EVALUATION AND OPTIMIZATION OF DIFFERENT SPECTRAL ESTIMATION METHODS FOR EEG SIGNALS

The goal of this work was the evaluation of various spectral estimation methods with regard to their suitability for classifying EEG data. A test environment was implemented in which the algorithms are optimized and evaluated using various artificial and real EEG data. The methods are based on autoregressive approaches, as well as from FFT, wavelet, and matching pursuit-based spectral estimations. The evaluation showed that the quality of the results strongly correlate with the computational effort of the algorithm. The matching pursuit algorithm (MP) was implemented and further optimized since it had the best test result and had good scalability. Even under a sufficiently low runtime, it still gave good results.