# Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Classical Machine Learning and Deep Learning Optimization Techniques with Neurofeedback Source: [Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Classical Machine Learning and Deep Learning Optimization Techniques with Neurofeedback]() Collection index: [llms.txt]() Data updated: 2026-04-03T05:00:04.568Z Authors: Robiul Islam, Dmitry I. Ignatov, Karl Kaberg, Roman Nabatchikov Published: 2025-09-17T15:26:45.000Z ## Abstract This study investigates the performance of classifiers across EEG frequency bands, evaluating efficient class prediction for the left and right hemispheres using various optimisers. Three neural network architectures a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) are implemented and compared using the TensorFlow and PyTorch frameworks. Adagrad and RMSprop optimisers consistently outperformed others across frequency bands, with Adagrad excelling in the beta band and RMSprop achieving superior performance in the gamma band. Classical machine learning methods (Linear SVM and Random Forest) achieved perfect classification with 50--100 times faster training times than deep learning models. However, in neurofeedback simulations with real-time performance requirements, the deep neural network demonstrated superior feedback-signal generation (a 44.7% regulation rate versus 0% for classical methods). SHAP analysis reveals the nuanced contributions of EEG frequency bands to model decisions. Overall, the study highlights the importance of selecting a model dependent on the task: classical methods for efficient offline classification and deep learning for adaptive, real-time neurofeedback applications. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Hemisphere Classification on Mona Lisa](): Hemisphere Classification; Mona Lisa; ROC-AUC: 100. - [Hemisphere Classification on Necker cube](): Hemisphere Classification; Necker cube; ROC AUC: 100. - [EEG Hemisphere Classification on Mona Lisa](): EEG Hemisphere Classification; Mona Lisa; Negative Count: 16. - [EEG Hemisphere Classification on Necker Cube](): EEG Hemisphere Classification; Necker cube; Negative Count: 16. - [EEG-based brain hemisphere classification on Mona Lisa](): EEG-based brain hemisphere classification; Mona Lisa; ROC AUC: 90. - [EEG-based Brain Hemisphere Classification on Necker cube](): EEG-based brain hemisphere classification; Necker cube; ROC AUC: 0.92. ## Original sources and implementation [Original paper]() [PDF]()