About this Research Topic
LLMs, powered by advanced natural language processing capabilities, enable researchers to analyze vast amounts of neuroscience literature, uncovering patterns and generating hypotheses. These models also assist in interpreting neuroimaging data by extracting contextual information, offering novel avenues for exploration and understanding. Additionally, BCI-based solutions are gaining traction in the industrial sector, where they enhance performance and safety in critical operations.
Despite the advancements, BCIs face challenges in real-world applications, such as recognizing accurate human mental states and emotions. Novel machine or deep learning models are required to overcome these issues and advance the field. Efforts are directed towards developing intelligent hardware, software, and devices with human-like intelligence, fostering an AI-focused industrial ecosystem.
This Research Topic invites contributions based on, but not limited to:
-Psychological, cognitive, and behavioral impacts of AI and LLM interactions with humans.
-AI applications in analyzing neuroscience, brain imaging, and therapeutics literature.
-Ethical considerations in AI and LLM use in neuroscience, including privacy and fairness.
-Intelligent brain signal processing and reinforcement learning in BCI.
-Industrial BCI applications and novel paradigms in Industry 4.0.
-Affective BCI in emotion and mental state recognition.
-Neuroergonomics and mutual learning in human-machine interaction.
Submissions of original research, reviews, mini-reviews, editorials, commentaries, study protocols, and case reports are welcome to explore these intersections, advancing understanding and innovation in AI, LLMs, BCI, and human research.
Keywords: Large Language Models, Artificial Intelligent, Natural Language Processing
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.