Music Generation and Emotion Estimation from EEG Signals for Inducing Affective States

2020 ◽  
Author(s):  
Kana Miyamoto ◽  
Hiroki Tanaka ◽  
Satoshi Nakamura
Keyword(s):  
Affective States ◽  
Eeg Signals ◽  
Music Generation ◽  
Emotion Estimation

scholarly journals Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals

2017 ◽  
Vol 244 ◽  
pp. 81-89 ◽  
Author(s):  
Pablo Arnau-González ◽  
Miguel Arevalillo-Herráez ◽  
Naeem Ramzan
Keyword(s):  
Affective States ◽  
Eeg Signals

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

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 683 ◽  
Author(s):  
Jiahui Cai ◽  
Wei Chen ◽  
Zhong Yin
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Least Square ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Affective States ◽  
Eeg Signals ◽  
Subject Selection ◽  
Automation Systems ◽  
Eeg Data

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.

scholarly journals Multimodal Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression

2020 ◽  
Vol 10 (2) ◽  
pp. 85 ◽  
Author(s):  
Yanjia Sun ◽  
Hasan Ayaz ◽  
Ali N. Akansu
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Brain Activity ◽  
Affective State ◽  
Emotional Valence ◽  
Video Content ◽  
Affective States ◽  
Eeg Signals ◽  
Image Content ◽  
Affective Expressions

Human facial expressions are regarded as a vital indicator of one’s emotion and intention, and even reveal the state of health and wellbeing. Emotional states have been associated with information processing within and between subcortical and cortical areas of the brain, including the amygdala and prefrontal cortex. In this study, we evaluated the relationship between spontaneous human facial affective expressions and multi-modal brain activity measured via non-invasive and wearable sensors: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. The affective states of twelve male participants detected via fNIRS, EEG, and spontaneous facial expressions were investigated in response to both image-content stimuli and video-content stimuli. We propose a method to jointly evaluate fNIRS and EEG signals for affective state detection (emotional valence as positive or negative). Experimental results reveal a strong correlation between spontaneous facial affective expressions and the perceived emotional valence. Moreover, the affective states were estimated by the fNIRS, EEG, and fNIRS + EEG brain activity measurements. We show that the proposed EEG + fNIRS hybrid method outperforms fNIRS-only and EEG-only approaches. Our findings indicate that the dynamic (video-content based) stimuli triggers a larger affective response than the static (image-content based) stimuli. These findings also suggest joint utilization of facial expression and wearable neuroimaging, fNIRS, and EEG, for improved emotional analysis and affective brain–computer interface applications.

A nonlinear dynamical approach to analysis of emotions using EEG signals based on the Poincaré map function and recurrence plots

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammad Bagher Khodabakhshi ◽  
Valiallah Saba
Keyword(s):  
Poincaré Map ◽  
Statistical Significance ◽  
Emotional States ◽  
Poincare Map ◽  
Affective States ◽  
Recurrence Plots ◽  
Eeg Signals ◽  
Transient Time ◽  
Significant Information ◽  
Map Function

AbstractDynamic variations of electroencephalogram (EEG) contain significant information in the study of human emotional states. Transient time methods are well suited to evaluate short-term dynamic changes in brain activity. Human affective states, however, can be more appropriately analyzed using chaotic dynamical techniques, in which temporal variations are considered over longer durations. In this study, we have applied two different recurrence-based chaotic schemes, namely the Poincaré map function and recurrence plots (RPs), to analyze the long-term dynamics of EEG signals associated with state space (SS) trajectory of the time series. Both approaches determine the system dynamics based on the Poincaré recurrence theorem as well as the trajectory divergence producing two-dimensional (2D) characteristic plots. The performance of the methods is compared with regard to their ability to distinguish between levels of valence, arousal, dominance and liking using EEG data from the “dataset for emotion analysis using physiological” database. The differences between the levels of emotional feelings were investigated based on the analysis of variance (ANOVA) test and Spearman’s statistics. The results obtained from the RP features distinguish between the emotional ratings with a higher level of statistical significance as compared with those produced by the Poincaré map function. The scheme based on RPs was particularly advantageous in identifying the levels of dominance. Out of the 32 EEG electrodes examined, the RP-based approach distinguished the dominance levels in 23 electrodes, while the approach based on the Poincaré map function was only able to discriminate dominance levels in five electrodes. Furthermore, based on nonlinear analysis, significant correlations were observed over a wider area of the cortex for all affective states as compared with that reported based on the analysis of EEG power bands.

scholarly journals A tEEG framework for studying designer’s cognitive and affective states

2020 ◽  
Vol 6 ◽  
Author(s):  
Mengting Zhao ◽  
Wenjun Jia ◽  
Daocheng Yang ◽  
Philon Nguyen ◽  
Thanh An Nguyen ◽  
...  
Keyword(s):  
Design Process ◽  
Current Literature ◽  
Wide Spectrum ◽  
Basic Design ◽  
Affective States ◽  
Controlled Experiments ◽  
Eeg Signals ◽  
Complex Design ◽  
Design Activities ◽  
Cognitive Activities

Abstract This paper proposes a task-related electroencephalogram research framework (tEEG framework) to guide scholars’ research on EEG-based cognitive and affective studies in the context of design. The proposed tEEG framework aims to investigate design activities with loosely controlled experiments and decompose a complex design process into multiple primitive cognitive activities, corresponding to which different research hypotheses on basic design activities can be effectively formulated and tested. Thereafter, existing EEG techniques and methods can be applied to analyse EEG signals related to design. Three application examples are presented at the end of this paper to demonstrate how the proposed framework can be applied to analyse design activities. The tEEG framework is presented to guide EEG-based cognitive and affective studies in the context of design. Existing methods and models are summarized, for the effective application of the tEEG framework, from the current literature spread in a wide spectrum of resources and fields.

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