Add to ORKGAbstract — This paper presents characterization of affect (valence and arousal) using the Magnetoencephalogram (MEG) brain signal. We attempt single-trial classification of movie and music videos with MEG responses extracted from seven participants. The main findings of this study are that: (i) the MEG signal effectively encodes affective viewer responses, (ii) clip arousal is better predicted than valence employing MEG and (iii) prediction performance is better for movie clips as compared to music videos. I
Abstract—We present a multimodal data set for the analysis of human affective states. The electroenc...
In this paper, we propose an approach for affective representation of movie scenes based on the emot...
Assessing emotional states of users evoked during their multimedia consumption has received a great ...
This paper presents characterization of affect (valence and arousal) using the Magnetoencephalogram ...
Abstract—This paper presents a new multimodal database and the associated results for characterizati...
Genre classification is an essential part of multimedia content recommender systems. In this study, ...
This paper presents a user-independent emotion recognition method with the goal of recovering affect...
Recently, the field of automatic recognition of users' affective states has gained a great deal of a...
In this work, we present DECAF-a multimodal data set for decoding user physiological responses to af...
This paper presents a subject-dependent homogenous emotion recognition method using electroencephal...
This paper presents a user-independent emotion recognition method with the goal of recovering affect...
Valence-arousal evaluation using physiological signals in an emotion recall paradigm CHANEL, Guillau...
This study explored the feasibility of using shared neural patterns from brief affective episodes (v...
In this paper, we propose an approach for affective characterization of movie scenes based on the em...
Abstract—We present a multimodal dataset for the analysis of human affective states. The electroence...
Abstract—We present a multimodal data set for the analysis of human affective states. The electroenc...
In this paper, we propose an approach for affective representation of movie scenes based on the emot...
Assessing emotional states of users evoked during their multimedia consumption has received a great ...
This paper presents characterization of affect (valence and arousal) using the Magnetoencephalogram ...
Abstract—This paper presents a new multimodal database and the associated results for characterizati...
Genre classification is an essential part of multimedia content recommender systems. In this study, ...
This paper presents a user-independent emotion recognition method with the goal of recovering affect...
Recently, the field of automatic recognition of users' affective states has gained a great deal of a...
In this work, we present DECAF-a multimodal data set for decoding user physiological responses to af...
This paper presents a subject-dependent homogenous emotion recognition method using electroencephal...
This paper presents a user-independent emotion recognition method with the goal of recovering affect...
Valence-arousal evaluation using physiological signals in an emotion recall paradigm CHANEL, Guillau...
This study explored the feasibility of using shared neural patterns from brief affective episodes (v...
In this paper, we propose an approach for affective characterization of movie scenes based on the em...
Abstract—We present a multimodal dataset for the analysis of human affective states. The electroence...
Abstract—We present a multimodal data set for the analysis of human affective states. The electroenc...
In this paper, we propose an approach for affective representation of movie scenes based on the emot...
Assessing emotional states of users evoked during their multimedia consumption has received a great ...