Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, computational methods have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). While there has been an increasing interest in these questions, the contribution of fuzzy systems has been diverse depending on the area of application. On the one hand, considering the decoding of brain activity, advanced computational intelligence methods that handles uncertainty such as fuzzy sets and systems, represent an excellent tool to overcome the challenge of processing extremely noisy signals that are very likely to be affected by non-stationarities, invariants and poor generalisation. On the other hand, as regards neuroscience research, possibility and fuzziness has equally been employed for the measurement of smooth integration between synapses, neurons, and brain regions or areas. In this context, the proposed special issue targets a specialized forum that can serve as a medium for computational intelligence researchers, to simulate and express uncertainty for analysis of neuroimaging data. Any area related to neuroscience such as computational neuroscience, brain computer/machine interfaces, neuroscience, neuroinformatic, neuroergonomics, computational cognitive neuroscience, affective neuroscience, neurobiology, brain mapping, neuro-engineering, and neurotechnology is appropriate.
This Research Topic focuses on recent advances, challenges, and future perspectives about computational methods applied in neuroimaging, studied in different domains of knowledge. Thus, we invite researchers to contribute original work related to this Research Topic, exploiting recent methodology using computational and mathematical techniques in neuroimaging, and addressing the challenges in developing dedicated systems for various clinical applications, while proposing new ideas and directions for future development.
• Computational methods for the analysis of brain signals from any functional or structural neuroimaging modalities (fMRI /MRI, PET/SPECT, EEG, MEG, fNIRS, DOI, EROS, etc.)
• Brain computer/machine interfaces (all paradigms, transfer learning, multi-modal BCI, Neural Prostheses) powered by Fuzzy Systems and computational methods.
Areas of interest include but not limited to:
• Computational methods for Neuroscience applications and the understanding of brain processes.
• Deep Computational Methods.
• Internet of Brain Things.
• Neuro-rehabilitation.
• Neuro-robotics.
Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, computational methods have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). While there has been an increasing interest in these questions, the contribution of fuzzy systems has been diverse depending on the area of application. On the one hand, considering the decoding of brain activity, advanced computational intelligence methods that handles uncertainty such as fuzzy sets and systems, represent an excellent tool to overcome the challenge of processing extremely noisy signals that are very likely to be affected by non-stationarities, invariants and poor generalisation. On the other hand, as regards neuroscience research, possibility and fuzziness has equally been employed for the measurement of smooth integration between synapses, neurons, and brain regions or areas. In this context, the proposed special issue targets a specialized forum that can serve as a medium for computational intelligence researchers, to simulate and express uncertainty for analysis of neuroimaging data. Any area related to neuroscience such as computational neuroscience, brain computer/machine interfaces, neuroscience, neuroinformatic, neuroergonomics, computational cognitive neuroscience, affective neuroscience, neurobiology, brain mapping, neuro-engineering, and neurotechnology is appropriate.
This Research Topic focuses on recent advances, challenges, and future perspectives about computational methods applied in neuroimaging, studied in different domains of knowledge. Thus, we invite researchers to contribute original work related to this Research Topic, exploiting recent methodology using computational and mathematical techniques in neuroimaging, and addressing the challenges in developing dedicated systems for various clinical applications, while proposing new ideas and directions for future development.
• Computational methods for the analysis of brain signals from any functional or structural neuroimaging modalities (fMRI /MRI, PET/SPECT, EEG, MEG, fNIRS, DOI, EROS, etc.)
• Brain computer/machine interfaces (all paradigms, transfer learning, multi-modal BCI, Neural Prostheses) powered by Fuzzy Systems and computational methods.
Areas of interest include but not limited to:
• Computational methods for Neuroscience applications and the understanding of brain processes.
• Deep Computational Methods.
• Internet of Brain Things.
• Neuro-rehabilitation.
• Neuro-robotics.
