Abstract
Human computer interface (HCI) has become more and more important in the last few years. This is mainly due to the increase in the technology and in the new possibilities in yielding a help to disabled people. Brain Computer Interfaces (BCI) represent a subset of the HCI systems which use measurements of the voluntary brain activity for driving a communication system mainly useful for severely disabled people. Electroencephalography (EEG) has been intensively used for the measurement of electrical signals related to the brain activity. The BCI usage requires the activation of mental tasks that could be derived by external stimulations (often audio-visual) or by autonomous activations (for example by thinking to move an arm for signaling a binary command). In the last few years, a new paradigm of activation has been used, consisting in the autonomous brain activation through self-induced emotions, remembered on autobiographical basis. In the present paper, we describe the state of the art of a BCI system based on self-induced emotions, from the activation paradigm to the used signal classification strategies and the final graphic interface. Moreover, we will discuss its extension toward a multi-emotional paradigm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdulkader, S.N., Ata, A., Mostafa, M.S.M.: Brain computer interfacing: applications and challenges. Egypt. Inform. J. 16, 213–230 (2015)
Avola, D., Spezialetti, M., Placidi, G.: Design of an efficient framework for fast prototyping of customized human–computer interfaces and virtual environments for rehabilitation. Comput. Methods Programs Biomed. 110(3), 490–502 (2013)
Basso Moro, S., Bisconti, S., Muthalib, M., Spezialetti, M., Cutini, S., Ferrari, M., Placidi, G., Quaresima, V.: A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study. Neuroimage 85, 451–460 (2014)
Basso Moro, S., Carrieri, M., Avola, D., Brigadoi, S., Lancia, S., Petracca, A., Spezialetti, M., Ferrari, M., Placidi, G., Quaresima, V.: A novel semi-immersive virtual reality visuo-motor task activates ventrolateral prefrontal cortex: a functional near-infrared spectroscopy study. J. Neural Eng. 13(3), 1–14 (2016)
Carrieri, M., Petracca, A., Lancia, S., Basso Moro, S., Brigadoi, S., Spezialetti, M., Ferrari, M., Placidi, G., Quaresima, V.: Prefrontal cortex activation upon a demanding virtual hand-controlled task: a new frontier for neuroergonomics. Feontiers Hum. Neurosci. 10, 1–13 (2016)
Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Schalk, G., Oriolo, G., Cherubini, A., Marciani, M., Babiloni, F.: Non-invasive brain–computer interface system: towards its application as assistive technology. Brain Res. Bull. 75, 796–803 (2008)
De Santis, A., Iacoviello, D.: Optimal segmentation of pupillometric images for estimating pupil shape parameters. Comput. Methods Programs Biomed. 84, 174–187 (2006)
De Santis, A., Iacoviello, D.: Robust real time eye tracking for computer interface for disables people. Comput. Methods Programs Biomed. 96, 1–11 (2009)
Ferrari, M., Bisconti, S., Spezialetti, M., Basso Moro, S., Di Palo, C., Placidi, G., Quaresima, V.: Prefrontal cortex activated bilaterally by a tilt board balance task: a functional near-infrared spectroscopy study in a semi-immersive virtual reality environment. Brain Topogr. 27(3), 353–365 (2014)
Iacoviello, D., Lucchetti, M.: Parametric characterization of the form of the human pupil from blurred noisy images. Comput. Methods Programs Biomed. 77, 39–48 (2005)
Iacoviello, D., Petracca, A., Spezialetti, M., Placidi, G.: A real-time classification algorithm for EEG-based BCI driven by self-induced emotions. Comput. Methods Programs Biomed. 122, 293–303 (2015a)
Iacoviello, D., Petracca, A., Spezialetti, M., Placidi, G.: A classification algorithm for electroencephalography signals by self-induced emotional stimuli. IEEE Trans. Cybern. 46(12), 3171–3180 (2015b)
Iacoviello, D., Pagnani, N., Petracca, A., Spezialetti, M., Placidi, G.: A poll oriented classifier for affective brain computer interfaces. In: NEUROTECHNIX, Lisbon, pp. 978–989 (2015c)
Miranda, R.A., Casebeer, W.D., Hein, A.M., Judy, J.W., Krotkov, E.P., Laabs, T.L., Manzo, J.E., Pankratz, K.G., Pratt, G.A., Sanchez, J.C., Weber, D.J., Wheeler, T.L., Ling, G.S.: DARPA-funded efforts in the development of novel brain–computer interface technologies. J. Neurosci. Methods 244, 52–67 (2015)
Muhl, C., Allison, B., Nijholt, A., Chanel, G.: A survey of affective brain computer interfaces: principles, state of the art, and challenges. Brain Comput. Interface 1(2) (2014a)
Mühl, C., Heylen, D., Nijholt, A.: Affective brain-computer interfaces: neuroscientific approaches to affect detection. In: Calvo, R.A., D’Mello, S.K., Gratch, A.K.J. (eds.) Handbook of Affective Computing. Oxford University Press (2014b)
Niedermeyer, E., Lopes da Silva, F.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, Philadelphia (2005)
Pistoia, F., Carolei, A., Iacoviello, D., Petracca, A., Sacco, S., Sarà, M., Spezialetti, M., Placidi, G.: EEG-detected olfactory imagery to reveal covert consciousness in minimally conscious state. Brain Inj. 29, 1729–1735 (2015)
Placidi, G., Avola, D., Ferrari, M., Iacoviello, D., Petracca, A., Quaresima, V., Spezialetti, M.: A low-cost real time virtual system for postural stability assessment at home. Comput. Methods Programs Biomed. 117(2), 322–333 (2014)
Placidi, G., Avola, D., Petracca, A., Sgallari, F., Spezialetti, M.: Basis for the implementation of an EEG-based single-trial binary brain computer interface through the disgust produced by remembering unpleasant odors. Neurocomputing 160, 308–318 (2015a)
Placidi, G., Petracca, A., Spezialetti, M., Iacoviello, D.: Classification strategies for a single-trial binary Brain Computer Interface based on remembering unpleasant odors. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7019–7022 (2015b)
Placidi, G., Di Giamberardino, P., Petracca, A., Spezialetti, M., Iacoviello, D.: Classification of Emotional Signals from the DEAP dataset: included in registration, pp. 15–21. NEUROTECHNIX, Porto (2016a)
Placidi, G., Petracca, A., Spezialetti, M., Iacoviello, D.: A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People. J. Med. Syst. 40(34), 1–14 (2016b)
Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)
Wolpaw, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., Vaughan, T.: Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002)
Acknowledgments
This research has been supported by the “Progetto di Ateneo 2015”: “A classification algorithm of EEG signals: from self- induced emotions to human machine interface” (C26A15Z7N2) of “Sapienza” University of Rome.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Di Giamberardino, P., Iacoviello, D., Placidi, G., Polsinelli, M., Spezialetti, M. (2018). A Brain Computer Interface by EEG Signals from Self-induced Emotions. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_77
Download citation
DOI: https://doi.org/10.1007/978-3-319-68195-5_77
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68194-8
Online ISBN: 978-3-319-68195-5
eBook Packages: EngineeringEngineering (R0)