Therapeutic neuromodulation of a variety of neurodegenerative disorders, such as epilepsy, seizure, and Alzheimer's disease, holds great potential for the advent of closed-loop brain-computer interface (BCI) and neurofeedback (NF) technologies. The seamless closed-loop brain interface is often comprised of steps of neural recording, decoding, and stimulation, which necessitates multidisciplinary study in a number of fields. Electrophysiological approaches with electrodes were extensively used in previous brain interfaces, both invasively in the form of Electrocorticography (ECoG) and non-invasively in the form of electroencephalography (EEG).
Recent advances have been made in all three phases of the closed-loop brain-computer interface technologies. At the neural recording stage, new sensing modalities such as in-ear EEG, functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) have emerged to enhance brain recording's wearability, spatial resolution, and temporal resolution. At the neural decoding stage, in addition to traditional feature extraction methods such as support vector machine (SVM), independent component analysis (ICA), and common spatial patterns (CSP), new methodologies such as Riemannian geometry, deep learning, and transfer learning have been proposed to improve the feature representation. At the neurostimulation stage, apart from electrical stimuli, acoustic and optical stimuli have been introduced and demonstrated in occasions such as cochlear implants.
In this Research Topic, we aim to address the advances in different stages of brain-computer interface technologies and relevant applications, promote the discussion around this topic and facilitate knowledge dissemination
Research advances from all BCI-related topics, including (but not limited to) wearable devices, implantable systems, front-end circuits & components, neural recording, neural signal processing, neurostimulation, and relevant applications are all within the scope of this research topic.
Some examples (but not limited to) of sub-topics in the field of brain-computer interfaces are listed here:
• New circuits, sensors, and actuator designs for brain-computer interfaces
• Advanced devices, systems, and materials for wearable and implantable brain-computer interfaces
• Brain signal processing and feature extraction methods
• Machine learning and deep learning applications in brain signal analysis
• Novel neurophysiological feedback methodologies
• Motion analysis in brain-computer interfaces
• Neuroscience clinical studies with BCI-related systems
• New technology developments for neural interface
Therapeutic neuromodulation of a variety of neurodegenerative disorders, such as epilepsy, seizure, and Alzheimer's disease, holds great potential for the advent of closed-loop brain-computer interface (BCI) and neurofeedback (NF) technologies. The seamless closed-loop brain interface is often comprised of steps of neural recording, decoding, and stimulation, which necessitates multidisciplinary study in a number of fields. Electrophysiological approaches with electrodes were extensively used in previous brain interfaces, both invasively in the form of Electrocorticography (ECoG) and non-invasively in the form of electroencephalography (EEG).
Recent advances have been made in all three phases of the closed-loop brain-computer interface technologies. At the neural recording stage, new sensing modalities such as in-ear EEG, functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) have emerged to enhance brain recording's wearability, spatial resolution, and temporal resolution. At the neural decoding stage, in addition to traditional feature extraction methods such as support vector machine (SVM), independent component analysis (ICA), and common spatial patterns (CSP), new methodologies such as Riemannian geometry, deep learning, and transfer learning have been proposed to improve the feature representation. At the neurostimulation stage, apart from electrical stimuli, acoustic and optical stimuli have been introduced and demonstrated in occasions such as cochlear implants.
In this Research Topic, we aim to address the advances in different stages of brain-computer interface technologies and relevant applications, promote the discussion around this topic and facilitate knowledge dissemination
Research advances from all BCI-related topics, including (but not limited to) wearable devices, implantable systems, front-end circuits & components, neural recording, neural signal processing, neurostimulation, and relevant applications are all within the scope of this research topic.
Some examples (but not limited to) of sub-topics in the field of brain-computer interfaces are listed here:
• New circuits, sensors, and actuator designs for brain-computer interfaces
• Advanced devices, systems, and materials for wearable and implantable brain-computer interfaces
• Brain signal processing and feature extraction methods
• Machine learning and deep learning applications in brain signal analysis
• Novel neurophysiological feedback methodologies
• Motion analysis in brain-computer interfaces
• Neuroscience clinical studies with BCI-related systems
• New technology developments for neural interface
