The main purpose of this work is to establish an exploratory approach using electroencephalographic (EEG) signal, analyzing the patterns in the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining of EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the representation of time-frequency patterns of the signal's information content by WC qualiatative analysis. Results suggest that the proposed methodology is capable of identifying regions in time-frequency spectrum during the specified task of BCI. Furthermore, an example of a region is identified, and the patterns are classified using a Naïve Bayes Classifier (NBC). This innovative characteristic of the process justifies the feasibility of the proposed approach to other data mining applications. It can open new physiologic researches in this field and on non stationary time series analysis.

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This work presents a new perspective for fluorescence signal detection using specific optics on Lab-on-Chip devices. An apparatus was designed and implemented in order to assess the performance of a fluorescence technique using different detection spot configurations, using the chip itself as a waveguide for illumination. The experiments conducted investigate the influence of the dimensions - diameter and height - of the spot on the amplitude of the detection output signal. Results show that the configuration of optical interfaces must be considered in order to improve detection output, or to be able to detect less fluorophore molecules in the spot.