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@article{201909,
        author = {Jaivanthi GV and Dhanushree D and Priyadharshini A and Swetha S and Bhuvanesvari S and Gnanasekar V and Geetha G},
        title = {Neurolink AI Based Consciousness Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {5904-5909},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201909},
        abstract = {Artificial Intelligence and Brain-Computer Interface technologies are transforming the future of human communication and intelligent computing. This project presents an AI-Based Neurolink Thought-to-Text Converter that uses Electroencephalography signals to decode cognitive activity and convert human thoughts into meaningful textual output. The system integrates EEG signal acquisition, preprocessing, feature extraction, deep learning classification, and natural language generation techniques to create a real-time assistive communication framework.
The proposed system focuses on helping individuals suffering from neurological disorders, paralysis, and speech impairments. Deep learning models such as Convolutional Neural Networks, Long Short-Term Memory Networks, and Recurrent Neural Networks are trained using EEG datasets to identify neural activity patterns associated with imagined speech and thought formation.
The framework also incorporates adaptive learning and intelligent postprocessing techniques to improve communication efficiency and prediction accuracy. The proposed solution contributes significantly to healthcare, neuroinformatics, assistive communication, and human-computer interaction. Future improvements can enhance multilingual support, real-time deployment, and integration with smart systems.},
        keywords = {},
        month = {May},
        }