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.
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.
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.
Emotion recognition in speech is essential for human-computer interaction. This study introduces a refined hybrid Quantum Convolutional Neural Network (QCNN) framework designed to substantially enhance Speech Emotion Recognition (SER). It addresses major challenges such as speaker variability, noise robustness, and cross-domain applicability that often limit classical deep learning models. By Utilizing quantum principles like superposition and entanglement within a 6-qubit, 8-layer parameterized circuit, the model derives highdimensional, noise-resistant features from speech signals. The method includes sophisticated preprocessing steps, such as bandpass filtering and energy-based Voice Activity Detection (VAD), followed by Mel-Frequency Cepstral Coefficients (MFCC) extraction. Evaluated on a diverse dataset of 4,515 samples across eight emotions (Angry, Calm, Disgust, Fear, Happy, Neutral, Sad, Surprised), the QCNN achieved an accuracy of 86 %, setting a new benchmark. It surpasses classical approaches such as a CNN with attention(77%), a traditional QCNN(77.87%), and a hybrid quantum-classical network(76%). The model demonstrated balanced performance, with precision scores ranging from 0.79 to 1.0 (including a perfect 1.0 for Fear) and recall scores from 0.82 to 0.94. These results underscore the promise of quantum-enhanced neural networks in capturing complex, nonlinear speech emotion patterns and establishing a new standard for robust, generalizable SER systems.
Quantum machine learning (QML) offers a promising avenue for advancing representation learning in complex signal domains. In this study, we investigate the use of parameterised quantum circuits (PQCs) for speech emotion recognition (SER)-a challenging task due to the subtle temporal variations and overlapping affective states in vocal signals. We propose a hybrid quantum-classical architecture that integrates PQCs into a conventional convolutional neural network (CNN), leveraging quantum properties such as superposition and entanglement to enrich emotional feature representations. Experimental evaluations on three benchmark datasets IEMOCAP, RECOLA, and MSP-IMPROV-demonstrate that our hybrid model achieves improved classification performance relative to a purely classical CNN baseline, with over 50% reduction in trainable parameters. Furthermore, Adjusted Rand Index (ARI) analysis demonstrates that the quantum model yields feature representations with improved alignment to true emotion classes compared with the classical model, reinforcing the observed performance gains. This work provides early evidence of the potential for QML to enhance emotion recognition and lays the foundation for future quantum-enabled affective computing systems.
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.