Car accidents are considered to be one of the major cause of death all around the world. In order to design electronic devices to improve car safety in the future, we aimed to identify electroencephalographic (EEG) activities to disentangle left from right steering actions in car driving simulation. For this purpose, we performed 128-channels EEG recordings during the driving of a car simulator. EEG scalp topographies resulting from and Independent Component Analysis were clustered across subjects. The corresponding time-frequency patterns of power activity were compared, revealing two distinct EEG clusters reacting with a coupled alpha and beta desynchronization in the preparation of the left and right steering onset, respectively. Topographic maps and dipole localization showed that these EEG components are originated from motor regions of the two hemispheres. Overall, these results illustrate that alpha and beta EEG rhythms could be exploited to predict the driver's intention in steering actions.

Alpha and beta EEG desynchronizations anticipate steering actions in a driving simulation experiment

Vecchiato G;Del Vecchio M;Marchenkova A;Avanzini P
2020

Abstract

Car accidents are considered to be one of the major cause of death all around the world. In order to design electronic devices to improve car safety in the future, we aimed to identify electroencephalographic (EEG) activities to disentangle left from right steering actions in car driving simulation. For this purpose, we performed 128-channels EEG recordings during the driving of a car simulator. EEG scalp topographies resulting from and Independent Component Analysis were clustered across subjects. The corresponding time-frequency patterns of power activity were compared, revealing two distinct EEG clusters reacting with a coupled alpha and beta desynchronization in the preparation of the left and right steering onset, respectively. Topographic maps and dipole localization showed that these EEG components are originated from motor regions of the two hemispheres. Overall, these results illustrate that alpha and beta EEG rhythms could be exploited to predict the driver's intention in steering actions.
2020
Istituto di Neuroscienze - IN -
Clustering
Driving
EEG
Independent Component Analysis
Wavelet decomposition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/403570
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