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Abstract
Brain computer Interface (BCI) is one of the clinical applications that might restore communication to people with severe motor disabilities. Recording and analysis of electrophysiological brain signals is the base of BCI research and development. Electrocorticography (ECoG) is a semi invasive record to brain signals from electrode grids on the cortex surface. ECoG signal makes possible localization of the source of neural signals with respect to certain brain functions due to its high spatial resolution. This study is a step towards exploring the usability of ECoG signal as BCI input technique and a multidimensional BCI control. The objective of this thesis is to predict individual finger movement from ECoG signal by combining both classification and regression problems in machine learning of signal responses (regression via classification), on the other hand addressing the signal responses variability within a single subject. The dataset used in this thesis is the one presented in the fourth dataset from BCI competition IV. The difficulty is that; there is no simple and direct relation between ECoG signals and finger movements