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@article{182057,
        author = {Ankita Roy and Abhishek Kumar Kashyap},
        title = {Artificial Intelligence in Signal Acquisition for EEG-Based Brain-Computer Interfaces},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {621-630},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182057},
        abstract = {Brain-Computer Interfaces (BCIs) have become an important technology for enabling direct communication between the human brain and external devices. However, their practical use has faced challenges due to the low accuracy and reliability of interpreting brain signals. Signal acquisition is essential to BCI systems, especially those that rely on non-invasive Electroencephalography (EEG). Yet, issues like noise interference, signal variability, and hardware limitations disrupt effective interpretation of brain signals. This paper looks at the important role of Artificial Intelligence (AI) in improving the EEG signal acquisition process. AI methods like deep learning, reinforcement learning, and adaptive sampling are changing how we enhance signals, remove artifacts, optimize electrodes, and assess quality in real time. We provide a review of the methods, structures, and advantages related to AI-driven signal acquisition. We conclude that smart acquisition systems are a crucial step toward creating real-time, high-accuracy, and user-friendly BCI technology.},
        keywords = {Artificial Intelligence (AI), Brain-Computer Interface (BCI), Electroencephalography (EEG), Signal Acquisition, Machine Learning in BCI, Signal Enhancement.},
        month = {July},
        }