Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for I MS). All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. These parameters create the MS database composed of 83 measurements. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (I MS). Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS.

Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. 5Department of Science, Landspitali University Hospital, Reykjavík, Iceland.3Department of Anatomy, University of Iceland, Reykjavík, Iceland.2Department of Advanced Biomedical Sciences, University Hospital of Naples “Federico II”, Naples, Italy.1Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavík, Iceland.Karlsson 1, Valdís Baeringsdóttir 1, Hannes Petersen 3,4 and Paolo Gargiulo 1,5* Svansson 1, Sólveig Agnarsdóttir 1, Gunnar H. Marco Recenti 1†, Carlo Ricciardi 1,2†, Romain Aubonnet 1, Ilaria Picone 1,2, Deborah Jacob 1, Halldór Á.

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