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This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.

Abstract. In this paper we describe a genetic fuzzy relational neural network (FRNN) designed for classification tasks. The genetic part of the proposed system determines the best configuration for the fuzzy relational neural network. Besides optimizing the parameters for the FRNN, the fuzzy membership functions are adjusted to fit the problem. The system is tested in several infant cry database reaching results up to 97.55%. The design and implementation process as well as some experiments along with their results are shown.