Emotion Recognition Technology Research Articles

Revolutionizing getaways: automation and artificial intelligence’s transformative journey in tourism and hospitality

PurposeThis theme issue aims to explore the depth and breadth of automation and artificial intelligence’s (AI’s) impact on the hospitality and tourism industry.Design/methodology/approachThe thematic examination of the articles presented in this theme issue highlights the central results concerning the strategic inquiry, especially regarding their addition to knowledge, which is vital for fostering idea creation and theoretical research beneficial to various stakeholders in the tourism and hospitality sector. Given the swift evolution of these technologies, grasping their utilization, advantages and obstacles is essential for industry experts, decision-makers and scholars.FindingsThis theme issue encapsulates the transformative impact of automation and AI in the tourism and hospitality industry, highlighting the juxtaposition of enhanced operational efficiency against employment security concerns. It emphasizes the need for ethical governance to address AI’s ethical dilemmas and security vulnerabilities. The exploration of consumer attitudes towards AI, particularly in service robots and facial emotion recognition technologies, is deemed crucial for leveraging AI’s full potential in service delivery. Furthermore, AI’s entry into gastronomy prompts a reevaluation of traditional culinary practices, advocating for a balance between technological innovation and the preservation of cultural heritage.Originality/valueThis theme issue stands out for its focused exploration of the transformative effects of automation and AI within the hospitality and tourism industry. Unlike broader discussions on technology in business, it delves into specific AI-driven innovations, operational changes and the resultant shifts in consumer behavior and industry standards. By curating a collection of empirical research, theoretical analyses and content analyses, this issue is providing important insights into how automation and AI are reshaping service delivery, management practices and strategic decision-making in an industry characterized by its intense customer focus and reliance on human-to-human interaction.

EMOTION BASED MUSIC RECOMMENDATION SYSTEM

The daily lives of people are greatly influenced by music. Everyone wants to listen to music that suits their personal tastes and mood. Users are constantly needed to manually browse the music and construct a playlist based on their mood. The suggested project, which creates a music playlist depending on users' current moods, is quite effective. The most effective approach to convey a person's current mood is through their facial expressions. Webcams are used to capture facial expressions, which are then fed into a learning algorithm to determine the most likely emotion. The emotion recognition system is built using the FER 2013 dataset for training, which allows it to identify seven different emotions. Its operation involves capturing live video input from a webcam, processing it through the model, and then providing a prediction of the detected emotion. By employing webcams to capture real-time facial expressions, the system dynamically adapts playlist recommendations to match the user's emotional state. This seamless integration of emotion recognition technology with music selection enhances user experience by ensuring that the music aligns with their current feelings, thereby fostering a more immersive and personalized listening experience.

Philosophical Lessons for Emotion Recognition Technology

Emotion recognition technology uses artificial intelligence to make inferences about a person’s emotions, on the basis of their facial expressions, body language, tone of voice, or other types of input. Underlying such technology are a variety of assumptions about the manifestation, nature, and value of emotions. To assure the quality and desirability of emotion recognition technology, it is important to critically assess the assumptions embedded in the technology. Within philosophy, there is a long tradition of epistemological, ontological, phenomenological, and ethical reflection on the manifestation, nature, and value of emotions. This article draws from this tradition of philosophy of emotions, in order to challenge the assumptions underlying current emotion recognition technology and to promote a more critical engagement with the concept of emotions in the tech-industry.

Convey: Developing a Facial Emotion Recognition System for Enhancing Interpersonal Interactions of Children with Autism Spectrum Disorders

Children with Autism Spectrum Disorder (ASD) oftentimes have difficulty in both interpersonal and intrapersonal interactions. Most children with ASD lack emotional intelligence and cannot properly comprehend social cues and responses of the people around them. These social impairments are causing them to have difficulty in maintaining relationships thus, to combat these issues, the researchers presented an application with Facial Emotion Recognition (FER) technology using the deep-learning approach namely Convolutional Neural Network (CNN). Using the FER allows the children to perceive the emotions of their interaction partner at face value. The model used in developing the application sits at an accuracy level of 98%. In addition to the FER technology, the research implements a kind of mood meter that enables the children to plot their mood and a dashboard that tracks the accumulated plots. The tracker lets the children convey their emotions as well as help their interaction partner to understand them better. Furthermore, the application includes a recommendation system that suggests various relaxation and meditation techniques that the child can use to manage outbursts or spontaneous emotional activity. The Convey mobile application aimed to empower children that has ASD and help them lead a normal life.

