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@article{190044,
        author = {Keerti Mole and Noor Fathima and Sneha L B and Pavithra T J and Shreyan Jain},
        title = {Quantum Machine Learning for Predicting Brain computer Interface Signals},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {3944-3950},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190044},
        abstract = {Brain–Computer Interface systems enable communication between the human brain and external devices by analyzing electroencephalogram signals. However, accurate prediction of brain states remains challenging due to the high dimensionality, noise, and non-linear characteristics of brain signals. Traditional machine learning approaches often face limitations in capturing complex patterns present in electroencephalogram data. This paper presents a Quantum Machine Learning based framework for predicting brain–computer interface signals using a hybrid classical and quantum approach. The proposed system processes electroencephalogram data stored in European Data Format files through signal preprocessing, feature extraction, and dimensionality reduction techniques. Classical optimization methods are combined with quantum-enhanced feature encoding and kernel-based classification to improve prediction accuracy. Experimental results demonstrate that the proposed approach achieves superior performance compared to classical machine learning models, highlighting the potential of quantum machine learning for advanced brain–computer interface applications in healthcare and cognitive monitoring},
        keywords = {Brain computer interface, EEG signals, hybrid quantum-classical learning, quantum machine learning.},
        month = {January},
        }