At the child brain facility at the Erasmus Medical Centre, multiple tests are performed with children who have one of several disorders. Two of these tests are done with electroencephalogram measurements and are called mismatch negativity and acoustic change complex. After a signal processing pipeline, the EEG measurements from these tests are shown as waveforms called event-related potentials. The goal of these measurements is to see if there is any relation between the waveforms and the disorder, age and other information about the subjects.\newline

These waveforms are measured for each subject, EEG electrode, and in the case of MMN, for different stimuli, which naturally results in a tensor data structure. Algorithms for discriminant analysis and regression that are developed to be applied to tensors are described and altered to take into account the properties of the EEG data. Discriminant analysis can be used to improve classification algorithms that distinguish disorders, while regression can be used to predict variables such as test scores based on the measured data. The algorithms are first tested on simulated data, which shows they can have some merit. Classification rates improve in most simulated cases when the discriminant analysis is applied to the data. Regression can also reliably predict variables when strong correlations are present between the input tensor and output variable. Based on the data from the child brain facility, the discriminant analysis still improves classification rates in some cases, but not as significantly as on the simulated data. Regression using the algorithms described in this thesis is not useful in predicting test scores from other experiments done with the subjects. \newline

The algorithms are also dissected to discover which specific features in the data tensor are weighted heavier by the algorithms. This is done to gain new insights into the differences between the disorders. When comparing the weights that are used for the simulated data with the features that are of importance, there is some relation, but not a very strong one. When the input tensor is however segmented in the time mode, the times of interest can be identified. The regression algorithm also resulted in weights that can be analysed to look at when and where the measurements relate to the output, but this did not show any interesting results. For real data, the segmented tensor resulted in some interesting insights about the differences between the various disorders.\newline

For the current dataset, the discriminant analysis algorithms do improve classification rates, but not by much. The features weighted the most by this algorithm in combination with a segmented tensor might give some insight into the disorders. The tensor regression methods do not work to predict a test score and do not give new insights into the disorders.

This bachelor end project thesis is describing ways to use dry-electrode electroencephalography (EEG) measurements for authentication. There are five proposed methods based on the literature study, which in this report are called: ’frequency tagging’, ’pseudowords’, ’familiar music’, ’mental tasks’, and ’emotions’. First, all methods were tested, however, this proved too much work to perform each of them thoroughly and wellbounded, so two were selected for further investigation.

For the method involving frequency tagging, experiments were done and the obtained data was used to extract features. This analysis showed that tagging can be discovered in the EEG data, but re-induction of tagged words was hard to achieve, which would make it difficult to create an authentication system reliant on it. Analyzing results from experiments to test the pseudoword method was more promising. Some similarities between the features that were expected from papers and features from our results were found. These features were therefore used for classification, and resulted in small improvements in accuracy. This accuracy did however vary a lot between different data sets.