Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Consciousness and General Anesthesia: Challenges for Measuring the Depth of Anesthesia

The optimal consciousness level required for general anesthesia with surgery is unclear, but in existing practice, anesthetic oblivion, may be incomplete. This article discusses the concept of consciousness, how it is altered by anesthetics, the challenges for assessing consciousness, currently used technologies for assessing anesthesia levels, and future research directions. Wakefulness is marked by a subjective experience of existence (consciousness), perception of input from the body or the environment (connectedness), the ability for volitional responsiveness, and a sense of continuity in time. Anesthetic drugs may selectively impair some of these components without complete extinction of the subjective experience of existence. In agreement with Sanders et al. (2012), the authors propose that a state of disconnected consciousness is the optimal level of anesthesia, as it likely avoids both awareness and the possible dangers of oversedation. However, at present, there are no reliably tested indices that can discriminate between connected consciousness, disconnected consciousness, and complete unconsciousness.

The status of e-learning, personality traits, and coping styles among medical students during the COVID-19 pandemic: a cross-sectional study

Objective

The objective of this study was to explore the learning preferences and habits of medical students during the pandemic home e-learning, and to investigate the incidence of adverse emotions, optimistic character level and coping style. To explore the influencing factors of adverse emotions.

Methods

A cross-sectional survey was conducted in China from March to June 2022. Medical students were recruited from three universities in China, and a questionnaire survey was conducted. The questionnaires consisted of a "e-learning preferences and habits questionnaire", life orientation test questionnaire (LOT-R), and simple coping style questionnaire (SCSQ). Finally, a total of 492 medical students who met the inclusion and exclusion criteria became the research subjects and completed the survey.

Results

A total of 57.7% believed they experienced no adverse emotions during home e-learning. ① During the COVID-19 pandemic, the score of optimistic personality of medical students was (7.25 ± 1.933), and the score of pessimistic personality was (5.82 ± 2.240). The score of positive coping was (21.75 ± 5.379), and the score of negative coping was (11.75 ± 3.611). ② The occurrence of medical students' adverse emotions during e-learning was influenced by "Whether there is a private, quiet space to study", "Degree of knowledge mastery", "Physical discomfort or not", "Keep a regular schedule or not", "Optimistic personality tendency".

Conclusion

This study demonstrates the during home e-learning, most medical students have their own learning equipment and can meet their learning needs. Their favorite mobile device to use is a mobile phone, and their favorite method of teaching is to provide course playback. More than half of medical students believe that they have some inconvenience in conducting research during home e-learning. With regard to teacher's real-time screen, the largest number of medical students support teachers turning on live screens so that they feel like they are interacting with the teacher. The preference for blended teaching is highest among medical students. In general, medical students were highly adaptive of the newest e-learning approach. Based on the statistic analysis, the factors that "Whether there is a private, quiet space to study", "Degree of knowledge mastery", "Physical discomfort or not", "Keep a regular schedule or not", and "Optimistic personality tendency" may be the influencing factors for the occurrence of adverse emotions.

SSTM-IS: simplified STM method based on instance selection for real-time EEG emotion recognition

Introduction

EEG signals can non-invasively monitor the brain activities and have been widely used in brain-computer interfaces (BCI). One of the research areas is to recognize emotions objectively through EEG. In fact, the emotion of people changes over time, however, most of the existing affective BCIs process data and recognize emotions offline, and thus cannot be applied to real-time emotion recognition.

Methods

In order to solve this problem, we introduce the instance selection strategy into transfer learning and propose a simplified style transfer mapping algorithm. In the proposed method, the informative instances are firstly selected from the source domain data, and then the update strategy of hyperparameters is also simplified for style transfer mapping, making the model training more quickly and accurately for a new subject.

Results

To verify the effectiveness of our algorithm, we carry out the experiments on SEED, SEED-IV and the offline dataset collected by ourselves, and achieve the recognition accuracies up to 86.78%, 82.55% and 77.68% in computing time of 7s, 4s and 10s, respectively. Furthermore, we also develop a real-time emotion recognition system which integrates the modules of EEG signal acquisition, data processing, emotion recognition and result visualization.

Discussion

Both the results of offline and online experiments show that the proposed algorithm can accurately recognize emotions in a short time, meeting the needs of real-time emotion recognition applications.

Online Learning for Wearable EEG-Based Emotion Classification

Giving emotional intelligence to machines can facilitate the early detection and prediction of mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition is widely applied because it measures electrical correlates directly from the brain rather than indirect measurement of other physiological responses initiated by the brain. Therefore, we used non-invasive and portable EEG sensors to develop a real-time emotion classification pipeline. The pipeline trains different binary classifiers for Valence and Arousal dimensions from an incoming EEG data stream achieving a 23.9% (Arousal) and 25.8% (Valence) higher F1-Score on the state-of-art AMIGOS dataset than previous work. Afterward, the pipeline was applied to the curated dataset from 15 participants using two consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. Mean F1-Scores of 87% (Arousal) and 82% (Valence) were achieved for an immediate label setting. Additionally, the pipeline proved to be fast enough to achieve predictions in real-time in a live scenario with delayed labels while continuously being updated. The significant discrepancy from the readily available labels on the classification scores leads to future work to include more data. Thereafter, the pipeline is ready to be used for real-time applications of emotion classification.

