Análisis del etiquetado emocional de videos educativos

La publicación y el uso de los videos educativos, tanto en el aprendizaje formal como informal se ha incrementado en los últimos años. Una investigación previa permitió dar cuenta del creciente interés en la comunidad científica en el uso de emociones para potenciar los sistemas de e-learning; así como, identificar una falta de estándares para el meta-anotado emocional de videos educativos. En este artículo, se presenta un primer análisis de un conjunto de bases de datos que alojan videos etiquetados emocionalmente, revisando su meta-anotación, específicamente con relación a las emociones. Se propone un conjunto de elementos que podrían formar parte de un proceso de meta-anotación para etiquetar emocionalmente videos educativos.Facultad de Informátic

Towards EEG-based BCI driven by emotions for addressing BCI-Illiteracy: a meta-analytic review

Many critical aspects affect the correct operation of a Brain Computer Interface. The term BCI-illiteracy' describes the impossibility of using a BCI paradigm. At present, a universal solution does not exist and seeking innovative protocols to drive a BCI is mandatory. This work presents a meta-analytic review on recent advances in emotions recognition with the perspective of using emotions as voluntary, stimulus-independent, commands for BCIs. 60 papers, based on electroencephalography measurements, were selected to evaluate what emotions have been most recognised and what brain regions were activated by them. It was found that happiness, sadness, anger and calm were the most recognised emotions. Relevant discriminant locations for emotions recognition and for the particular case of discrete emotions recognition were identified in the temporal, frontal and parietal areas. The meta-analysis was mainly performed on stimulus-elicited emotions, due to the limited amount of literature about self-induced emotions. The obtained results represent a good starting point for the development of BCI driven by emotions and allow to: (1) ascertain that emotions are measurable and recognisable one from another (2) select a subset of most recognisable emotions and the corresponding active brain regions

Implicit Interaction with Textual Information using Physiological Signals

Implicit interaction refers to human-computer interaction techniques that do not require active engagement from the users. Instead, the user is passively monitored while performing a computer task, and the data gathered is used to infer implicit measures as inputs to the system. Among the multiple applications for implicit interaction, collecting user feedback on information content is one that has increasingly been investigated. As the amount of available information increases, traditional methods that rely on the users' explicit input become less feasible. As measurement devices become less intrusive, physiological signals arise as a valid approach for generating implicit measures when users interact with information. These signals have mostly been investigated in response to audio-visual content, while it is still unclear how to use physiological signals for implicit interaction with textual information. This dissertation contributes to the body of knowledge by studying physiological signals for implicit interaction with textual information. The research targets three main research areas: a) physiology for implicit relevance measures, b) physiology for implicit affect measures, and c) physiology for real-time implicit interaction. Together, these provide understanding not only on what type of implicit measures can be extracted from physiological signals from users interacting with textual information, but also on how these can be used in real time as part of fully integrated interactive information systems. The first research area targets perceived relevance, as the most noteworthy underlying property regarding the user interaction with information items. Two experimental studies are presented that evaluate the potential for brain activity, electrodermal activity, and facial muscle activity as candidate measures to infer relevance from textual information. The second research area targets affective reactions of the users. The thesis presents two experimental studies that target brain activity, electrodermal activity, and cardiovascular activity to indicate users' affective responses to textual information. The third research area focuses on demonstrating how these measures can be used in a closed interactive loop. The dissertation reports on two systems that use physiological signals to generate implicit measures that capture the user's responses to textual information. The systems demonstrate real-time generation of implicit physiological measures, as well as information recommendation on the basis of implicit physiological measures. This thesis advances the understanding of how physiological signals can be implemented for implicit interaction in information systems. The work calls for researchers and practitioners to consider the use of physiological signals as implicit inputs for improved information delivery and personalization.Implisiittinen vuorovaikutus viittaa ihmisen ja tietokoneen välisen vuorovaikutuksen tekniikoihin, jotka eivät vaadi käyttäjän tarkkaavaisuutta. Tämän sijaan järjestelmä kerää käyttäjästä tietoja passiivisesti ja käyttää näitä tietoja operatiivisina syötteinä. Esimerkiksi viestiä kirjoitettaessa (eksplisiittinen vuorovaikutus) järjestelmä tunnistaa tekemämme kirjoitusvirheen ja automaattisesti korjaa väärin kirjoitetun sanan (implisiittinen vuorovaikutus). Implisiittinen vuorovaikutus mahdollistaa näin uusia vuorovaikutuskanavia vaivaamatta lainkaan käyttäjää. Mittauslaitteiden kehityksen myötä implisiittisessä vuorovaikutuksessa voidaan hyödyntää myös fysiologisia signaaleja, kuten aivovasteita ja kardiovaskulaarisia reaktioita. Näiden signaalien analyysi paljastaa tietoja käyttäjän kiinnostuksen kohteista ja tunteista suhteessa tietokoneen esittämään sisältöön, ja tarjoaa näin järjestelmälle paremmat mahdollisuudet vastata käyttäjän tarpeisiin. Väitöskirjani tarkoituksena on tutkia käyttäjien fysiologisia signaaleja sekä kerätä tietoa heidän reaktioistaan ja mielipiteistään suhteessa tekstipohjaiseen informaatioon ja käyttää näitä signaaleja ja tietoja implisiittisen vuorovaikutuksen mahdollistamiseksi. Tarkkaan ottaen tarkoituksenani on tutkia a) fysiologisten signaalien kykyä kertoa siitä, miten kiinnostavana käyttäjä kokee lukemansa tekstin, b) fysiologisten signaalinen käyttökelpoisuutta ennustamaan, minkälaisia tunnereaktiota (esim. huvittuneisuutta) tekstit herättävät lukijassa sekä, c) fysiologisen signaalien käyttökelpoisuutta reaaliaikaisessa implisiittisessä vuorovaikutuksessa. Tutkimuksen tulokset osoittavat, että fysiologiset signaalit tarjoavat toimivan ratkaisun reaaliaikaiseen implisiittiseen vuorovaikutukseen tekstipohjaisten sisältöjen parissa. Tutkimuksen löydösten pääviesti tutkimusyhteisölle ja alan ammattilaisille on se, että implisiittisinä syötteinä fysiologiset signaalit helpottavat informaation kulkua ja parantavat personalisoimista ihmisen ja tietokoneen välisessä vuorovaikutuksessa

