Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

In this paper, we address the problem of quantifying the commonly observed disorganization of the stereotyped wave form of the ERP associated with the P300 component in patients with Alzheimer's disease. To that extent, we propose two new measures of complexity which relate the spectral content of the signal with its temporal waveform: the spectral matching coefficient and the spectral matching entropy. We show by means of experiments that those measures effectively measure complexity and are related to the shape in an intuitive way. Those indexes are compared with commonly used measures of complexity when comparing AD patients against age-matched healthy controls. The results indicate that AD ERP signals are, indeed, more complex in the shape than that of controls, and this result is evidenced mainly by means of our new measures which have a better performance compared to similar ones. Finally, we try to explain this increase in complexity in light of the communication through coherence hypothesis framework, relating commonly found changes in the EEG with our own results.

Motivation modulates behaviour depending upon contextual and internal cues. Like animals, successful artificial agents must implement different behavioural strategies in order to satisfy dynamical needs. Such causal factors emerge from internal homeostatic or allostatic processes, as well as from external stimuli or threats. However, when two or more needs coalesce, a situation of motivational conflict ensues. In this work we present a four-stage dynamical framework for the resolution of motivational conflict based upon principles from dynamical systems and statistical mechanics. As a central mechanism for the resolution of conflict we propose the use of potentials with multiple wells or minima. This model leads to behavioural switching either by means of a bifurcation or by the stochastic escape from one of the wells. We present analytical and simulation results that reproduce known motivational conflict phenomena observed in the study of animal behaviour, in the case of two conflicting motivations.

This is a repository copy of Obstacle avoidance using stereo vision and deep reinforcement learning in an animal-like robot.

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