Improving Speech Recognition with Convolutional Neural Networks

This project explores advanced techniques in speech recognition, focusing on emotion identification using Convolutional Neural Networks for improved accuracy and real-time processing efficiency. Emotion recognition from speech signals plays a crucial role in various applications, including human-computer interaction, customer service, mental health monitoring, and entertainment. This project proposes an innovative approach to emotion recognition using Convolutional Neural Networks (CNNs) applied to speech data. By leveraging advanced deep learning techniques, the proposed system aims to accurately identify and classify emotions conveyed through vocal expressions. The project begins with a comprehensive review of existing literature on emotion recognition and speech processing, identifying key challenges and opportunities in the field. Building upon prior research, the project introduces a novel CNN architecture optimized for emotion recognition tasks. This architecture is designed to extract relevant features from speech signals and capture subtle nuances indicative of different emotional states. One of the distinguishing features of the proposed approach is its multi-modal integration, which combines information from both audio and visual modalities to enhance emotion recognition accuracy. In addition to analysing speech signals, the system incorporates visual cues such as facial expressions and gestures, providing a more comprehensive understanding of the speaker's emotional state. Real-time processing efficiency is prioritized in the design of the system, ensuring prompt and responsive emotion recognition in interactive applications. Optimization techniques such as model quantization and lightweight architecture design are employed to minimize computational overhead while maintaining high accuracy. To address the variability and subjectivity of emotional expression, the system incorporates user-specific adaptation mechanisms. Through continuous learning and feedback integration, the system dynamically adapts to individual speakers' speech patterns and emotional characteristics, enhancing its ability to accurately recognize emotions in diverse contexts. The project also explores ensemble learning strategies to improve robustness and generalization performance. By combining predictions from multiple CNN models trained on diverse datasets, the system achieves greater resilience to variations in emotional expression and environmental factors. Ethical considerations, including privacy protection and responsible data handling, are integral aspects of the project's design and implementation. Measures are implemented to ensure the ethical collection, storage, and usage of speech data, safeguarding user privacy and maintaining trust in the system. Overall, the proposed system represents a significant advancement in emotion recognition technology, offering a sophisticated and versatile solution for accurately identifying emotions from speech signals. By leveraging deep learning techniques, multi-modal integration, real-time processing optimization, user-specific adaptation, and ensemble learning, the system demonstrates promising potential for various practical applications requiring robust and context-aware emotion recognition capabilities. Keywords: Speech Recognition, Emotion Identification, Convolutional Neural Networks (CNNs), Real-time Processing, Multi-modal Integration, User-specific Adaptation, Ensemble Learning, Deep Learning, Emotional Expression, Ethical Data Handling

Automatic detection of expressed emotion from Five-Minute Speech Samples: Challenges and opportunities.

Research into clinical applications of speech-based emotion recognition (SER) technologies has been steadily increasing over the past few years. One such potential application is the automatic recognition of expressed emotion (EE) components within family environments. The identification of EE is highly important as they have been linked with a range of adverse life events. Manual coding of these events requires time-consuming specialist training, amplifying the need for automated approaches. Herein we describe an automated machine learning approach for determining the degree of warmth, a key component of EE, from acoustic and text natural language features. Our dataset of 52 recorded interviews is taken from recordings, collected over 20 years ago, from a nationally representative birth cohort of British twin children, and was manually coded for EE by two researchers (inter-rater reliability 0.84-0.90). We demonstrate that the degree of warmth can be predicted with an F1-score of 64.7% despite working with audio recordings of highly variable quality. Our highly promising results suggest that machine learning may be able to assist in the coding of EE in the near future.

