Brain-machine interfaces (BMIs) allow participants to control external devices directly using brain signals without relying on the peripheral nervous system and muscles. As a new technology, BMI has great potential in motor function enhancement to help patients disabled by diseases, such as stroke. In particular, BMI systems with interactive invasive or non-invasive stimulation, which can provide an active way to reveal relationships underlying the interplay between body control and brain activities, are receiving increasing attention. While developing such enhanced BMI systems, one of the major challenges is to design an explainable and advanced intelligent method for decoding the information embedded in brain signals such as EEG, fMRI and fNIRs, LFP, ECoG, etc.
The development of explainable and advanced intelligent methods for decoding information embedded in acquired brain signals could not only enhance the performance of BMI systems but also help to better understand the communication mechanism between brain and body control. The goal of this Research Topic is to collect the current development of such advanced methods in various BMI systems and to collect the current development of neural modulation techniques such as neurofeedback training and deep brain stimulation in advancing the treatment of movement disorders such as Parkinson’s disease, essential tremor, dystonia, etc.
We welcome Original Research and Review articles covering topics of interest including but are not limited to:
- Explainable machine learning in the processing of information embedded in brain signals such as EEG, fMRI, fNIRs, LFP, ECoG, etc.
- Advanced intelligent method to analyze the relationship between body control and brain activities
- Adaptive deep brain stimulation based on the detection of some disease-related states such as tremor, epilepsy, or Parkinsonism states using BMI
- BMI neurofeedback training system for motor function enhancement, Parkinson’s disease, epilepsy, etc
- Advanced feature learning algorithms in enhanced BMI systems
- Explainable deep learning techniques for decoding brain signals (EEG, fMRI and fNIRs)
- Interpretable Analysis of Radiomics for fMRI
Brain-machine interfaces (BMIs) allow participants to control external devices directly using brain signals without relying on the peripheral nervous system and muscles. As a new technology, BMI has great potential in motor function enhancement to help patients disabled by diseases, such as stroke. In particular, BMI systems with interactive invasive or non-invasive stimulation, which can provide an active way to reveal relationships underlying the interplay between body control and brain activities, are receiving increasing attention. While developing such enhanced BMI systems, one of the major challenges is to design an explainable and advanced intelligent method for decoding the information embedded in brain signals such as EEG, fMRI and fNIRs, LFP, ECoG, etc.
The development of explainable and advanced intelligent methods for decoding information embedded in acquired brain signals could not only enhance the performance of BMI systems but also help to better understand the communication mechanism between brain and body control. The goal of this Research Topic is to collect the current development of such advanced methods in various BMI systems and to collect the current development of neural modulation techniques such as neurofeedback training and deep brain stimulation in advancing the treatment of movement disorders such as Parkinson’s disease, essential tremor, dystonia, etc.
We welcome Original Research and Review articles covering topics of interest including but are not limited to:
- Explainable machine learning in the processing of information embedded in brain signals such as EEG, fMRI, fNIRs, LFP, ECoG, etc.
- Advanced intelligent method to analyze the relationship between body control and brain activities
- Adaptive deep brain stimulation based on the detection of some disease-related states such as tremor, epilepsy, or Parkinsonism states using BMI
- BMI neurofeedback training system for motor function enhancement, Parkinson’s disease, epilepsy, etc
- Advanced feature learning algorithms in enhanced BMI systems
- Explainable deep learning techniques for decoding brain signals (EEG, fMRI and fNIRs)
- Interpretable Analysis of Radiomics for fMRI
