The teaching-learning process is seeing a big transformation in this digital age. It involves digital classrooms with various accessories of online tools such as video conferencing, digital materials, and other platforms for learning and assessment with options for both real-time and self-paced work in addition to the availability of teachers over video conferencing, text, phone, email, etc. To improve the online learning efficiency, assessing the cognitive state during the learning phase is highly required for the success of these developments. This work focused on cognitive state analysis during different learning tasks is determined by EEG brain signals that are captured using 128 channels Emotive Epoch headset device. Artifacts prominent in raw signals are filtered by linear filtering. Feature extraction for determination of concentration levels is done by applying fuzzy fractal dimension measures and Discrete Wavelet Transform (DWT) on the processed signals. The classification of extracted parameters into concentration levels is done by using deep learning algorithms like Enhanced Convolutional Neural Network (ECNN). This ECNN deep learning classification is highly accurate amongst all other remaining classifiers and is used as a feedback model to regulate this cognitive state.

在这个数字时代，教学过程正在发生巨大的变化。它涉及具有各种在线工具附件的数字教室，例如视频会议，数字资料和其他用于学习和评估的平台，除了可以通过视频会议，文本，为了提高在线学习效率，对于这些发展的成功，迫切需要评估学习阶段的认知状态。这项工作专注于在不同学习任务期间的认知状态分析，是由使用128通道Emotive Epoch头戴式耳机设备捕获的EEG脑信号确定的。通过线性滤波对原始信号中突出的伪像进行滤波。通过对处理后的信号应用模糊分形维度量和离散小波变换（DWT）来完成确定浓度水平的特征提取。通过使用深度学习算法（如增强卷积神经网络（ECNN））将提取的参数分类为浓度水平。此ECNN深度学习分类在所有其他其余分类器中非常准确，并用作调节此认知状态的反馈模型。