Improve Affective Learning with EEG Approach

With the development of computer science, cognitive science and psychology, a new paradigm, affective learning, has emerged into e-learning domain. Although scientists and researchers have achieved fruitful outcomes in exploring the ways of detecting and understanding learners affect, e.g. eyes motion, facial expression etc., it sounds still necessary to deepen the recognition of learners affect in learning procedure with innovative methodologies. Our research focused on using bio-signals based methodology to explore learner's affect and the study was primarily made on Electroencephalography (EEG). After the EEG signals were collected from EEG equipment, we tidied the EEG data with signal processing algorithms and then extracted some features. We applied k-Nearest-Neighbor classifier and Naive Bayes classifier to these features to find out a combination, which may mostly contribute to reflect learners' affect, for example, Attention. In the classification algorithm, we presented a different way of using the Self-Assessment Manikin (SAM) model to classify and analyze learners attention, although the SAM was normally used for classifying emotions, for example, happiness etc. For the purpose of evaluating our findings, we also developed an affective learning prototype based on university e-learning web site. A real time EEG feedback window and an attention report were integrated into the system. The result of the experiment was encouraging and further discussion was also included in this paper

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition