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@article{182434,
        author = {Krish Kumar Gupta and Mamta Kumari and Dr Rajalakshmi},
        title = {Quantum Machine Learning Techniques for Decoding Brainwave Signals},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2351-2356},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182434},
        abstract = {The accurate decoding of brainwave signals is critical for advancing applications in brain-computer interfaces (BCIs), neurological diagnostics, and cognitive state monitoring. Traditional machine learning approaches such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) have demonstrated notable success in EEG signal classification tasks; however, they are often constrained by the need for large, annotated datasets, handcrafted feature engineering, and high computational costs. The emerging field of Quantum Machine Learning (QML) offers new possibilities by leveraging quantum computational principles to process complex, high-dimensional data more efficiently.
In this study, we investigate the use of QML techniques for decoding EEG signals by implementing and comparing four models: Quantum Support Vector Classifier (QSVC), Variational Quantum Classifier (VQC), classical Support Vector Classifier (SVC), and Random Forest. A synthetic EEG-like dataset is generated to simulate brainwave patterns, and models are evaluated based on Accuracy, Precision, Recall, and F1-Score, supported by Confusion Matrix and ROC Curve analyses. Results indicate that while classical models like Random Forest and SVC currently outperform quantum models in accuracy, QML models demonstrate feasibility and offer a foundation for future advancements. The study highlights the potential of quantum approaches in EEG decoding tasks and discusses avenues for further optimization with the evolution of quantum hardware.},
        keywords = {Quantum Machine Learning, EEG Signal Decoding, Brainwave Analysis, QSVC, VQC, Support Vector Classifier, Random Forest, Quantum Computing, ROC Curve, Confusion Matrix.},
        month = {July},
        }