Neuromodulation –referred to as the modulation of brain function via the application of weak direct current– and neurofeedback –a psychophysiological procedure that provides with models of neural activity to subjects with the goal of controlling them online– are alternative non-pharmacological ways of treating neurological related diseases and disorders. They have been successfully applied in a variety of neurological conditions including Parkinson’s disease, chronic pain, epilepsy, depression, essential tremor, among many others. Typical challenges in these types of treatments are related to the way of collecting data, the improvement in the efficiency of the methods, the interpretability of feedback signals, to name a few.
Currently, artificial intelligence (AI), and machine learning (ML) in particular, allow a better understanding of brain activity and a better brain-computer interface (BCI) mechanism of interaction. The integration of AI/ML in the data collection and monitoring phases of neuromodulation or neurofeedback can be used for early diagnosis and accurate non-pharmacological treatment of neurological diseases and disorders. Moreover, ML enables analyzing large volumes of patient information to improve the efficiency of neuromodulation and neurofeedback. The introduction of AI/ML in BCI and neuroimaging empowers these techniques for data acquisition, monitoring, analysis and prevention of neurological diseases and disorders. Thus, further investigation is necessary to understand and extend brain-computer interfaces and neuroimaging techniques implementing AI/ML. The latter integration into neuromodulation or neurofeedback can provide new opportunities to improve significantly the efficiency in the output response of these types of treating neurological diseases and disorders.
The goal of this research topic is to cover studies at the intersection of AI/ML-based BCI and neuroimaging with neuromodulation and neurofeedback in the treatment of neurological diseases and disorders. The research topic welcomes submission on topics related, but not limited, to:
• AI/ML techniques for signal conditioning of BCI and neuroimaging in neuromodulation or neurofeedback
• AI/ML techniques for pattern recognition of BCI and neuroimaging in neuromodulation or neurofeedback
• Novel algorithms in AI/ML for improving neuromodulation or neurofeedback
• Intelligent system applications with neuromodulation or neurofeedback
• AI/ML techniques applied for neurodegenerative diseases
• AI/ML for interpretability in neuromodulation or neurofeedback
• Early diagnosis and/or treatment monitoring using neuromodulation or neurofeedback with AI/ML
• AI/ML-based treatment of data in neuromodulation or neurofeedback
• Cross-disciplinary theory and methodologies between AI/ML, BCI, neuroimaging, and neuromodulation or neurofeedback
• BCI, electroencephalogram (EEG), magnetic resonance imaging (MRI), and other related interfaces with AI/ML using for neuromodulation or neurofeedback
Neuromodulation –referred to as the modulation of brain function via the application of weak direct current– and neurofeedback –a psychophysiological procedure that provides with models of neural activity to subjects with the goal of controlling them online– are alternative non-pharmacological ways of treating neurological related diseases and disorders. They have been successfully applied in a variety of neurological conditions including Parkinson’s disease, chronic pain, epilepsy, depression, essential tremor, among many others. Typical challenges in these types of treatments are related to the way of collecting data, the improvement in the efficiency of the methods, the interpretability of feedback signals, to name a few.
Currently, artificial intelligence (AI), and machine learning (ML) in particular, allow a better understanding of brain activity and a better brain-computer interface (BCI) mechanism of interaction. The integration of AI/ML in the data collection and monitoring phases of neuromodulation or neurofeedback can be used for early diagnosis and accurate non-pharmacological treatment of neurological diseases and disorders. Moreover, ML enables analyzing large volumes of patient information to improve the efficiency of neuromodulation and neurofeedback. The introduction of AI/ML in BCI and neuroimaging empowers these techniques for data acquisition, monitoring, analysis and prevention of neurological diseases and disorders. Thus, further investigation is necessary to understand and extend brain-computer interfaces and neuroimaging techniques implementing AI/ML. The latter integration into neuromodulation or neurofeedback can provide new opportunities to improve significantly the efficiency in the output response of these types of treating neurological diseases and disorders.
The goal of this research topic is to cover studies at the intersection of AI/ML-based BCI and neuroimaging with neuromodulation and neurofeedback in the treatment of neurological diseases and disorders. The research topic welcomes submission on topics related, but not limited, to:
• AI/ML techniques for signal conditioning of BCI and neuroimaging in neuromodulation or neurofeedback
• AI/ML techniques for pattern recognition of BCI and neuroimaging in neuromodulation or neurofeedback
• Novel algorithms in AI/ML for improving neuromodulation or neurofeedback
• Intelligent system applications with neuromodulation or neurofeedback
• AI/ML techniques applied for neurodegenerative diseases
• AI/ML for interpretability in neuromodulation or neurofeedback
• Early diagnosis and/or treatment monitoring using neuromodulation or neurofeedback with AI/ML
• AI/ML-based treatment of data in neuromodulation or neurofeedback
• Cross-disciplinary theory and methodologies between AI/ML, BCI, neuroimaging, and neuromodulation or neurofeedback
• BCI, electroencephalogram (EEG), magnetic resonance imaging (MRI), and other related interfaces with AI/ML using for neuromodulation or neurofeedback