Implementing machine learning techniques for continuous emotion prediction from uniformly segmented voice recordings.

Emotional recognition from audio recordings is a rapidly advancing field, with significant implications for artificial intelligence and human-computer interaction. This study introduces a novel method for detecting emotions from short, 1.5 s audio samples, aiming to improve accuracy and efficiency in emotion recognition technologies. We utilized 1,510 unique audio samples from two databases in German and English to train our models. We extracted various features for emotion prediction, employing Deep Neural Networks (DNN) for general feature analysis, Convolutional Neural Networks (CNN) for spectrogram analysis, and a hybrid model combining both approaches (C-DNN). The study addressed challenges associated with dataset heterogeneity, language differences, and the complexities of audio sample trimming. Our models demonstrated accuracy significantly surpassing random guessing, aligning closely with human evaluative benchmarks. This indicates the effectiveness of our approach in recognizing emotional states from brief audio clips. Despite the challenges of integrating diverse datasets and managing short audio samples, our findings suggest considerable potential for this methodology in real-time emotion detection from continuous speech. This could contribute to improving the emotional intelligence of AI and its applications in various areas.

AN EEG-BASED EMOTION RECOGNITION MODEL USING AN INTERACTION DESIGN FRAMEWORK AND DEEP LEARNING

This research makes significant advances in the field of emotion recognition by presenting a new generative adversarial network (GAN) model that integrates deep learning with electroencephalography (EEG). To achieve more accurate data production and real data matching, the model utilizes self-attention and residual neural networks. Additionally, this process is accomplished by substituting an autoencoder for the discriminator in the GAN, and incorporating a reconstruction loss function. We include the self-attention mechanism and residual block in the building of the model to overcome the vanishing gradient problem. This allows the model to acquire information related to emotions in a more in-depth manner, which ultimately results in an improvement in the emotion detection accuracy. The DEAP and MAHNOB-HCI datasets are chosen for the experimental validation portion of this research. These datasets are subsequently compared and analyzed with traditional deep learning methods and well-known emotion identification algorithms. Based on these findings, it is evident that the model that we propose performs exceptionally well on the emotion recognition test, which offers substantial support for studies and applications in this field. In addition, within the context of emotion detection systems, this study places particular emphasis on the crucial role that interaction design frameworks play in enhancing both the user experience and the usability of the system. By pushing the emotion recognition technology boundaries, a new paradigm for the application of deep learning in EEG emotion recognition is provided with this comprehensive research contribution.

Real-Time Emotion Recognition and its Effects in a Learning Environment.

The purpose of the article is to understand the current state of both the technology and the implementation of emotion recognition in the educational environment. The goal is to obtain detailed information about the current state of emotion recognition technology and how its practical use is being carried out in educational settings. In this line, it examines the proposals from publications over the last 10 years on the advancement of technology for emotion recognition in education. A total of 1,347 studies were obtained and 43 were included in the review for analysis and discussion. The article demonstrates how the number of studies has increased in recent years, with a higher frequency in online learning. Furthermore, according to the Technological Readiness Level, despite the growing interest in emotion recognition in the educational environment, its implementation is still far from becoming a reality. Most of the research has been conducted from a theoretical perspective and none of them has been fully developed and implemented in the classroom. In addition, many of the studies analysed have not tested the validity of their findings.

The Infopolitics of feeling: How race and disability are configured in Emotion Recognition Technology

In this article, we argue that facial emotion recognition technology (facial ERT) reproduces historical forms of pseudoscience based on the concept of quantifiable and unequally distributed emotional capacity. Drawing on Kyla Schuller’s Biopolitics of Feeling and Colin Koopman’s theory of infopower, we put forward the term ‘the infopolitics of feeling’ to describe how facial ERT encodes culturally ‘correct’ or normative forms of emotional expression that have historically been used to define and delineate what it means to be human. To make this argument, we provide a close reading of Girl Decoded, the autobiography of Rana el Kaliouby, the founder and former CEO of the leading Emotion artificial intelligence (AI) firm Affectiva. Girl Decoded, we argue pits el Kaliouby herself – portrayed as the empathetic, liberal, emotionally expressive and ideal ‘feeling’ subject – against two non-normative figures: the unfeeling autist and the inscrutable Oriental who must be ‘cured’ through Affectiva’s facial ERT.

