Using independent vector analysis (IVA) to analyze and find subgroups in functional magnetic resonance imaging (fMRI) and functional ultrasound (fUS) data requires a lot of manual labour. Recently methods like subgroup identification using IVA (SI-IVA) and IVA for common subspace identification (IVA-CS) have tried to reduce this labour through automation. However, both methods did not test for accuracy. This thesis shows through simulations that these proposed methods are not accurate or robust enough to be trusted and that spectral clustering is a better alternative for automatic subgroup identification. Spectral clustering is then incorporated into the analysis of experimental fUS data of two groups of mice to try and identify these automatically. In this analysis, adaptive constrained IVA (acIVA) was used to incorporate references, further improving the interpretability of the results as components are directly linked to prior constraints.
However, applying subgroup analysis showed that the mice could not be clustered based on their response to the stimuli. Still, spectral clustering is more accurate in the simulations making it a promising alternative for automatic subgroup identification. Furthermore, combining spectral clustering with acIVA makes the results more interpretable due to constrained components not being subject to permutation ambiguity.

60 million people around the world have epilepsy, which is a neurological disorder that severely impacts their day to day life negatively. Currently available methods to reduce the effects of epilepsy are either ineffective or require expensive and invasive surgery. A new method has been found that can suppress epilepsy without the need of surgery, called Transcutaneous Vagus Nerve Stimulation (t-VNS). Detecting epileptic seizures is important for this method, as the stimulation should only be used during a seizure. Traditionally, detecting epilepsy is done using scalp-Electroencephalography (EEG), which requires a controlled environment and is hard to use in day to day life. Recently, advancements have been made in ear-EEG, which allows for EEG outside a controlled environment. This study focuses on detecting epilepsy using ear-EEG. Ear-EEG was simulated using scalp-EEG channels close to the ear. After low-pass filtering and downsampling the results were obtained using features obtained from the Wavelet Transform (WT) and Fourier Transform (FT) in combination with several Machine Learning (ML) models; these being a random forest, a Support Vector Machine (SVM), and a Neural Network (NN). Furthermore PCA was also applied to the features, with a threshold of 0%, 95% and 99%. The results clearly show that using the WT outperforms features from the FT. Furthermore, out of the three models, the NN consistently has the best sensitivity for detecting seizures. The best sensitivity was achieved using WT features with a NN and a threshold of 99% for the PCA. The accuracy and sensitivity are 99.3% and 83.5% respectively, which is comparable to previous ear-EEG based research detecting epileptic seizures.