The automotive industry is foreseeing a future where driver and car will form a synergy, by allowing the car to predict the driver’s intent. Nissan has already successfully predicted the intent to make lane changes in a driving simulator with just six electroencephalography (EEG) electrodes on the movement related cortical potential using Machine Learning. This thesis assesses if the prediction can be improved with EEG layouts containing more electrodes. A large data set was recorded with a similar lane change paradigm in a driving simulator as Nissan’s original study. Seven predefined EEG layouts were assessed: including 4, 9, 18, 25, 32, 48 and 64 electrodes. The linear discriminant analysis classifier was used offline to classify two classes: driving straight or steering. Secondly, to assess if a different selection of electrodes could have performed better, partial least squares (PLS) regression was used to select electrodes based on importance. Lastly, it was also assessed if the classifier could predict the direction of lane change as well as intention, in other words, if the data set consisted of three classes: steering left, steering right and driving straight. There was a statistical difference between the performance of the seven layouts with two classes, but from Layout 3 (with 18 electrodes) and up this difference was no longer significant. The selection of electrodes based on PLS did not prove to be better than the predefined layouts. When the data is divided into three classes, for the prediction of direction, similar results were seen: there was a statistical difference between the layouts and Layout 3 proved to be sufficient. Performance improves with more electrodes, but this effect plateaus. Layout 3 contained a satisfactory amount of electrodes, namely 18, to predict lane change intent. Moreover, the direction of the intended lane change can also be predicted sufficiently with this layout.