Future Directions for Brain-Computer Interfaces and Artificial Intelligence
Disciplines
Artificial Intelligence and Robotics | Bioelectrical and Neuroengineering | Technology and Innovation
Abstract (300 words maximum)
Abstract
Brain-computer interfaces (BCIs) have revolutionized human-computer interaction by enabling direct communication between the brain and external devices. The integration of artificial intelligence (AI) has significantly improved BCI performance in decoding neural signals, enhancing accuracy, speed, and adaptability. This paper explores advancements in AI-powered BCIs, highlighting machine learning techniques, multimodal fusion, and healthcare applications. It also identifies challenges such as data limitations, ethical concerns, and computational constraints. A future research agenda emphasizes hybrid AI models, real-time processing, and explainable frameworks for the broader adoption of AI-driven BCIs.
Introduction & Context
Brain-computer interfaces (BCIs) enable direct interaction between the human brain and external systems, offering applications in neuroprosthetics, cognitive enhancements, and assistive technologies. AI integration has significantly advanced BCI technology by improving neural signal decoding, reducing noise, and enabling real-time processing. BCIs leverage various neural recording techniques, including EEG, fMRI, and ECoG, which are augmented by AI-driven methods such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and hybrid models. These advancements facilitate applications in neurorehabilitation and mental health interventions. However, challenges like data variability, ethical concerns, and computational constraints remain critical hurdles.
Research Question
What are the advancements, challenges, and future directions in AI-powered BCIs for improving decoding accuracy and enhancing practical applications?
Methodology
This study adopts a systematic literature review (2018–2025) using sources like IEEE and PubMed. Key dimensions analyzed include performance metrics (accuracy, latency, adaptability), challenges (data limitations, computational complexity, ethics), and emerging trends (hybrid models, explainable AI).
Conclusion
AI-powered BCIs are transforming neurorehabilitation and assistive technologies. Overcoming barriers like data scarcity, ethical challenges, and computational demands is essential. Future research should emphasize hybrid AI models, real-time capabilities, dataset standardization, and ethical frameworks to enhance trust and usability.
Academic department under which the project should be listed
CCOB - Information Systems and Securty
Primary Investigator (PI) Name
Adriane B. Randolph
Future Directions for Brain-Computer Interfaces and Artificial Intelligence
Abstract
Brain-computer interfaces (BCIs) have revolutionized human-computer interaction by enabling direct communication between the brain and external devices. The integration of artificial intelligence (AI) has significantly improved BCI performance in decoding neural signals, enhancing accuracy, speed, and adaptability. This paper explores advancements in AI-powered BCIs, highlighting machine learning techniques, multimodal fusion, and healthcare applications. It also identifies challenges such as data limitations, ethical concerns, and computational constraints. A future research agenda emphasizes hybrid AI models, real-time processing, and explainable frameworks for the broader adoption of AI-driven BCIs.
Introduction & Context
Brain-computer interfaces (BCIs) enable direct interaction between the human brain and external systems, offering applications in neuroprosthetics, cognitive enhancements, and assistive technologies. AI integration has significantly advanced BCI technology by improving neural signal decoding, reducing noise, and enabling real-time processing. BCIs leverage various neural recording techniques, including EEG, fMRI, and ECoG, which are augmented by AI-driven methods such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and hybrid models. These advancements facilitate applications in neurorehabilitation and mental health interventions. However, challenges like data variability, ethical concerns, and computational constraints remain critical hurdles.
Research Question
What are the advancements, challenges, and future directions in AI-powered BCIs for improving decoding accuracy and enhancing practical applications?
Methodology
This study adopts a systematic literature review (2018–2025) using sources like IEEE and PubMed. Key dimensions analyzed include performance metrics (accuracy, latency, adaptability), challenges (data limitations, computational complexity, ethics), and emerging trends (hybrid models, explainable AI).
Conclusion
AI-powered BCIs are transforming neurorehabilitation and assistive technologies. Overcoming barriers like data scarcity, ethical challenges, and computational demands is essential. Future research should emphasize hybrid AI models, real-time capabilities, dataset standardization, and ethical frameworks to enhance trust and usability.