Sophia Jowett, Victoria E Warburton, Lee C Beaumont, Luke Felton
Teacher-Student relationship quality as a barometer of teaching and learning effectiveness: Conceptualization and measurement
The British journal of educational psychology
emotion recognition based electroencephalography eeg signals garnered significant attention across diverse domains including healthcare education information sharing gaming among others despite potential absence standardized feature set poses challenge efficiently classifying various emotions addressing issue high dimensionality paper introduces advanced variant coati optimization algorithm coa called ecoa global optimization selecting best subset eeg features emotion recognition specifically coa suffers local optima imbalanced exploitation abilities metaheuristic methods proposed ecoa incorporates coa runge kutta optimizer run algorithms scale factor sf enhanced solution quality esq mechanism run applied resolve raised shortcomings coa proposed ecoa algorithm extensively evaluated using cec22 test suite two eeg emotion recognition datasets deap dreamer furthermore ecoa applied binary multiclass classification emotions dimensions valence arousal dominance using multilayer perceptron neural network mlpnn experimental results revealed ecoa algorithm powerful search capabilities original coa seven wellknown counterpart methods related statistical convergence diversity measures furthermore ecoa efficiently support feature selection find best eeg features maximize performance four quadratic emotion classification problems compared methods counterparts suggested method obtains classification accuracy 8517 9521 binary classification low high arousal emotions two public datasets deap dreamer respectively 558 898 superior existing approaches working datasets different subjects respectively