Emotion Recognition from EEG Using RASM and LSTM

et al. 2018

Self Cite

657

352

Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

Section: Methodsmentioning

confidence: 99%

“…A new framework which consists of a linear EEG mixing model and an emotion timing model was proposed ( Figure 6 b) [ 27 ]. Specially, the context correlations of the EEG feature sequences were used to improve the recognition accuracy.…”

Section: Emotional Relevant Features Of Physiological Signalsmentioning

confidence: 99%

A Review of Emotion Recognition Using Physiological Signals

Shu

Xie

et al. 2018

Self Cite

657

352

“…Li et al [20] developed an RNN-based emotion recognition model. The model considers three characteristics of EEG signals: Temporal, spatial and frequency.…”

Section: Rnn-based Modelsmentioning

confidence: 99%

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Han

Min

2019

Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell’s emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.

“…Since EEG signals are time-sequential, many researchers have employed RNN to recognize emotion from the signals. Li et al [21] proposed emotion recognizer based on RNN structure. They focused on three types of properties of EEG signals: frequency, spatial and temporal.…”

Section: Deep Learning-based Modelsmentioning

confidence: 99%

Emotion Variation from Controlling Contrast of Visual Contents through EEG-Based Deep Emotion Recognition

Han

Min

2020

Visual contents such as movies and animation evoke various human emotions. We examine an argument that the emotion from the visual contents may vary according to the contrast control of the scenes contained in the contents. We sample three emotions including positive, neutral and negative to prove our argument. We also sample several scenes of these emotions from visual contents and control the contrast of the scenes. We manipulate the contrast of the scenes and measure the change of valence and arousal from human participants who watch the contents using a deep emotion recognition module based on electroencephalography (EEG) signals. As a result, we conclude that the enhancement of contrast induces the increase of valence, while the reduction of contrast induces the decrease. Meanwhile, the contrast control affects arousal on a very minute scale.