Safely operating a vehicle requires the full attention of the driver. Should the driver lose focus as a result of performing other tasks simultaneously, there could be disastrous outcomes. To gain insight into a driver’s mental state, the cognitive load experienced by the driver can be investigated. Measuring cognitive load can be done in numerous ways, one popular approach is the use of Electroencephalography (EEG). A lot of the data that can be extracted from EEG-signals, are redundant or irrelevant when trying to classify cognitive load. This thesis focuses on identifying EEG-features relevant to the classification of cognitive load experienced by drivers, through the use of feature selection algorithms. An experimental approach was utilized where three feature selection algorithms (ReliefF, BSS/WSS and BIRS) were applied to the available datasets. The feature subsets produced by the algorithms achieved higher classification accuracies compared to the use of all features. The best performing subset was generated by the ReliefF algorithm which achieved an accuracy of 66%. However, several other unique subsets achieved comparable results, therefore no single feature subset could be identified as most relevant for classification of cognitive load experienced by drivers. To conclude, the proposed approach could not identify features which could be used to confidently predict a driver’s mental state. / Vehicle Driver Monitoring (VDM)

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