This study by Nilotpal Das and Monisha Chakraborty discusses EEGOpt, which is a Bayesian optimization approach automating end-to-end EEG classification that treats every pipeline choice, such as denoising (none/EMD/WPD), feature extraction (covariance, wavelet, fractal, entropy, statistical), PCA dimensionality reduction, and classifier (KNN, SVM, LR, RF, MLP), as hyperparameters in a hierarchical search space. Tree-structured Parzen Estimators (TPE) optimize signal denoising, feature extraction, and classifier selection. TPE also evaluates the configurations on the training data and then tests the best model on a held-out set of data. With three music-EEG datasets (ICM, NMED-E, and NMED-M), EEGOpt showed consistently more accuracy and efficiency. WPD was the best denoiser, and KNN with covariance/wavelet features won. Analyses of optimization showed that TPE approached the global optima more closely than GP, CMA-ES, QMC, and Random Search. Other experiments showed that caching with the key accelerator was effective.