AN ADAPTIVE QUANTUM COMPUTING FRAMEWORK FOR EEG BRAIN-STATE CLASSIFICATION USING ENTANGLEMENT-AWARE FEATURE MAPPING

Electroencephalography is widely used in braincomputer interfaces, neurological research, and brain-state classification. However, EEG signals contain noise, artifacts, and variations between individuals, making accurate classification difficult. This project proposes an Adaptive Quantum Computing Framework for EEG Brain-State Classification Using Entanglement-Aware Feature Mapping. Initially, EEG signals are collected from publicly available datasets and preprocessed using filtering, artifact removal, segmentation, and normalization techniques. Relevant temporal and frequency-domain features are extracted and encoded into quantum states using parameterized quantum gates. An entanglement-aware featuremapping technique captures relationships between selected features through quantum entangling operations. A Variational Quantum Classifier is then employed to classify brain states using a hybrid quantum-classical optimization process. The adaptive mechanism evaluates different feature maps, entanglement patterns, circuit depths, and training parameters to identify suitable configurations. The proposed framework is evaluated using accuracy, precision, recall, macroF1 score, computational cost, and noise sensitivity. Classical machine learning and deep learning models are used for comparison. Subjectindependent testing assesses the model's ability to classify EEG signals from previously unseen participants. The study aims to investigate the effectiveness and limitations of adaptive quantum computing for EEG brain-state classification. Keywords: Quantum Computing, EEG Classification, Quantum Machine Learning, Entanglement-Aware Feature Mapping, Variational Quantum Classifier, Brain–Computer Interface.