摘要：Fear, anxiety, and preference in fish are generally evaluated by video-based behavioural analyses. We previously proposed a system that can measure bioelectrical signals, called ventilatory signals, using a 126-electrode array placed at the bottom of an aquarium and achieved cameraless real-time analysis of motion and ventilation. In this paper, we propose a method to evaluate the emotional state of fish by combining the motion and ventilatory indices obtained with the proposed system. In the experiments, fear/anxiety and appetitive behaviour were induced using alarm pheromone and ethanol, respectively. We also found that the emotional state of the zebrafish can be expressed on the principal component (PC) space extracted from the defined indices. The three emotional states were discriminated using a model-based machine learning method by feeding the PCs. Based on discrimination performed every 5 s, the F-score between the three emotional states were as follows: 0.84 for the normal state, 0.76 for the fear/anxiety state, and 0.59 for the appetitive behaviour. These results indicate the effectiveness of combining physiological and motional indices to discriminate the emotional states of zebrafish.
