San Francisco: Google is all set to collaborate with researchers to develop new artificial intelligence (AI) algorithms to improve brain stimulation devices to treat people with psychiatric illness and direct brain injuries, such as stroke. The tech giant has tied up with researchers at Mayo Clinic to develop a set of paradigms, or viewpoints, that simplify comparisons between effects of electrical stimulation on the brain. They developed a new type of algorithm called "Basis Profile Curve Identification". "Our findings show that this new type of algorithm may help us understand which brain regions directly interact with one another, which in turn may help guide placement of electrodes for stimulating devices to treat network brain diseases," said Kai Miller, a Mayo Clinic neurosurgeon. "As new technology emerges, this type of algorithm may help us to better treat patients with epilepsy, movement disorders like Parkinson's disease, and psychiatric illnesses like obsessive-compulsive disorder and depression," he added.

Google is all set to collaborate with researchers to develop new artificial intelligence (AI) algorithms to improve brain stimulation devices to treat people with psychiatric illness and direct brain injuries, such as stroke. The tech giant has tied up with researchers at Mayo Clinic to develop a set of paradigms, or viewpoints, that simplify comparisons between effects of electrical stimulation on the brain. They developed a new type of algorithm called "basis profile curve identification". "Our findings show that this new type of algorithm may help us understand which brain regions directly interact with one another, which in turn may help guide placement of electrodes for stimulating devices to treat network brain diseases," said Kai Miller, a Mayo Clinic neurosurgeon. "As new technology emerges, this type of algorithm may help us to better treat patients with epilepsy, movement disorders like Parkinson's disease, and psychiatric illnesses like obsessive compulsive disorder and depression," he added.

Currently, the state of the art for classification of psychiatric illness is based on audio-based classification. This thesis aims to design and evaluate a state of the art text classification network on this challenge. The hypothesis is that a well designed text-based approach poses a strong competition against the state-of-the-art audio based approaches. Dutch natural language models are being limited by the scarcity of pre-trained monolingual NLP models, as a result Dutch natural language models have a low capture of long range semantic dependencies over sentences. For this issue, this thesis presents belabBERT, a new Dutch language model extending the RoBERTa[15] architecture. belabBERT is trained on a large Dutch corpus (+32GB) of web crawled texts. After this thesis evaluates the strength of text-based classification, a brief exploration is done, extending the framework to a hybrid text- and audio-based classification. The goal of this hybrid framework is to show the principle of hybridisation with a very basic audio-classification network. The overall goal is to create the foundations for a hybrid psychiatric illness classification, by proving that the new text-based classification is already a strong stand-alone solution.