TLBEMSE: design of a transfer learning-based bioinspired ensemble model for preemptive detection of stress and emotional disorders

Electroencephalogram signals are used to depict emotional and stress disorders. To overcome issues of existing models, novel transfer learning-based bioinspired ensemble model for preemptive detection of stress and emotional disorders is discussed. The proposed model includes features of mel-frequency cepstral coefficient, iVector, cosine, Fourier and wavelet components. A combination of these features is processed via gray wolf optimization which aims at variance maximization across. The selected features are converted into 2D representation and processed via a transfer learning-based convolutional neural network model combining ResNet 101, MobileVNet, and YoLo models. The classified results from these models are further cross-validated via use of ensemble classification that combines Naïve Bayes, support vector machine, random forest, logistic regression, and multilayer perceptron models. These classifiers perform several post-processing tasks involving identification of disease spread probability, estimation of future diseases, etc. The proposed model was trained on DEAP and interface datasets, compared w.r.t. various state-of-the-art methods, in relation to accuracy, recall, precision, area under the curve, and delay performance. Based on this performance, proposed model’s effectiveness was noticed, showcasing 8.5% higher accuracy, 8.3% higher precision, 5.9% better recall, 4.5% better AUC, and 14.9% faster classification performance, which makes it highly useful for clinical deployments.