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.
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 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.
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.
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.
Emotion Recognition (ER) is an important capability that is increasingly being used to enhance the user experience in various fields such as, Human-Computer interaction, Cognitive Neuroscience, Neurofeedback, Robotics, Artificial Intelligence, Augmented Reality, Virtual Reality, Mental Health, etc. Emotions are generated in the limbic system, a set of brain structures that are involved in emotional processing, memory, and motivation. ER from electroencephalography (EEG) signals has gained attention from researchers in recent years as it provides a more accurate and non-intrusive way of understanding human emotions. This research paper presents a comprehensive study of the use of Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) for ER using EEG signals. The aim of this study is to investigate the potential of quantum machine learning algorithms in improving the performance of ER tasks compared to classical Machine Learning (ML) algorithms. The model was simulated using IBM Qiskit due to the limited resources of quantum computers. Intriguingly, the experimental results contradict the speed-up claims of Quantum Computing (QC) by exhibiting longer computation times and lesser accuracy when compared to traditional methods. SVM and NN yield 68.5% and 69.2% accuracy while the quantum version gives 63% and 65.2% accuracy on the benchmark dataset SEED. This research provides evidence that Quantum Machine Learning (QML) algorithms need to be improved for ER from EEG signals and provide a reliable and non-intrusive way of understanding human emotions.
A brain-computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human-computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a Quantum Support Vector Machine (QSVM) with a Quantum Neural Network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for next-generation neurotechnological systems.
Quantum computing has recently inspired many applications in machine learning. In this paper, we present a novel idea of combining neuromorphic computing and quantum computing to develop an advanced application for studying and analysing brain signals. We integrate a quantum spiking leaky integrate-and-fire (QLIF) neuron into a 3D spiking neural network framework designed for analysing and classifying biological data. This modified model is applied to EEG brain signals obtained from experimental measurements of a subject's wrist movements, providing a real-world demonstration of the model's capabilities. The proof-of-concept results show that the QLIF model outperforms its classical counterpart across a series of binary classification tasks, suggesting the promising compatibility of quantum neuromorphic algorithms for the spatio-temporal EEG dataset. Our illustration offers a new direction for enhancing the advantages of quantum computing in complex machine learning applications.