Pulse rate estimation using imaging photoplethysmography: Generic framework and comparison of methods on a publicly available dataset

Objective: to provide an algorithmic framework for comparing methods of pulse rate estimation using imaging photoplethysmography (iPPG), and to investigate performance of several existing methods on a publicly available dataset. Approach: first we reveal essential steps of pulse rate estimation from facial video and review methods applied at each of the steps. Then we investigate performance of these methods for DEAP dataset www.eecs.qmul.ac.uk/mmv/datasets/deap/ containing facial videos and reference contact photoplethysmograms. Main results: best assessment precision is achieved when pulse rate is estimated using continuous wavelet transform from iPPG extracted by the POS method (overall mean absolute error below 2 heart beats per minute). Significance: a framework is provided for theoretical comparison of methods for pulse rate estimation from iPPG; performance of the most popular methods is reported for a publicly available dataset that can be used as a benchmark

Ordinal Patterns, Entropy, and EEG

In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-world data and, especially, of electroencephalogram (EEG) data. We apply already known (empirical permutation entropy, ordinal pattern distributions) and new (empirical conditional entropy of ordinal patterns, robust to noise empirical permutation entropy) methods for measuring complexity, segmentation and classification of time series

Emergence and suppression of cooperation by action visibility in transparent games

Real-world agents, humans as well as animals, observe each other during interactions and choose their own actions taking the partners' ongoing behaviour into account. Yet, classical game theory assumes that players act either strictly sequentially or strictly simultaneously without knowing each other's current choices. To account for action visibility and provide a more realistic model of interactions under time constraints, we introduce a new game-theoretic setting called transparent games, where each player has a certain probability of observing the partner's choice before deciding on its own action. By means of evolutionary simulations, we demonstrate that even a small probability of seeing the partner's choice before one's own decision substantially changes the evolutionary successful strategies. Action visibility enhances cooperation in an iterated coordination game, but reduces cooperation in a more competitive iterated Prisoner's Dilemma. In both games, "Win-stay, lose-shift" and "Tit-for-tat" strategies are predominant for moderate transparency, while a "Leader-Follower" strategy emerges for high transparency. Our results have implications for studies of human and animal social behaviour, especially for the analysis of dyadic and group interactions