Epileptic seizures are significant challenges in a neurological environment primarily due to the non-stationary and complex nature of the electroencephalogram (EEG) signals. In this paper, we present a Hybrid Quantum-Classical Neural Framework (HQCNF) that leverages quantum computing to support taxonomy of epilepsy seizures with deep neural learning. The framework applies a Continuous Wavelet Transform (CWT) to convert EEG recordings into a time-frequency representation deploying scalograms intended to rigorously scrutinize the features describing oscillations and other elements noted when seizures occur. The model presented in this paper is built from classical architecture and uses quantum-inspired neural layers to support providing the atomic feature representations in order to make sense of discriminability behavior and other elements of interpretable behavior in epistemology and learning. The HQCNF model achieves 99% classification accuracy and provides evidence of enhanced generalization, and outperforms typical deep learning models. The research supports the methodology of hybrid quantum-classical paradigms to move beyond a conventional computing biomedical signal analysis restriction, and the ability to examine infrastructure presented by HQCNF moves us toward real-time assessments and promotes further examination of efforts towards intelligent diagnostic methodologies utilized in neurological disorder management frameworks. The work focuses on the value of quantum-enhanced learning as it relates to EEG-based examination in epilepsy management, and makes advanced hybrid-learning implementations of quantum-enhanced learning that provides for more efficient and reliable accuracy in EEG-based examination of epilepsy signal processing.
Epileptic seizures are significant challenges in a neurological environment primarily due to the non-stationary and complex nature of the electroencephalogram (EEG) signals. In this paper, we present a Hybrid Quantum-Classical Neural Framework (HQCNF) that leverages quantum computing to support taxonomy of epilepsy seizures with deep neural learning. The framework applies a Continuous Wavelet Transform (CWT) to convert EEG recordings into a time-frequency representation deploying scalograms intended to rigorously scrutinize the features describing oscillations and other elements noted when seizures occur. The model presented in this paper is built from classical architecture and uses quantum-inspired neural layers to support providing the atomic feature representations in order to make sense of discriminability behavior and other elements of interpretable behavior in epistemology and learning. The HQCNF model achieves 99% classification accuracy and provides evidence of enhanced generalization, and outperforms typical deep learning models. The research supports the methodology of hybrid quantum-classical paradigms to move beyond a conventional computing biomedical signal analysis restriction, and the ability to examine infrastructure presented by HQCNF moves us toward real-time assessments and promotes further examination of efforts towards intelligent diagnostic methodologies utilized in neurological disorder management frameworks. The work focuses on the value of quantum-enhanced learning as it relates to EEG-based examination in epilepsy management, and makes advanced hybrid-learning implementations of quantum-enhanced learning that provides for more efficient and reliable accuracy in EEG-based examination of epilepsy signal processing.
Automated epileptic seizure detection from electroencephalogram (EEG) recordings remains a challenging biomedical signal analysis task because of the nonlinear, non-stationary, and high-dimensional characteristics of neural activity. Although deep learning has substantially improved automated seizure classification, conventional architectures may exhibit limited capability in modeling complex latent feature interactions while maintaining computational efficiency in biomedical applications. This study presents an entanglement-driven hybrid quantum–classical representation learning framework that integrates Continuous Wavelet Transform (CWT)-based time–frequency analysis, EfficientNet-B0 feature extraction, quantum-compatible latent feature compression, angle-based quantum state encoding, and a customized four-qubit Variational Quantum Convolutional Neural Network (QCNN) for EEG seizure classification. The proposed shallow variational quantum architecture is designed to facilitate expressive quantum feature learning while remaining compatible with the computational constraints of near-term noisy intermediate-scale quantum (NISQ) systems. An experimental evaluation was conducted on a balanced subset of 4,000 EEG segments from the UCI Epileptic Seizure Recognition Dataset. Stratified five-fold cross-validation achieved a mean classification accuracy of 97.80% ± 1.50%, demonstrating consistent predictive performance and generalization across several data partitions. Furthermore, evaluation on an independent held-out test set yielded 99.50% accuracy, 99.03% precision, 100.00% recall, 99.51% F1-score, and an area under the ROC curve (AUC) of 1.000. To comprehensively assess the proposed framework, additional investigations, including quantum circuit expressibility, barren plateau analysis, optimization trainability, latent feature-space separability, robustness evaluation, computational scalability, and patch-level quantum attention visualization, were performed, offering complementary evidence regarding representation quality, optimization stability, and model interpretability. Although the proposed framework is currently validated using classical quantum simulation, the results demonstrate the feasibility of hybrid quantum–classical representation learning for intelligent EEG seizure analysis and establish a foundation for future deployment on emerging quantum-computing platforms.