Enhancing User Experience Using a Framework Integrating Emotion Recognition and Eye-Tracking

In a rapidly evolving digital landscape, ensuring a seamless and engaging user experience (UX) has become paramount. This study delves into the intricate realm of web interaction design, aiming to enhance user satisfaction and engagement. Through the integration of emotion recognition and eye-tracking technologies, a profound relationship between user emotions and web interface design is unveiled. The central theme of this research revolves around the integration of emotion recognition and eye-tracking technologies to evaluate web interaction designs. An experimental study involving 24 participants from diverse backgrounds and age groups was conducted. These participants navigated web interfaces that featured both emotion recognition and eye-tracking technologies. Three distinct tasks were carefully crafted to represent a spectrum of web interactions, ranging from basic navigation to complex decision-making. Data collection involved real-time valence and arousal scores, video recordings, visual attention patterns, task completion times, and questionnaires. The analysis revealed a series of significant discoveries. Users who engaged with simplified web versions consistently exhibited elevated valence values, indicative of heightened positive emotional feedback, diverging starkly from their counterparts navigating complex web iterations. The dynamic ebb and flow of emotional states, underscored by arousal levels, underscore the pivotal role of real-time emotional assessment in the critical evaluation of web interfaces. Moreover, the study unveiled a persistent preference for a state of emotional calmness during interactions, demonstrating a universal need for user-centered design principles that prioritize minimal cognitive load and emotional tranquility. In summation, this research contributes a robust framework to the design community and academia for the comprehensive evaluation of web interaction designs. The findings underscore the paramount significance of simplicity, real-time emotional evaluation, and unwavering adherence to user-centered design principles in the realm of web interaction. This study constitutes an invaluable repository for designers, developers, and researchers, steering their endeavors towards the relentless pursuit of optimized user experiences within the ever-evolving digital landscape.

Exploration of Facial Emotion Detection Systems Utilizing Convolutional Neural Networks: A Comprehensive Review

Facial emotion detection systems have witnessed significant advancements, particularly with the utilization of convolutional neural networks (CNNs). This paper provides a thorough survey of such systems, beginning with an introduction to artificial intelligence and the evolutionary trajectory of neural networks, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and CNNs. The paper elaborates on CNNs' architecture and functionality, elucidating key components such as convolutional layers, pooling layers, and fully connected layers, while also spotlighting prominent CNN architectures like AlexNet and ResNet. It delineates the broad scope and diverse applications of facial emotion detection systems across various domains, including marketing research, crowd testing, AI robots, banking, and entertainment. In the literature review section, recent research papers on CNN models for facial expression recognition are synthesized, highlighting variances in datasets, methodologies, and accuracy levels. The paper concludes that CNNs represent the current pinnacle of facial emotion classification techniques, surpassing previous methodologies such as eigenfaces. It underscores the efficacy of deep CNN architectures trained on extensive facial image datasets in proficiently identifying emotions from facial expressions. Moreover, the paper emphasizes the necessity for ongoing endeavors to enhance accuracy, particularly concerning complex emotions like disgust. In essence, CNNs exhibit substantial promise for the development of real-world facial emotion detection systems, heralding a new era of sophisticated emotion recognition technology.

Framework for the Classification of Facial Emotions Using Soft Computing Techniques

Background:: Facial emotion recognition (FER) technology is enumerated as a productive interface in several operations, which has been specifically focused on as a substitute communication path among a user and an appliance for human computer interface in the previous decade. The efficiency of the facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models. Methods:: The objective of this paper was to classify the facial emotion electromyogram (EMG) signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm, and Naive-Bayes algorithm. This research work was directed towards the correlation among the classification accuracies by applying distinct feature extraction procedures on fEMGs. At first, eight participants (six male and two female) were recruited for data recording. Four electrodes were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear) and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation techniques for enhanced processing. After that, the time-domain and frequency-domain feature extraction procedures were carried out. Time domain and frequency domain features were applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and happy. After the feature extraction process, the extracted features were aligned into respective emotions by applying classifiers. The extracted features were further trained and classified by applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB 2020. Results:: The SVM classifier and neural network classifier generated an accuracy of 93.80% and 96.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%. Conclusion:: Facial emotion recognition (FER) is foresighted as a progressive or futuristic model, which has attracted the attention of researchers in several areas of learning due to its higher prospects in distinct applications. Acknowledgment of the emotions through biomedical signals produced from movements of facial muscles is lately presented using an explicit and authentic route.

Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram

Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.

Interaction mode enables user perception recognition and perception optimization: An AI human-computer interaction study

Artificial intelligent (AI) has various ways of human-computer interaction, but most of them overlook the recognition of human perception. If the interaction mode is combined with psychology, the user's mood change can be identified by the user's subtle expression, movement change and voice tone change, so as to provide corresponding services and improve the user experience. Statistical analysis of human responses to different situations in cognitive psychology, incorporating them into human-computer interaction methods. The current human-computer interaction modes in products tend to be standardized, and focusing user experience on user perception will bring special experiences to users. Emotional recognition is a cross disciplinary discipline with broad application prospects, but it has not yet reached a mature stage and requires corpus enrichment, theoretical strengthening, and method innovation. The era of artificial intelligence is leading a new wave of technological progress, and emotion recognition, as an important topic in the field of artificial intelligence, can help computer intelligence recognize human emotions and make human-computer interaction more friendly. In the near future, research on emotion recognition technology will make greater progress and be better applied to practical products.

Facial emotion recognition through artificial intelligence

This paper introduces a study employing artificial intelligence (AI) to utilize computer vision algorithms for detecting human emotions in video content during user interactions with diverse visual stimuli. The research aims to unveil the creation of software capable of emotion detection by leveraging AI algorithms and image processing pipelines to identify users' facial expressions. The process involves assessing users through images and facilitating the implementation of computer vision algorithms aligned with psychological theories defining emotions and their recognizable features. The study demonstrates the feasibility of emotion recognition through convolutional neural networks (CNN) and software development and training based on facial expressions. The results highlight successful emotion identification; however, precision improvement necessitates further training for contexts with more diverse images and additional algorithms to distinguish closely related emotional patterns. The discussion and conclusions emphasize the potential of A.I. and computer vision algorithms in emotion detection, providing insights into software development, ongoing training, and the evolving landscape of emotion recognition technology. Further training is necessary for contexts with more diverse images, alongside additional algorithms that can effectively distinguish between facial expressions depicting closely related emotional patterns, enhancing certainty and accuracy.

Healthy and Happy? An Ethical Investigation of Emotion Recognition and Regulation Technologies (ERR) within Ambient Assisted Living (AAL)

Ambient Assisted Living (AAL) refers to technologies that track daily activities of persons in need of care to enhance their autonomy and minimise their need for assistance. New technological developments show an increasing effort to integrate automated emotion recognition and regulation (ERR) into AAL systems. These technologies aim to recognise emotions via different sensors and, eventually, to regulate emotions defined as “negative” via different forms of intervention. Although these technologies are already implemented in other areas, AAL stands out by its tendency to enable an inconspicuous 24-hour surveillance in the private living space of users who rely on the technology to maintain a certain degree of independence in their daily activities. The combination of both technologies represents a new dimension of emotion recognition in a potentially vulnerable group of users. Our paper aims to provide an ethical contextualisation of the novel combination of both technologies. We discuss different concepts of emotions, namely Basic Emotion Theory (BET) and the Circumplex Model of Affect (CMA), that form the basis of ERR and provide an overview over the current technological developments in AAL. We highlight four ethical issues that specifically arise in the context of ERR in AAL systems, namely concerns regarding (1) the reductionist view of emotions, (2) solutionism as an underlying assumption of these technologies, (3) the privacy and autonomy of users and their emotions, (4) the tendency of machine learning techniques to normalise and generalise human behaviour and emotional reactions.

