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.
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.
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.
Emotion recognition seeks to identify a person’s state by examining signals such as facial expressions, speech, physiological signals and conversational content. Yet accurate emotion recognition presents obstacles because these signals can be complex, noisy, nonlinear and heavily affected by surrounding context. This paper investigates quantum-based representations combined with machine learning techniques to enhance emotion classification. The research examines emotion sources, including facial expressions, speech, EEG signals and conversational data. It explores how quantum-inspired techniques can merge with established deep learning methods to produce representations. The design presented fuses a neural network with a four qubit ZZFeatureMap component enabling the system to learn data from both conventional and quantum derived feature spaces. Experiments examined EEG segments for each participant and the system achieved average recognition rates of seventy nine point six percent for valence and eighty one point five percent for arousal across ten repeated trials. These findings show that quantum derived features can add information when combined with conventional deep learning methods. Nevertheless because experiments used simulated circuits the results do not prove a performance advantage over conventional techniques on actual quantum computers. The study also highlights difficulties such as quantum noise, computational scalability, experimental assessment, data privacy and real world deployment.
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.
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.
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.
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.