Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks Dharmendra Pathak * , Ramgopal Kashyap Department of CSE, Amity School of Engineering and Technology, Amity University, Chhattisgarh 492001, India Corresponding Author Email: dharmendra.pathak@s.amity.edu https://doi.org/10.18280/ts.400414 ABSTRACT Received: 16 January 2023 Revised: 1 April 2023 Accepted: 28 May 2023 Available online: 31 August 2023 The COVID-19 pandemic has precipitated an unprecedented surge in the proliferation of online E-learning platforms, designed to cater to a wide array of subjects across all age groups. However, a paucity of these platforms adopts a learner-centric approach or validates user learning, underscoring the need for effective E-learning validation and personalized learning recommendations. This paper addresses these challenges by implementing an innovative approach that leverages real-time electroencephalogram (EEG) signals collected from learners, who don neuro headsets while partaking in online courses. These EEG signals are subsequently classified using Convolutional Neural Networks (CNN) and Long Short- Term Memory (LSTM) deep learning models, with the intent of discerning the efficacy of the E-learning process. The proposed models have yielded promising classification accuracies of 68% and 97% for the CNN and LSTM models, respectively, demonstrating their rapidity and precision in classifying E-learning EEG signals. Thus, these models hold substantial potential for application in similar E-learning validation scenarios. Furthermore, this study introduces an automated framework designed to track the learning curve of users and furnish valuable recommendations for E-learning materials. The presented approach, therefore, not only validates the E-learning process but also aids in optimizing the learning experiences on E-learning platforms. Keywords: automated framework, convolution neural network, deep learning, EEG signals, E- learning, feature extraction, Long Short- Term Memory, neuro headsets 1. INTRODUCTION We are currently witnessing an era characterized by a burgeoning growth of technology in education. From online courses and virtual laboratories to e-tutoring, digital learning platforms have emerged as effective and economical alternatives to traditional classroom instruction, a trend catalyzed by the ongoing COVID-19 pandemic. Recent research indicates that E-learning platforms enhance student retention rates by 25% to 60% compared to conventional teaching methods, offering unparalleled flexibility in terms of time, location independence, resource availability, and ease of access [1]. However, despite the demonstrated efficacy of E-learning, maintaining high levels of concentration over extended periods remains a formidable challenge for users [2]. Consequently, there is a pressing need for a framework capable of not only validating and customizing learning in real-time but also elucidating the user's learning trajectory. Electroencephalographic (EEG) signals, the digital imprints of brain activity measured in microvolts (μV), have been proposed as a solution. These signals are characterized by specific frequencies: Delta (0Hz to 4Hz), associated with healing, deep sleep, and the immune system; Theta (4Hz to 8Hz), correlated with relaxation, creativity, and emotional states; Alpha (8Hz to 12Hz), indicative of focus and relaxation; Beta (12Hz to 40Hz), linked to problem-solving and conscious focus; and Gamma (40Hz to 100Hz), representative of acute senses, cognition, and learning. These signals can be captured using neuro headsets or consumer-grade brain-computer interface (BCI) devices, with subsequent analysis via machine learning/deep learning algorithms. Deep learning, a subset of machine learning, employs deep neural networks to mimic the functions of the human brain for classification tasks [3]. As depicted in Figure 1, each layer within these networks - input, hidden, and output - serves as a processing unit responsible for tasks such as feature extraction and classification. Crucial parameters governing the strength of network classification include weight, bias, and activation functions. Deep learning's ability to handle large, complex datasets through the concept of feature hierarchy, as demonstrated in Figure 2, renders it ideal for pattern recognition, classification, and the identification of hitherto unknown patterns [4]. The present study explores the classification of EEG signals using Convolutional Neural Networks (CNN) and Long Short- Term Memory (LSTM) deep learning models. These models are capable of classifying real-time E-learning EEG data to validate user learning, monitor attention levels, and delineate learning patterns. Furthermore, the proposed framework offers recommendations for E-learning material customization and provides improved feedback mechanisms for individual and collaborative learning. This methodology could also serve as a potential tool for identifying learning disabilities among students. Traitement du Signal Vol. 40, No. 4, August, 2023, pp. 1457-1467 Journal homepage: http://iieta.org/journals/ts 1457