ENHANCING HUMAN-ROBOT INTERACTION THROUGH GROUP EMOTION RECOGNITION

Abstract - This article explores within the field of Human-Robot Interaction (HRI), focusing on the complicated relationship between emotions, decision-making, and robot behaviors. Emotions are essential to effective communication and interaction, requiring the development of emotion recognition systems in robots. The article explores both individual and group emotion recognition, including microexpressions and macroexpressions. Group emotion dynamics, encompassing phenomena like emotional contagion, convergence, and social influence, are separated to understand how emotions combine within collective settings. A concept, Group Emotion Recognition (GER), is introduced, providing a framework for recognizing emotions within groups. GER involves proximity metrics, emotion classification, and entropy-based analysis to quantify emotion diversity. The article also outlines how GER can enhance user engagement, personalize interactions, improve group dynamics, and foster social acceptance in various human-robot interaction scenarios. Decision-making based on GER, driven by positive or negative emotion labels, is discussed, highlighting the adaptability and sensitivity required for effective human-robot interactions. Ethical considerations regarding the use of emotion recognition technology are addressed throughout the article, emphasizing responsible implementation. Overall, this work lays a solid foundation for advancing the field of HRI by integrating emotion recognition and decision-making to create emotionally intelligent and socially aware robots.

Research on Emotional Improvement of Product Design Based on Emotion Recognition Technology

Abstract In this paper, we first extracted the time-domain features, frequency-domain features and spatial-domain features of EEG signals, combined with the three-stage feature selection algorithm applicable to the binary classification problem and the multi-classification problem, and constructed the SEE model for emotion recognition based on EEG signals. Then, based on the three-level design model of emotion, emotion decoding and labeling are carried out on the instinctive layer, behavioral layer and reflective layer of product design, and the constructed model is combined to improve the product design emotionally. Finally, after analyzing the results of product emotion annotation, we explore the performance of the EEG-based emotion recognition model and the improvement effect of product design emotionalization. The results showed that the average accuracy of the EEG signal emotion recognition model for various emotion recognition was about 0.99, and the intensity of emotion intensity in Dahe was 0.32 and 0.25, respectively, accounting for 0.57 of the total sample, and the performance evaluation indicators of the eight emotions were greater than 0.85. Ninety percent of product experiencers had pre- and post-improvement differences between [0.12, 0.22] for happiness and [-0.20, -0.04] for dissatisfaction.

Functional connectivity-enhanced feature-grouped attention network for cross-subject EEG emotion recognition

Electroencephalogram (EEG)-based automatic emotion recognition technologies are gaining significant attention and have become crucial in the field of brain–computer interfaces (BCIs). In particular, deep learning methods have been widely used in emotion recognition in recent years. However, most existing methods generally tend to focus on EEG spatiotemporal information, which ignores the potential relationships between brain activity signals and the differences in functional connectivity under different emotions. Here, we raise a functional connectivity-enhanced feature-grouped attention network (FC-FAN) for cross-subject emotion recognition. The FC-FAN model developed is a dual-input model. One input consists of the differential entropy data derived from the original EEG signals, while the other input comprises the functional connectivity data obtained through the calculation of the phase synchronization index. Then the primary EEG features of the two groups’ input data are extracted through two specific residual blocks. Next, the designed time-series feature grouped attention module (TFGAM) and functional connectivity feature grouped attention model (F2GAM) are utilized to mark interested information or suppress uninterested features for the two groups’ features, respectively. Finally, generated information interacts through a fusion operator. The designed framework could not only sufficiently learn the spatiotemporal features of EEG signals but also clearly analyze nonlinear correlations between electrode signals. Comprehensive tests confirm that the FC-FAN has an excellent effect on subject-independent emotion recognition tasks.