The human brain is highly dynamic and complex, supporting a remarkable range of functions by dynamically integrating, and coordinating different brain regions and networks across multiple spatial and temporal scales. Research on the human brain has become truly interdisciplinary involving medicine, neurobiology, engineering, and related fields. Research is focused on both bottom-up and top-down ways to explore the neurophysiological mechanisms underlying higher brain functions by extracting meaningful information from recordings of brain electrophysiological activity. Non-invasive brain stimulation has shown promising results across a range of neurological and neuropsychiatric disorders due to its high spatiotemporal precision.
Recent advances in data acquisition and developments in artificial intelligence (AI) have accelerated data-driven investigations. Fruitful contributions to our knowledge about brain dynamics have been achieved. For example, deep neural networks facilitated by transfer learning and attention mechanism have significantly improved the decoding accuracy in brain-computer interfaces. However, intra- and inter-subject variability due to the noisy, nonstationary, nonlinear, high-dimensional EEG time series is still a major challenge. And a thorough understanding of the mechanisms of neuromodulation actions is urgently needed for stimulation parameters optimization, response prediction, and consistent therapy.
This Research Topic aims to combine the top-down and bottom-up methods to produce robust results which allow a meaningful interpretation in terms of the underlying brain dynamics with an emphasis on brain decoding and neuromodulation. Gather the efforts of experts from various disciplines and publish original research articles covering advances in the theory, model, and technical aspects of brain dynamics, especially the studies based on EEG signals. Diverse information processing analysis, modeling techniques, nonlinear dynamics, control theories, and AI methods will be discussed to study the statistical and dynamical properties of brain activities and brain neuromodulation.
This Research Topic explores the advances, challenges, and prospects of brain decoding and neuromodulation techniques. This Research Topic is focused on novel methodological approaches or applications of related new methods including validation studies. Topics include but are not limited to the following:
• Brain signal processing and brain imaging
• Brain dynamics and connectivity
• Deep learning for brain decoding
• Brain-computer interfaces
• Neural rehabilitation
• AI aided diagnosis of brain disorders
• Neurofeedback and neuromodulation
• Novel paradigms of neurometry and sensory stimulation
• Brain-inspired intelligence
• Human-machine hybrid-augmented intelligence
The human brain is highly dynamic and complex, supporting a remarkable range of functions by dynamically integrating, and coordinating different brain regions and networks across multiple spatial and temporal scales. Research on the human brain has become truly interdisciplinary involving medicine, neurobiology, engineering, and related fields. Research is focused on both bottom-up and top-down ways to explore the neurophysiological mechanisms underlying higher brain functions by extracting meaningful information from recordings of brain electrophysiological activity. Non-invasive brain stimulation has shown promising results across a range of neurological and neuropsychiatric disorders due to its high spatiotemporal precision.
Recent advances in data acquisition and developments in artificial intelligence (AI) have accelerated data-driven investigations. Fruitful contributions to our knowledge about brain dynamics have been achieved. For example, deep neural networks facilitated by transfer learning and attention mechanism have significantly improved the decoding accuracy in brain-computer interfaces. However, intra- and inter-subject variability due to the noisy, nonstationary, nonlinear, high-dimensional EEG time series is still a major challenge. And a thorough understanding of the mechanisms of neuromodulation actions is urgently needed for stimulation parameters optimization, response prediction, and consistent therapy.
This Research Topic aims to combine the top-down and bottom-up methods to produce robust results which allow a meaningful interpretation in terms of the underlying brain dynamics with an emphasis on brain decoding and neuromodulation. Gather the efforts of experts from various disciplines and publish original research articles covering advances in the theory, model, and technical aspects of brain dynamics, especially the studies based on EEG signals. Diverse information processing analysis, modeling techniques, nonlinear dynamics, control theories, and AI methods will be discussed to study the statistical and dynamical properties of brain activities and brain neuromodulation.
This Research Topic explores the advances, challenges, and prospects of brain decoding and neuromodulation techniques. This Research Topic is focused on novel methodological approaches or applications of related new methods including validation studies. Topics include but are not limited to the following:
• Brain signal processing and brain imaging
• Brain dynamics and connectivity
• Deep learning for brain decoding
• Brain-computer interfaces
• Neural rehabilitation
• AI aided diagnosis of brain disorders
• Neurofeedback and neuromodulation
• Novel paradigms of neurometry and sensory stimulation
• Brain-inspired intelligence
• Human-machine hybrid-augmented intelligence
