrainTechnologies, 75010 Paris, FranceCNRS UMR-7225, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, 75013 Paris, FranceThe recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.https://www.mdpi.com/1424-8220/19/3/601electroencephalography (EEG)single-channel EEGmuscular artefactsquality assessmentartefact detectionwearable systems |
| spellingShingle | Fanny Grosselin Xavier Navarro-Sune Alessia Vozzi Katerina Pandremmenou Fabrizio De Vico Fallani Yohan Attal Mario Chavez Quality Assessment of Single-Channel EEG for Wearable Devices Sensors electroencephalography (EEG) single-channel EEG muscular artefacts quality assessment artefact detection wearable systems |
| title | Quality Assessment of Single-Channel EEG for Wearable Devices |
| title_full | Quality Assessment of Single-Channel EEG for Wearable Devices |
| title_fullStr | Quality Assessment of Single-Channel EEG for Wearable Devices |
| title_full_unstemmed | Quality Assessment of Single-Channel EEG for Wearable Devices |
| title_short | Quality Assessment of Single-Channel EEG for Wearable Devices |
| title_sort | quality assessment of single channel eeg for wearable devices |
| topic | electroencephalography (EEG) single-channel EEG muscular artefacts quality assessment artefact detection wearable systems |
| url | https://www.mdpi.com/1424-8220/19/3/601 |
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