The effect of teacher support on Chinese university students' sustainable online learning engagement and online academic persistence in the post-epidemic era

Since entering the post-epidemic era of COVID-19 at the end of 2021, schools have mostly adopted a combined online and offline teaching mode to effectively respond to the normalized epidemic, which has changed the traditional learning mode of students. Based on the study demand-resources (SD-R) model theory, this study developed a research model and proposed six research hypotheses to explore the relationship between Chinese university students' perceived teacher support (PTS), online academic self-efficacy (OAS-E), online academic emotions (OAE), sustainable online learning engagement (SOLE), and online academic persistence (OAP) in the post-epidemic era. In this study, 593 Chinese university students were invited to respond to a questionnaire survey using the convenience sampling method. The results of the study showed that: PTS had a positive effect on OAS-E and OAE; OAS-E had a positive effect on OAE; OAS-E and OAE had a positive effect on the students' SOLE; and SOLE had a positive effect on their OAP. Based on the analysis, it is recommended that teachers provide more support and resources to further enhance students' academic self-efficacy and academic emotions, and thus ensure students' SOLE and OAP.

Research on Emotion Recognition Method Based on Adaptive Window and Fine-Grained Features in MOOC Learning

In MOOC learning, learners' emotions have an important impact on the learning effect. In order to solve the problem that learners' emotions are not obvious in the learning process, we propose a method to identify learner emotion by combining eye movement features and scene features. This method uses an adaptive window to partition samples and enhances sample features through fine-grained feature extraction. Using an adaptive window to partition samples can make the eye movement information in the sample more abundant, and fine-grained feature extraction from an adaptive window can increase discrimination between samples. After adopting the method proposed in this paper, the four-category emotion recognition accuracy of the single modality of eye movement reached 65.1% in MOOC learning scenarios. Both the adaptive window partition method and the fine-grained feature extraction method based on eye movement signals proposed in this paper can be applied to other modalities.

Predict Students’ Attention in Online Learning Using EEG Data

In education, it is critical to monitor students’ attention and measure the extents to which students participate and the differences in their levels and abilities. The overall goal of this study was to increase the quality of distance education. In particular, in order to craft an approach that will effectively augment online learning using objective measures of brain activity, we propose a brain–computer interface (BCI) system that aims to use electroencephalography (EEG) signals for the detection of student’s attention during online classes. This system will aid teachers to objectively assess student attention and engagement. To this end, experiments were conducted on a public dataset; we extracted power spectral density (PSD) features using used a fast Fourier transform. Different attention indexes were calculated. Then, we built three different classification algorithms: k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF). Our proposed random forest classifier achieved a higher accuracy (96%) than KNN and SVM. Moreover, our results compared to state-of-the-art attention-detection systems with respect to the same dataset. Our findings revealed that the proposed RF approach can be used to effectively distinguish the attention state of a user.

Responsive Dashboard as a Component of Learning Analytics System for Evaluation in Emergency Remote Teaching Situations

The pandemic crisis has forced the development of teaching and evaluation activities exclusively online. In this context, the emergency remote teaching (ERT) process, which raised a multitude of problems for institutions, teachers, and students, led the authors to consider it important to design a model for evaluating teaching and evaluation processes. The study objective presented in this paper was to develop a model for the evaluation system called the learning analytics and evaluation model (LAEM). We also validated a software instrument we designed called the EvalMathI system, which is to be used in the evaluation system and was developed and tested during the pandemic. The optimization of the evaluation process was accomplished by including and integrating the dashboard model in a responsive panel. With the dashboard from EvalMathI, six online courses were monitored in the 2019/2020 and 2020/2021 academic years, and for each of the six monitored courses, the evaluation of the curricula was performed through the analyzed parameters by highlighting the percentage achieved by each course on various components, such as content, adaptability, skills, and involvement. In addition, after collecting the data through interview guides, the authors were able to determine the extent to which online education during the COVID 19 pandemic has influenced the educational process. Through the developed model, the authors also found software tools to solve some of the problems raised by teaching and evaluation in the ERT environment.

Artificial Intelligence Methodologies for Data Management

This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning.

Emotion classification using EEG signal for women safety application based on deep learning

An emotion is a conscious logical response that varies for different situations in women’s life. These mental responses are caused by physiological, cognitive, and behavioral changes. Gender-based violence undermines the participation of women in decision-making, resulting in a decline in their quality of life. More accurate and automatic classification of women’s emotions can enhance human-computer interfaces and security in real time. There are some wearable technologies and mobile applications that claim to ensure the safety of women. However, they rely on limited social action and are ineffective at ensuring women’s safety when and where it is needed. In this work, a novel CDB-LSTM network has been proposed to accurately classify the emotions of women in seven different classes. The electroencephalogram (EEG) offers non-radioactive methods of identifying emotions. Initially, the EEG signals are preprocessed and they are converted into images via Time-Frequency Representation (TPR). A smoothed pseudo-Wigner-Ville distribution (SPWVD) is employed to convert the EEG time-domain signals into input images. Consequently, these converted images are given as input to the Convolutional Deep Belief Network (CDBN) for extracting the most relevant features. Finally, Bi-directional LSTM is used for classifying the emotions of women into seven classes namely: happy, relax, sad, fear, anxiety, anger, and stress. The proposed CDB-LSTM network preserves the high accuracy range of 97.27% in the validation phase. The proposed CDB-LSTM network improves the overall accuracy by 6.20% 32.98% 6.85% and 3.30% better than CNN-LSTM, Multi-domain feature fusion model, GCNN-LSTM and CNN with SVM and DT respectively.