Emotion recognition from users' EEG signals with the help of stimulus VIDEOS

Zhu, Yachen; Wang, Shangfei; Ji, Qiang

doi:10.1109/icme.2014.6890161

Cited by 22 publications

(13 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• It's the first time to obtain video features as privileged information for emotion recognition from EEG [43], and use EEG features as privileged information for video emotion tagging. We regard the proposed method as implicit fusion, which use multiple modalities only during training, not testing.…”

Section: Video Emotional Taggingmentioning

confidence: 99%

Emotion Recognition with the Help of Privileged Information

Wang

Zhu

Yue

et al. 2015

IEEE Trans. Auton. Mental Dev.

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a novel approach to recognize emotions with the help of privileged information, which is only available during training, but not available during testing. Such additional information can be exploited during training to construct a better classifier. Specifically, we recognize audience's emotion from EEG signals with the help of the stimulus videos, and tag videos' emotions with the aid of EEG signals. Firstly, frequency features are extracted from EEG signals and audio/visual features are extracted from video stimulus. Secondly, features are selected by statistical tests. Thirdly, a new EEG feature space and a new video feature space are constructed simultaneously using Canonical Correlation Analysis. Finally, two Support Vector Machines (SVM) are trained on the new EEG and video feature spaces respectively. During emotion recognition from EEG, only EEG signals are available, and the SVM classifier obtained on EEG feature space is used; while for video emotion tagging, only video clips are available, and the SVM classifier constructed on video feature space is adopted. Experiments of EEG-based emotion recognition and emotion video tagging are conducted on three benchmark databases, demonstrating that video content, as the context, can improve the emotion recognition from EEG signals and EEG signals available during training can enhance emotion video tagging.

show abstract

Section: Video Emotional Taggingmentioning

confidence: 99%

Emotion Recognition with the Help of Privileged Information

Wang

Zhu

Yue

et al. 2015

IEEE Trans. Auton. Mental Dev.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Certainly, less number of participants could lead to unbalanced results (see Table 5). Based on survey, EEG based emotion databases, such as SEED [192] and MAHNOB HCI [95], have been constructed using 15 and 30 subjects respectively. However, the widely used database DEAP [37] has been created using 32 subjects, showcasing the reliability of the data.…”

Section: ) Subject Informationmentioning

confidence: 99%

Review on Stimuli Presentation for Affect Analysis Based on EEG

Sarma

Barma

2020

IEEE Access

View full text Add to dashboard Cite

This work presents a comprehensive review on stimuli presentation, which is an important stage of any emotion elicitation experiment in affect analysis. Due to lack of standard guidelines, the researchers employ their self-devised methods which are not always sufficiently informative-making this area very inconsistent and ambiguous. In addition, an ample study about this stage including how to select, design and present the stimuli has not been reported properly earlier. In this purpose, an inclusive study has been conducted aiming to summarize various aspects of stimuli presentation including type of stimuli, available database, presentation tools, subjective measures, ethical issues and so on. Certainly, among several methods of emotion recognition (e.g., facial expression, speech, gesture and physiological signal), the EEG based emotion recognition works have been considered here due to availability of sufficient number of works, reliability and well-established technology. In total, 137 peer reviewed articles have been studied and the results show that about 83% of emotion elicitations have been performed by employing visual stimuli (mostly pictures and video). Therefore, presentation of visual stimuli has been explored with great emphasis covering laboratory setup, presentation timing, subjective issues, and ethical issues. Finally, an extensive recommendations regarding stimuli presentation has been provided which could guide to conduct the emotion elicitation experiments effectively.

show abstract

“…By using a fast Fourier transformation, the frequency features (60 power features, 16 power difference features) were prepared. In each channel, the power features were computed on four frequency bands, i.e., theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Power difference features were employed to detect the variation in cerebral activity between the left and right cortical areas.…”

Section: Feature Extraction and The Target Emotion Classesmentioning

confidence: 99%

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

2019

View full text Add to dashboard Cite

Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.

show abstract

Emotion recognition from users' EEG signals with the help of stimulus VIDEOS

Cited by 22 publications

References 9 publications

Emotion Recognition with the Help of Privileged Information

Emotion Recognition with the Help of Privileged Information

Review on Stimuli Presentation for Affect Analysis Based on EEG

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

Contact Info

Product

Resources

About