Unsupervised Learning of Brain State Dynamics during Emotion Imagery using High-Density EEG

Here we assume that emotional states correspond to functional dynamic states of brain and body, and attempt to characterize the appearance of these states in high-density scalp electroencephalographic (EEG) recordings acquired from 31 participants during 1-2 hour sessions, each including fifteen 3-5 min periods of self-induced emotion imagination using the method of guided imagery. EEG offers an objective and high-resolution measurement of whatever portion of cortical electrical dynamics is resolvable from scalp recordings. Despite preliminary progress in EEG-based emotion decoding using supervised machine learning methods, few studies have applied data-driven, unsupervised decomposition approaches to investigate the underlying EEG dynamics by characterizing brain temporal dynamics during emotional experience. This study applies an unsupervised approach – adaptive mixture independent component analysis (adaptive mixture ICA, AMICA) that learns a set of ICA models each accounted for portions of a given multi-channel EEG recording. We demonstrate that 20-model AMICA decomposition can identify distinct EEG patterns or dynamic states active during each of the fifteen emotion-imagery periods. The transition in EEG patterns revealed the time-courses of brain-state dynamics during emotional imagery. These time-courses varied across emotions: “grief” and “happiness” showed more abrupt transitions while “contentment” was nearly indistinguishable from the preceding rest period. The spatial distributions of independent components (ICs) of the AMICA models showed higher similarity within-subject across emotions than within-emotion across subjects. No significant differences in IC distributions were found between positive and negative emotions. However, significant changes in IC distributions during emotional imagery compared to rest were identified in brain areas such as the left prefrontal cortex, the posterior cingulate cortex, the motor cortex, and the visual cortex. The study demonstrates the feasibility of AMICA in modeling high-density and nonstationary EEG and its utility in providing data-driven insights into brain state dynamics during self-paced emotional experiences, which have been difficult to measure. This approach can advance our understanding of highly dynamical emotional processes and improve the performance of EEG-based emotion decoding for affective computing and human-computer interaction.

The influence of mascot’s modeling characteristics on emotional image

We intends to investigate the impact of the characteristics of mascots modelling on the emotional imagery based on the theory of perceptual engineering. Specifically, we used the method of semantic difference image cognitive analysis on representative samples by virtue of SPSS software, which completes principal component analysis and regression analysis, typical vocabulary screening, as well as extraction of projects and category of the project modelling using morphological analysis in combination with the theory of science I analysis the correlation coefficient. Eventually, we will translate user’s perceptual demand description into rational modelling characteristics, so as to acquire a better understanding of the correlation between mascots modelling characteristic and emotional image, all of which provide certain reference basis for designers.

Kansei Engineering as a Tool for the Design of Traditional Pattern

Traditional patterns are widely used in the modern design due to their long history, rich connotation, and beautiful form. However, the current application of traditional patterns in the modern design is mostly based on the designer’s subjective preferences, not from the perspective of consumers, to explore their feelings about traditional patterns, and which design factors have an impact on consumers, which is the main reason why modern applications of traditional patterns cannot meet the esthetic needs of modern consumers. Therefore, to make better inheritance of the traditional pattern and meet the needs of contemporary consumers, this article takes the caisson lotus pattern of Mogao Cave in the Tang dynasty as an example and first, using the theory of Kansei engineering to investigate the perceptual cognition of the young consumers aged 20–35 years old on the lotus pattern, then use SPSS 24.0 software to analyze the perceptual evaluation data, find the design element combination code corresponding to the perceptual vocabulary, and establish a mathematical model that can predict consumers’ emotional imagery of the lotus pattern of the caisson in the Tang dynasty. Through the verification of the model, the test results show that the model has a high degree of credibility; designers can use this model to quickly evaluate and redesign the lotus pattern to better meet the needs of modern consumers. At the same time, the method of this paper can also be applied to other design fields with user-centered concerns.

Investigating EEG Patterns for Dual-Stimuli Induced Human Fear Emotional State

Most electroencephalography (EEG) based emotion recognition systems make use of videos and images as stimuli. Few used sounds, and even fewer studies were found involving self-induced emotions. Furthermore, most of the studies rely on single stimuli to evoke emotions. The question of “whether different stimuli for same emotion elicitation generate any subject-independent correlations” remains unanswered. This paper introduces a dual modality based emotion elicitation paradigm to investigate if emotions can be classified induced with different stimuli. A method has been proposed based on common spatial pattern (CSP) and linear discriminant analysis (LDA) to analyze human brain signals for fear emotions evoked with two different stimuli. Self-induced emotional imagery is one of the considered stimuli, while audio/video clips are used as the other stimuli. The method extracts features from the CSP algorithm and LDA performs classification. To investigate associated EEG correlations, a spectral analysis was performed. To further improve the performance, CSP was compared with other regularized techniques. Critical EEG channels are identified based on spatial filter weights. To the best of our knowledge, our work provides the first contribution for the assessment of EEG correlations in the case of self versus video induced emotions captured with a commercial grade EEG device.

The Plurality of Perspectives

There is a second problem with Vendler’s proposed reduction of “objective” imaginings (from-the-outside) to “subjective” imaginings (from-the-inside): it dismisses the possibility of seeing oneself from-the-outside while still maintaining internal or embodied perspectives such as kinesthetic imagery. Yet internal and external perspectives can often come together or come apart in interesting ways: there is a plurality of perspectives. Evidence for the claim that an external visual perspective may coexist and align with internal embodied and emotional imagery is explored by drawing on examples from autobiographical memory, cinema, and sports psychology. Observer perspectives are memories in which one sees oneself from-the-outside, but one may still maintain internal embodied and emotional imagery and there need be no inherent feeling of detachment.