Stress and emotion monitoring via EEG is a critical task in human-centered neurotechnology, whereas existing machine learning systems often face challenges regarding high computational cost and limited explainability. In this paper, an Explainable Quantum EEG Analysis (EQEA) framework is presented for real-time stress detection by seamlessly integrating quantum-inspired feature modeling with interpretable classification. Multi-channel EEG data are encoded using a Quantum Variational Fusion Network that captures nonlinear spectral-temporal relationships via variational quantum circuits to produce compact feature embeddings. These representations are then classified by the Dual-Stage Hybrid Quantum Classifier that aggregates quantum decision boundary with lightweight deep-learning refinement in order to reduce the computational overhead. The explainability module, Explainable Entangled Feature Attribution, adopts SHAP-based entanglement attribution to identify critical EEG channels and frequency bands that contribute to stress prediction. Experimental results on multi-subject EEG datasets showed 94.8% accuracy, 4 times reduction in feature dimensionality, and 32% lower computation time compared to classical CNN and SVM models. The proposed EQEA framework delivers an interpretable and scalable solution for real-time stress assessment in wearable neuro-monitoring and brain-computer interface applications.
Stress and emotion monitoring via EEG is a critical task in human-centered neurotechnology, whereas existing machine learning systems often face challenges regarding high computational cost and limited explainability. In this paper, an Explainable Quantum EEG Analysis (EQEA) framework is presented for real-time stress detection by seamlessly integrating quantum-inspired feature modeling with interpretable classification. Multi-channel EEG data are encoded using a Quantum Variational Fusion Network that captures nonlinear spectral-temporal relationships via variational quantum circuits to produce compact feature embeddings. These representations are then classified by the Dual-Stage Hybrid Quantum Classifier that aggregates quantum decision boundary with lightweight deep-learning refinement in order to reduce the computational overhead. The explainability module, Explainable Entangled Feature Attribution, adopts SHAP-based entanglement attribution to identify critical EEG channels and frequency bands that contribute to stress prediction. Experimental results on multi-subject EEG datasets showed 94.8% accuracy, 4 times reduction in feature dimensionality, and 32% lower computation time compared to classical CNN and SVM models. The proposed EQEA framework delivers an interpretable and scalable solution for real-time stress assessment in wearable neuro-monitoring and brain-computer interface applications.
Applications in behavioural research, human-computer interaction, and mental health depend on the ability to recognize emotions. In order to improve the accuracy of emotion recognition using electroencephalography (EEG) data, this work presents a hybrid quantum deep learning technique. Conventional EEG-based emotion recognition techniques are limited by noise and high-dimensional data complexity, which make feature extraction difficult. To tackle these issues, our method combines traditional deep learning classification with quantum-enhanced feature extraction. To identify important brain wave patterns, Bandpass filtering and Welch method are used as preprocessing techniques on EEG data. Intricate inter-band interactions that are essential for determining emotional states are captured by mapping frequency band power attributes (delta, theta, alpha, and beta) to quantum representations. Entanglement and rotation gates are used in a hybrid quantum circuit to maximize the model's sensitivity to EEG patterns associated with different emotions. Promising results from evaluation on a test dataset indicate the model's potential for accurate emotion recognition. The model will be extended for real-time applications and multi-class categorization in future study, which could improve EEG-based mental health screening instruments. This method offers a promising tool for applications in adaptive human-computer systems and mental health monitoring by showcasing the possibilities of fusing traditional deep learning with quantum processing for reliable, scalable emotion recognition.
