Section: New Results

Quality Assessment of Single-Channel EEG for Wearable Devices

Participants : Fanny Grosselin, Xavier Navarro-Sune, Alessia Vozzi, Katerina Pandremmenou, Fabrizio de Vico Fallani, Yohan Attal, Mario Chavez [Correspondant] .

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

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