Electroencephalography (EEG) provides a powerful non-invasive modality for capturing millisecond-scale brain dynamics, yet traditional analysis pipelines often rely on hand-crafted features and classical models with limited ability to capture nonlinear dependencies. This study introduces Q-EEG, a novel quantum-enhanced computational framework for brain state classification. The proposed approach integrates classical pre-processing with quantum machine learning to exploit the representational capacity of quantum circuits. Raw EEG signals are first band-pass filtered into canonical rhythms—delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–100 Hz)—and artefact removal is performed using ICA. Average band powers are then computed via the PSD and assembled into feature vectors. These normalized features are embedded in quantum states via rotation gates, forming the initial input to a VQC. The VQC models cross-frequency relationships using parameterised rotating layers and entangling operations, with weights trained using hybrid classical–quantum training with the parameter-shift rule and gradient-based optimisers. The computational basis classifies via measurements and evaluates the framework on seizure-versus-non-seizure detection tasks. Compared to SVM and Random Forest, accuracy, precision, recall, and F1-score are measured. Experimental results show that quantum-enhanced EEG analysis may capture complex brain patterns, enabling scalable, interpretable, and clinically useful diagnostic systems. This study shows that Q-EEG can bridge neuroscience with quantum computing, enabling clinical translation.
Epilepsy, a neurological disorder characterized by recurrent and unpredictable seizures, demands timely and accurate detection for effective management and patient safety. While traditional machine learning approaches have shown promise in seizure detection, this study explores the potential of quantum computing, specifically utilizing a Variational Quantum Classifier. The Variational Quantum Classifier, grounded in quantum mechanics, aims to enhance computational capabilities for seizure detection. Leveraging quantum computing’s intrinsic parallelism and superposition properties, the model focuses on processing complex and high-dimensional Electroencephalogram signals commonly used in epilepsy diagnosis. The model undergoes training on a dataset containing both seizure and non-seizure instances, optimizing parameters for increased accuracy. Performance evaluations utilize metrics such as sensitivity, specificity, and overall classification accuracy.
Quantum machine learning (QML) is an emerging research field, which is devoted to devising and implementing quantum algorithms that could enable machine learning faster than that of classical computers. In this article, a hierarchic quantum mechanics-based framework is investigated to implement both the feature extraction and classification in the electroencephalogram (EEG) signal. First, the classical EEG signal dataset is prepared as a quantum state while the sign of the data point is preserved. The prepared quantum state is then evolved with the quantum wavelet packet transformation (QWPT) and the wavelet packet energy entropy (WPEE) feature is extracted as the input of the subsequent quantum classifier. We finally propose the improved quantum support vector machine with the arbitrary nonlinear kernel, which is employed to predict the label of the EEG signal. The complexity analysis indicates that the proposed framework provides exponential speedup over the same structured classical counterpart. Besides, the quantitative experimental results verify the feasibility and validity.
Parkinson's disease (PD) is a progressive neuro-degenerative condition with an urgent need for timely and pinpoint diagnosis for adequate treatment. Despite potential for prediction with traditional machine learning techniques, PD data remains difficult to solve for traditional machine learning techniques due to the high computational complexity and the nature of data. We introduce in this work a Hybrid Quantum-Classical AI Framework that combines advantages of QC and provide benefits in predictive accuracy as well as computational efficiency for real time Parkinson's disease detection. We use classical deep learning models in conjunction with Variational Quantum Circuits (VQC) to perform efficient feature extraction and classification of biomedical signals: speech and its recordings, gait analysis, and EEG data. We evaluate the performance of our proposed framework against deep learning models that are standard and based on the accuracy, precision, recall and processing time. Our experimental results suggest that our hybrid quantum classical model outperforms classical methods for both accuracy and efficiency, demonstrating the potential of quantum machine learning (QML) in healthcare. This work gives a taste of the quantum revolution realized in medical diagnostics and paves the way for what could in the future be quantum enhanced disease prediction.
This investigation presents a novel methodological framework for affective computing. Emotional processes exert substantial influence on cognitive decision-making mechanisms, brain-computer interface efficacy, and social intelligence paradigms, thereby establishing emotion recognition as a critical domain of scientific inquiry. Electroencephalographic (EEG) signals have garnered considerable scholarly attention due to their capacity to capture neurological activity with exceptional temporal resolution. However, the inherent complexity of these signals and significant inter-individual variability present substantial challenges in the development of accurate and generalizable computational models. To address these methodological limitations, this study proposes an innovative hybrid quantum-classical framework-the Quantum Deep Adaptation Network (QDAN)-for emotion recognition utilizing EEG signal analysis. This architectural approach strategically leverages the computational advantages inherent in quantum computing, particularly the fundamental quantum mechanical principles of superposition and entanglement, to enhance pattern recognition capabilities in high-dimensional EEG data representations. We employ sophisticated domain adaptation techniques to effectively mitigate variability across experimental sessions. The proposed methodological framework undergoes rigorous empirical validation utilizing the standardized SEED dataset. Experimental findings reveal significant performance heterogeneity across subjects and sessions, with the model achieving a maximum classification accuracy of 73.91 %. These results demonstrate the potential efficacy of quantum-enhanced computational approaches in addressing the complex challenges associated with EEG-based emotion recognition systems.