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Advances in computational intelligence and robotics book series

Quantum-Inspired Neurofeedback Mechanism for Stress Detection Using EEG Signals

Divya Singh, G. Senthil Kumar, Sanskar Agrawal

Published Dec 27, 2024• 1 citation
Publisher

Abstract

Stress, is a psychological experience representing the body's response to immune system activation and perceived danger, has emerged as a critical issue, particularly amid the global spread of Covid-19. While various techniques such as PET, ECG, EMG, and MRI exist for stress detection, this paper focuses on leveraging electroencephalography (EEG) signals within a quantum computing framework. We present a comparative analysis of stress detection methods utilizing EEG signals, with a particular emphasis on quantum-inspired approaches. Furthermore, we propose a real-time neurofeedback solution based on quantum computing principles to offer a quantitative assessment of stress levels. By integrating quantum computing concepts into stress detection and management, our research not only contributes to personal health but also extends to applications in stress-related mental health and clinical knowledge. Moreover, it advances the field of neuroethology by exploring the intersection of quantum technology and cognitive health.

DOI
10.4018/979-8-3693-3601-4.ch022

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Stress, is a psychological experience representing the body's response to immune system activation and perceived danger, has emerged as a critical issue, particularly amid the global spread of Covid-19. While various techniques such as PET, ECG, EMG, and MRI exist for stress detection, this paper focuses on leveraging electroencephalography (EEG) signals within a quantum computing framework. We present a comparative analysis of stress detection methods utilizing EEG signals, with a particular emphasis on quantum-inspired approaches. Furthermore, we propose a real-time neurofeedback solution based on quantum computing principles to offer a quantitative assessment of stress levels. By integrating quantum computing concepts into stress detection and management, our research not only contributes to personal health but also extends to applications in stress-related mental health and clinical knowledge. Moreover, it advances the field of neuroethology by exploring the intersection of quantum technology and cognitive health.
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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.
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.
In this chapter the possibility of Quantum Deep Learning as a tool to strengthen the diagnosis of mental disorders, with a particular focus on anxiety and depression, based on EEG data analysis is constructed. The conventional approach of diagnosing mental health disorders is considerably driven by structured interviews and is often very constrained due to variance in symptoms in individuals. As there are advances in mental health studies, EEG has proved useful in recording brain such activity which coincides with emotional and cognitive processes in the study subjects. The problem lies in the fact that EEG signals are complex, high dimensional data and as such they do not temporal and signal-to-noise ratio required for machine learning paradigm to database features. This work applies quantitative deep learning (QDL) to the DEAP dataset to overcome these hurdles, which includes EEG and raw physiological responses of participants watching video clips with emotional content. It utilizes QDL algorithms with emotional states that are classified with the aid of EEG.
Medical Health Informatics has envisaged quantum computing as flurry of promising solutions to deal with mental health related issues of young adults in recent times. Even though traditional machine algorithms try to find an amicable solution to healthcare service providers, still the efficient detection and classification of the highly complex patterns that emerges from the healthcare datasets needs further investigations. Quantum machine learning, inspired from the principle from Quantum computing is found to be a transformative alternative in redefining the healthcare applications. In this article, Quantum inspired Support Vector Machines (QISVMs) and Quantum inspired Neural Networks (QINN) as two efficient quantum machine learning approaches are proposed by combining the traditional yet very popular and powerful machine learning algorithms such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) with the power of quantum computing principles. The proposed Quantum inspired machine learning approaches are evaluated with several figure of merit and then, compared with the performance of the traditional algorithms counterpart for analyzing the most complex nature of the healthcare data with proper identification of minute wise data patterns and biomarkers in order to detect the early-stage healthcare issues pertained to a patient. Experimental study shows the efficacy of these quantum machine learning methods in comparison to the existing literature with threats to validity, some hidden challenges, and ethical issues associated with these technological advancements.