Abstract
Since emotions affect physical and psychologically the health of people, their identification is crucial for understanding human behavior. Despite the several systems developed in this regard, most of them underperform on people with disabilities, their setup is sensitive to noise or non-emotional stimuli. Recent studies consider electroencephalographic (EEG) signals for understanding emotional responses due to reflecting the activity of the central nervous system. However, the non-stationary nature of EEG signals demand elaborated signal processing approaches because not all time instants hold information related to the stimulus-response. This work proposes a temporal analysis approach, termed MILRES, based on the Multi-Instance Learning framework that includes a multiple instance Regularization with LASSO penalty and an Embedded instance Selection. We test MILRES in discriminating two states (high and low) of the valence and arousal emotional dimensions from the DEAP dataset. The proposed approach reaches \(84.4\%\) accuracy and \(79.5\%\) F1-score for valence, and \(81.9\%\) accuracy \(67.9\%\) for arousal. Such results evidence that MILRES outperforms other EEG-based emotion recognition approaches from the state-of-the-art, with the additional benefit of identifying the brain areas involved in processing emotions.
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Acknowledgment
This work is developed within the framework of the research project “programa reconstrucción del tejido social en zonas de pos-conflicto en Colombia del proyecto Fortalecimiento docente desde la alfabetización mediática Informacional y la CTel, como estrategia didáctico-pedagógica y soporte para la recuperación de la confianza del tejido social afectado por el conflicto, Código SIGP 58950” funded by “Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación, Fondo Francisco José de Caldas con contrato No 213-2018 con Código 58960”.
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Caicedo-Acosta, J., Cárdenas-Peña, D., Collazos-Huertas, D., Padilla-Buritica, J.I., Castaño-Duque, G., Castellanos-Dominguez, G. (2019). Multiple-Instance Lasso Regularization via Embedded Instance Selection for Emotion Recognition. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_25
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