Surya Pavan Kumar Gudla 3 2 ( April - June ) The study of emotion recognition via EEG is important due to the role emotion plays in decision making behaviour mental health human computer interaction and understanding of emotion from an EEG perspective Traditional machine learning methods use handcrafted features and previously have not been able to cope with nonlinear brain cell activity patterns While the deep learning architectures excel at building representations the data and compute power needed are often large In this paper a quantum kernel learning QKL approach for emotions recognition using EEG data is suggested The framework combines both the spectral preprocessing and the covariancebased representations on the symmetric positive definite SPD Riemannian manifold and tangent space embedding and quantum support vector learning The signals from EEG are mapped into a structured covariance representation that is able to maintain interchannel statistical structures The filters in quantum feature maps are parameterized quantum circuits that lift classical EEG representations to exponentially larger Hilbert spaces and thus offer a more powerfully discriminative kernel where classical EEG cannot compete The QSVM dual optimization problem is tackled in a classical manner with the quantum kernel Gram matrix A 32 channel 40 subject benchmark is used for the experimental analysis with 975 classification accuracy and higher robustness against the degradation of the signals when compared to the state of the art SVM CNNSVM and CNNQSVM baseline methods on all of the evaluation metrics Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026