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.
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.
Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for monitoring and analyzing brain activity. Traditional neural network models, such as EEG-Net, have achieved considerable success in decoding EEG signals but often struggle with the complexity and high dimensionality of the data. Recent advances in quantum computing present new opportunities to enhance machine learning models through quantum machine learning (QML) techniques. In this paper, we introduce Quantum-EEGNet (QEEGNet), a novel hybrid neural network that integrates quantum computing with the classical EEGNet architecture to improve EEG encoding and analysis, as a forward-looking approach, acknowledging that the results might not always surpass traditional methods but it shows its potential. QEEGNet incorporates quantum layers within the neural network, allowing it to capture more intricate patterns in EEG data and potentially offering computational advantages. We evaluate QEEGNet on a benchmark EEG dataset, BCI Competition IV 2a, demonstrating that it consistently outperforms traditional EEG-Net on most of the subjects and other robustness to noise. Our results highlight the significant potential of quantum-enhanced neural networks in EEG analysis, suggesting new directions for both research and practical applications in the field.
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.
In neuroscience and therapeutic practice, electroencephalography (EEG) is a vital instrument for tracking and analysing brain activity. While traditional neural network models, like EEG-Net, have made significant progress in interpreting EEG signals, they frequently encounter difficulties due to the great dimensionality and complexity of the data. Quantum machine learning (QML) techniques offer new ways to improve machine learning models, thanks to recent developments in quantum computing. As a forward-looking approach, we present Quantum-EEG Net (QEEG Net), a novel hybrid neural network that combines quantum computing with the classical EEG Net architecture to improve EEG encoding and analysis. While the results may not always outperform conventional methods, it demonstrates its potential. In order to capture more complex patterns in EEG data and maybe provide computational benefits, QEEG Net integrates quantum layers into the neural network. Using the benchmark EEG dataset, BCI Competition IV 2a, we test QEEG Net and show that it consistently performs better than standard EEG-Net on the majority of participants and has other robustness to noise.
Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as EEGNet have shown success in decoding EEG signals, they often struggle with data complexity, inter-subject variability, and noise robustness. Recent advancements in quantum machine learning (QML) offer new opportunities to enhance EEG analysis by leveraging quantum computing’s unique properties. In this study, we extend the previously proposed Quantum-EEGNet (QEEGNet), a hybrid neural network incorporating quantum layers into EEGNet, to investigate its generalization ability across multiple EEG datasets. Our evaluation spans a diverse set of cognitive and motor task datasets, assessing QEEGNet’s performance in different learning scenarios. Experimental results reveal that while QEEGNet demonstrates competitive performance and maintains robustness in certain datasets, its improvements over traditional deep learning methods remain inconsistent. These findings suggest that hybrid quantum-classical architectures require further optimization to fully leverage quantum advantages in EEG processing. Despite these limitations, our study provides new insights into the applicability of QML in EEG research and highlights challenges that must be addressed for future advancements.
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.
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.