Abstract
This paper proposes a methodology to perform emotional states classification by the analysis of EEG signals, wavelet decomposition and an electrode discrimination process, that associates electrodes of a 10/20 model to Brodmann regions and reduce computational burden. The classification process were performed by a Support Vector Machines Classification process, achieving a 81.46 percent of classification rate for a multi-class problem and the emotions modeling are based in an adjusted space from the Russell Arousal Valence Space and the Geneva model.
Highlights
The development of technologies that allow interaction between a user and a computer in a more natural way, has been one of biggest challenges in recent decades.Since Rosalind Picard founded the Affective Computing Group at MIT and propose theories to establish a better understanding of the impact of technology on the emotional states [1], a wide range of important developments focused in the human-machine interaction have been developed; being one of the most relevant the analysis of emotional states, due the great importance of emotions in our daily communication.To date many techniques to analyzing the physiological expressions of an emotion have been developed; most of them are susceptible to be manipulated by users and provide unreliable information
Previous efforts to perform emotional states recognition, has been reached up to 80% of classification rates (Table I), promising results if we consider that a person can recognize an emotional state from another only the 88% of the time
Such a setting is quite sensitive to the possible presence of outliers, as propose to use it instead the following prescription for regularization parameter: C = max(|y + 3σy|, |y − 3σy|), (16). This configuration shows the performance obtained under distinct stimulus over all the participants, and to ensure that each of this arrangements contains the information of different experimentation trials constructed under the following configuration: where yis the mean of the training responses, and σ y is the standard deviation of the training response values
Summary
The development of technologies that allow interaction between a user and a computer in a more natural way, has been one of biggest challenges in recent decades. To date many techniques to analyzing the physiological expressions of an emotion have been developed; most of them are susceptible to be manipulated by users and provide unreliable information. One of the main proposals to resolve this problems, are the analysis of bio-medical signals (biosignals), such as heart rate, breathing rate and the behavior of neural signals that properly processed can be an important and reliable information source [2]; being the brain signal analysis, one of the techniques that has gained an increased demand in the past decades, due the recent developments in Brain Computer Interfaces (BCI), signal processing and pattern recognition algorithms facilitates the analysis, development and implementation of affective technologies.
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