Quality Assessment of Single-Channel EEG for Wearable Devices

Fanny Grosselin (1, 2) , Xavier Navarro-Sune (3) , Alessia Vozzi (3) , Katerina Pandremmenou (3) , Fabrizio de Vico Fallani (2, 4, 1) , Yohan Attal (3) , Mario Chavez (1, 2)

1 ICM - Institut du Cerveau = Paris Brain Institute
2 CHU Pitié-Salpêtrière [AP-HP]
3 myBrainTechnologies
4 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]

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. View Full-Text

Mots clés: Artefact detection; Wearable systems; Single-channel EEG; Electroencephalography (EEG); Muscular artefacts; Quality assessment

Domaines: Ingénierie biomédicale; Traitement du signal et de l'image [eess.SP]

Fanny Grosselin, Xavier Navarro-Sune, Alessia Vozzi, Katerina Pandremmenou, Fabrizio de Vico Fallani, et al.. Quality Assessment of Single-Channel EEG for Wearable Devices. Sensors, 2019, 19 (3), pp.601. ⟨10.3390/s19030601⟩. ⟨hal-02017731⟩