A new EEG software that supports emotion recognition by using an autonomous approach
- 51 Downloads
Human behavior is manly addressed by emotions. One of the most accepted models that represent emotions is known as the circumplex model. This model organizes emotions into points on a bidimensional plane: valence and arousal. Despite the importance of the emotion recognition, there are limited initiatives that seek to classify emotions easily in an uncontrolled environment. In this work, we present the architecture and the design of an extensible software which allows recognizing and classifying emotions by using a low-cost EEG. The proposed software implements an emotion classifier although a support vector machines (SVM) are boosted with an autonomous bio-inspired approach. The contribution was experimentally evaluated by taking a set of well-known validated EEG Databases for Emotion Recognition. Computational experiments show promising results. Using our proposal for EEG emotion classification, we reach an accuracy close to 95%. The results obtained confirm that our approach is able to overcome to a commonly used SVM classifier and that the proposed software can be useful in real environments.
KeywordsEmotion recognition Software architecture Support vector machine Autonomous bat algorithm
Carla Taramasco has been supported by CORFO—CENS 16CTTS-66390 through the National Center on Health Information Systems. This work is also supported by the National Commission for Scientific and Technological Research (CONICYT) through the Program STIC-AMSUD 17STIC- 03: “MONITORing for ehealth,” FONDEF ID16I10449 “Sistema inteligente para la gestión y análisis de la dotación de camas en la red asistencial del sector público”, and MEC80170097 “Red de colaboración científica entre universidades nacionales e internacionales para la estructuración del doctorado y magister en informática médica en la Universidad de Valparaíso.” Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1160455. María Francisca Alonso-Sńchez is supported by CONICYT/FONDECYT/INICIACION/11160212. Victor Hugo C. de Albuquerque appreciates the received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6).
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this article.
The data generated to support the findings of this study have been deposited in the Figshare repository (https://figshare.com/s/1a80d472266b39ed2723).
- 4.Oatley K, Keltner D, Jenkins JM (2006) Understanding emotions. Blackwell Publishing, HobokenGoogle Scholar
- 8.Chen M, Han J, Guo L, Wang J, Patras I (2015) Identifying valence and arousal levels via connectivity between EEG channels. In: 2015 international conference on affective computing and intelligent interaction (ACII). IEEE. https://doi.org/10.1109/acii.2015.7344552
- 13.Schunk DH (2013) Motivation in education: theory, research, and applications, 4th edn. Pearson, LondonGoogle Scholar
- 14.Teplan M et al (2002) Fundamentals of EEG measurement. Meas Sci Rev 2(2):1–11Google Scholar
- 15.Luck SJ (2014) An introduction to the event-related potential technique. MIT press, CambridgeGoogle Scholar
- 20.Ali M, Al Machot F, Mosa AH, Kyamakya K (2016) A novel EEG-based emotion recognition approach for e-healthcare applications. In: Proceedings of the 31st annual ACM symposium on applied computing. ACM, pp 162–164Google Scholar
- 26.Munoz R, Villarroel R, Barcelos TS, Souza A, Merino E, Guiez R, Silva LA (2018) Development of a software that supports multimodal learning analytics: a case study on oral presentations. JUCS 24(2):149–170Google Scholar
- 33.Mizobe R, Martins L, Rodrigues D, Pontara K, Papa JP, Yang XS (2013) Binary bat algorithm for feature selection. Swarm Intell Bio-inspired Comput Theory Appl. https://doi.org/10.1016/B978-0-12-405163-8.00009-0 CrossRefGoogle Scholar
- 34.Sylvia A, Rajalaxmi R (2015) Unsupervised feature selection using binary bat algorithm. In: 2nd international conference on electronics and communication systems, pp 451–456. https://doi.org/10.1109/ECS.2015.7124945
- 40.Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys Eng Sci 454(1971):903–995. https://doi.org/10.1098/rspa.1998.0193 MathSciNetCrossRefzbMATHGoogle Scholar
- 41.Butterworth S (1930) On the theory of filter amplifiers. Wirel Eng 7(6):536–541Google Scholar
- 44.Soto R, Crawford B, Carrasco C, Almonacid B, Reyes V, Araya I, Misra S, Olguín E (2016) Solving manufacturing cell design problems by using a dolphin echolocation algorithm. In: Computational science and its applications—ICCSA 2016. Springer, pp 77–86. https://doi.org/10.1007/978-3-319-42092-9_7