Artificial Intelligence advancements in neural signal processing and neurotechnology: Volume II

Advancements in Artificial Intelligence (AI) have paved the way for ground-breaking applications in the field of neuroscience, particularly in the realm of neural signal processing and neurotechnology. Neural signals—such as electroencephalography (EEG), electrocorticogram (ECoG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and single-unit recordings—provide valuable insights into brain function, enabling researchers and clinicians to better understand both neural activity and pathologies.AI techniques, including Machine Learning (ML), Deep Learning (DL), and Data Analytics, have revolutionized neural signal processing by enhancing the accuracy, speed, and efficiency of data analysis. These advancements allow for the extraction of meaningful information from complex and noisy neural signals, leading to improved diagnosis, treatment, and understanding of neurological and psychiatric disorders.The integration of AI with neurotechnology has also opened new doors for the development of:• Assistive technologies• Brain-computer interfaces (BCIs)• Neuromodulation techniques• NeuroprostheticsAI algorithms can enhance the performance and adaptability of these devices, enabling precise control, interpretation, and translation of neural signals into actionable commands or feedback.This Topic addresses the forefront of AI and neurotechnology integration, aiming to catalyze advancements in leveraging neural signals. By bringing together researchers, engineers, and practitioners, we intend to foster collaboration and discussions that will propel the field forward. The exploration of EEG, ECoG, MEG, fMRI, and single-unit recording methods within the context of AI is pivotal for unravelling the full potential of these technologies, leading to transformative breakthroughs in neuroscience and neuroengineering.Key Areas of Focus:• AI Integration in Neural Signal Processing: Exploring remarkable advancements and unprecedented opportunities for the field of Brain-Computer Interfaces.• Novel Algorithms: Novel approaches for the processing and analysis of ECoG and MEG data.• Real-time Processing: Investigating methods for real-time signal processing and feature extraction to enable rapid interpretation of neural activity.• Machine Learning for Signal Decoding: State-of-the-art ML techniques for decoding complex signals, focusing on accuracy and real-time efficiency.• Neurophysiological Biomarkers: The role of AI in identifying diagnostic biomarkers from ECoG and MEG data.• Classification and Prediction: ML models for the classification and prediction of neurological disorders based on neural signal patterns.• Adaptive BCI Systems: Developing adaptive BCIs that leverage AI to tailor interfaces to individual users, enhancing usability over time.• AI-Driven Neurofeedback: Utilizing AI algorithms in the development of adaptive neurofeedback systems.• Neurotechnology Applications: Innovations in prosthetics, rehabilitation, and assistive devices to improve patient outcomes and quality of life.• Personalized Treatment: AI-based strategies for personalizing treatment plans based on individual neural profiles.• Interpretability and Ethics: Methods for enhancing the transparency of AI models to ensure responsible and ethical use in clinical and research settings. Topic editors: Siamak Pedrammehr, Hamid Rajebi, Roohallah Alizadehsani, Ali Jafarizadeh Source: https://www.frontiersin.org/research-topics/80494/artificial-intelligence-advancements-in-neural-signal-processing-and-neurotechnology-volume-ii