Abstract Mental stress detection through electroencephalography (EEG) signals faces difficulties because EEG signals display non-stationary behavior and are vulnerable to artifact interference. The research introduces a hybrid deep-quantum learning framework which combines a one-dimensional convolutional neural network (1D-CNN) with a quantum support vector classifier (QSVC) to differentiate between stress and calm states. The CNN functions as a trainable feature extractor which converts raw EEG time-series data into compressed latent representations that undergo mapping to a quantum Hilbert space through ZZFeatureMap before being classified by a quantum kernel. The research demonstrates through experiments on a multi-subject EEG dataset that the hybrid model reaches its highest accuracy of (81.00%) (F1: 0.81) under optimal configuration using 8-dimensional features with MinMax scaling to [0,1] and 1000 training samples because it surpasses both the classical SVM (76.80%) and end-to-end CNN (78.85%) baselines. The analysis demonstrates that quantum kernel performance depends on feature dimensionality and scaling because lower dimensions (F-4) lack expressivity while higher dimensions (F-12) reach a performance limit and scaling to [-1,1] causes accuracy loss of 16 percentage points when compared to [0,1] scaling. The quantum classifier shows good sample efficiency because it gains more from additional training data compared to the traditional model. The research demonstrates that hybrid deep-quantum architectures function in the NISQ era while creating a performance standard for quantum-based physiological signal processing.
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.
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.
According to the World Health Organization, cardiovascular diseases are responsible for several million deaths worldwide each year, thus highlighting a prime necessity for developing methods of diagnosis that are accurate and rapid. Although recent advancements in quantum-enhanced machine learning have demonstrated enhanced prediction performance, current research is still limited by the reliance on static datasets, the lack of interpretability, and the lack of privacy-preserving mechanisms for data sharing across institutions. In this paper, we introduce an Explainable Quantum-Enhanced Federated Learning Framework for Real-Time Cardiovascular Risk Assessment that integrates the scalability of federated learning with the computational benefits of quantum models to allow multiple healthcare centers and remote devices to train models collaboratively without sharing raw patient data. Quantum-inspired classifiers such as variational circuits and Quantum SVM are integrated into the federated pipeline to accelerate convergence and improve robustness. An explanation layer was implemented to enhance clinical trust by applying changed feature attribution methods that help the physicians comprehend how much the important factors like cholesterol, blood pressure, and ECG patterns contribute to the risk predictions. In addition, the system is meant for constant watch by the mixing of electronic health records and wearable sensor streams, thus making it possible to switch from one-time prediction to dynamically and personally tailored risk assessment. The proposed technique has been validated through experiments on standard heart disease datasets where it is also said to have done the opposite: it has improved predictive accuracy, reduced communication overhead compared to traditional federated systems, and provided insights that are ready for supporting clinical decision-making in real practice. Consequently, this framework is a first step towards quantum-driven healthcare solutions that are safe, scalable, and explainable.
In recent years, critical infrastructure systems—from smart grids to industrial automation platforms to cyber-physical environments—have become increasingly susceptible to failures, cyber attacks, and equipment deterioration. Many Machine Learning (ML) and Deep Learning (DL) models achieve high predictive accuracy but are difficult to interpret and require significant computational resources to deploy in safety-critical applications. The study introduced a new Explainable Quantum Artificial Intelligence (XQAI) framework for predicting failures in critical infrastructures, combining Quantum Adaptive Feature Embedding (QAFE), Variational Quantum Circuits (VQC), Bidirectional LSTM temporal learning, Quantum Attention mechanisms, and SHAP-based explainability analysis. The proposed framework leveraged heterogeneous data collected from Industrial Internet of Things (IIoT) sensors and SCADA systems to detect complex nonlinear patterns in infrastructure failures. The proposed XQAI framework was evaluated experimentally and demonstrated a very high accuracy of 98.9%, precision of 98.5%, recall of 98.1%, and F1-score of 98.3%, which are significantly higher than those of conventional machine learning, deep learning, and quantum learning models. The explainability module accurately identified the parameters that influenced infrastructure failure, particularly temperature, vibration, voltage instability, and communication latency. The results showed that integrating Explainable Artificial Intelligence (XAI) with quantum-augmented temporal learning had a remarkable impact on prediction reliability, interpretability, and computational efficiency. The proposed framework offers an intelligent infrastructure-monitoring solution that is scalable and transparent for the next-generation safety-critical industrial environment.