NOVEL COMPUTATIONAL ELECTROENCEPHALOGRAPHIC (EEG) METHODOLOGIES FOR AUTISM MANAGEMENT AND EPILEPTIC SEIZURE PREDICTION

The doctoral thesis deals with a novel methodology of looking and processing electroencephalographic (EEG) data. The first part deals with real-time brain stimulation in the form of a sonified neurofeedback therapy derived from a clinically comparable portable, 4-channel EEG system. The therapy aims to provide an effective management for symptoms of the Autism Spectrum Disorder (ASD). ASD is characterized with a high level of delta electroencephalographic waveform levels, while alpha and beta prove to be present at lower levels especially in the frontal-temporal regions. The treatment aims at lowering delta waves and promoting alpha and beta waveforms. The second part of the thesis focuses on using EEG data in the prediction of epileptic seizures. With the help of custom built algorithms and neural networks, an effective prediction of an epileptic seizure can be achieved.