Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

This paper proposes an emotion elicitation method to develop our Stock-Emotion dataset: a collection of the participants' electroencephalogram (EEG) signals who paper-traded using real stock market data, virtual money, and outcomes that emotionally affected them. A system for emotion recognition using this dataset was tested. The system extracted from the EEG signals the following features: five frequency bands, Differential Entropy (DE), Differential Asymmetry (DASM), and Rational Asymmetry (RASM), for each band. Our system then carried out feature selection using a filter method (Mutual Information Matrix), combined with a wrapper process (Chi-Square statistics) and alternatively using the embedded algorithms in a Deep Learning classifier. Finally, this work classified emotions in four quadrants of the circumplex model using Random Forest and Deep Learning algorithms. Our findings show that 1) the proposed emotion elicitation method is useful to provoke affective states associated with trading, 2) the proposed feature selection process improved the classification performance of our emotion recognition system, and 3) classifier performance of the system can recognize trading related emotions and has results comparable with the state of the art research corresponding to a similar number of output classes.

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Under certain driving conditions of a single-axis acoustic levitation device, a suspended sample leaves its stability state and starts to oscillate vertically around the initial equilibrium position. A published theory on such instabilities [J. Rudnick and M. Barmatz, "Oscillational instabilities in single-mode acoustic levitators," J. Acoust. Soc. Am., 87(1), 81-92, 1990] predicts the occurrence of time delays between the response of the cavity of the device and the motion of the sample inside it. In this paper, a theoretical and experimental investigation on similar time delay effects will be described. A solid sphere was moved in a controlled way inside a closed cylindrical cavity by means of a rod connecting the object to the outside of the system. A standing wave was generated inside the cavity by using a speaker. In this way, oscillations of the sphere were produced and the response of the sound field to such movement was studied. The effect of the frequency of the oscillations of the sphere on the time delay between the sound pressure and the movement of that object will be reported. In addition, the relations between the obtained results and the published theory on oscillational instabilities will be discussed.

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