Proceedings of the X Iberoamerican Conference on Applications and Usability of Interactive TV jAUTI2021

The X Ibero-American Conference on Applications and Usability of TVDI jAUTI 2021 is an organization of the Department of Electricity, Electronics and Telecommunications and the WiCOM-Energy Research Group of the University of the Armed Forces ESPE together with RedAUTI (Thematic Network on Applications and Usability of Interactive Digital Television). This year's edition was held from December 2 to 3, 2021 in the city of Sangolquí, Ecuador, taking place online. This book brings together 18 works presented on the design, development and experiences of applications for interactive digital television and related technologies (IPTV, Smart TV, Connected TV, and Web TV).La X Conferencia Iberoamericana de Aplicaciones y Usabilidad de la TVDI jAUTI 2021 es una organización del Departamento de Electricidad, Electrónica y Telecomunicaciones y el Grupo de Investigación WiCOM-Energy de la Universidad de las Fuerzas Armadas ESPE junto con la RedAUTI (Red temática en Aplicaciones y Usabilidad de Televisión Digital Interactiva). La edición de este año se realizó del 2 al 3 de diciembre de 2021 en la ciudad de Sangolquí, Ecuador, llevándose a cabo en modalidad online. Este libro reúne 18 trabajos presentados sobre el diseño, desarrollo y experiencias sobre aplicaciones para televisión digital interactiva y tecnologías relacionadas (IPTV, Smart TV, Connected TV, and Web TV).A X Conferência Ibero-Americana de Aplicações e Usabilidade da TVDI jAUTI 2021 é uma organização do Departamento de Eletricidade, Eletrônica e Telecomunicações e do Grupo de Pesquisa WiCOM-Energy da Universidade das Forças Armadas ESPE juntamente com a RedAUTI (Rede Temática sobre Aplicações e Usabilidade da Televisão Digital Interativa). A edição deste ano foi realizada de 2 a 3 de dezembro de 2021 na cidade de Sangolquí, Equador, online. Este livro reúne 18 trabalhos apresentados sobre design, desenvolvimento e experiências em aplicativos para televisão digital interativa e tecnologias relacionadas (IPTV, Smart TV, Connected TV e Web TV).RedAUT

Multimedia implicit tagging using EEG signals

Electroencephalogram (EEG) signals reflect brain activities associated with emotional and cognitive processes. In this paper, we demonstrate how they can be used to find tags for multimedia content without users' direct input. Alternative methods for multimedia tagging is attracting increasing interest from multimedia community. The new portable EEG helmets are paving the way for employing brain waves in human computer interaction. In this paper, we demonstrate the performance of EEG for tagging purposes using two different scenarios on MAHNOB-HCI database. First, an emotional tagging and classification using a reduced set of electrodes is presented. The emotional responses of 24 participants to short video clips are classified into three classes on arousal and valence. We show how a reduced set of electrodes based on previous studies can preserve and even enhance the emotional classification rate. We then demonstrate the feasibility of using EEG signals for tag relevance tasks. A set of images were shown to participants first, without any tag and then with a relevant or irrelevant tag. The relevance of the tag was assessed based on the EEG responses of the participants in the first second after the tag was depicted. Finally, we demonstrate that by aggregating multiple participants' responses we can significantly improve the tagging accuracy