The advent of the IV Industrial Revolution is marked by the rise of several technologies such as Computational Intelligence, Pervasive Computing / Internet of Things, and Biotechnology. Evolutionary Computing and Deep Learning allow the Machine Learning area to progress more and more, allowing the construction of more and more precise specialist systems with greater capacity for learning and generalization. These advances, when applied to the Neurosciences, have opened up more possibilities for understanding the functioning of the nervous system and the dynamics of nervous diseases.
The advances in Computational Intelligence, especially the new computational methods, algorithms and architectures, allow complex tasks to be carried out, such as: the construction of methods of diagnosis and treatment customized for diseases of the nervous system; the construction of intelligent prostheses and more precise brain-machine interfaces; the identification of emotions and the diagnosis of mental disorders through the automated analysis of signals of various kinds, and other applications.
We would like to acknowledge the contributions of Dr Sérgio Galdino, Federal University of Pernambuco, Brazil; Dr Fernando Sales, Universidade Federal de Pernambuco - UFPE, Brazil; Dr Meuse Nogueira Júnior, Universidade Federal de Pernambuco - UFPE, Brazil, Dr Sidney Lima, Universidade Federal de Pernambuco - UFPE, Brazil; Dr Bodhisattva Dash, Silicon Institute of Technology, Bhubaneswar, India and Dr Giselle Machado Magalhães Moreno, Brazil Universidade de São Paulo – USP, Brazil, Dr who helped shape the theme and refine the focus of this Research Topic.
This Research Topic is dedicated to machine learning methods and applications in applied neuroscience. The topics of interest are the following, although they are not limited to these:
1. Computational neuroscience: principles and applications;
2. Machine learning for diagnosis of diseases of the nervous system;
3. Machine learning to treat diseases of the nervous system;
4. Early diagnosis of Alzheimer's disease and dementia using intelligent systems;
5. Early diagnosis of Parkinson's disease using intelligent systems;
6. Affective Computing, recognition of emotions and applications;
7. Human-computer neuro interfaces;
8. Intelligent prostheses and machine learning;
9. Brain-machine interfaces and intelligent control systems;
10. Embodied approaches for neuroprostheses;
11. Embodied approaches for brain-computer interfaces;
12. Neurorobotics and neuro-controlled robots;
13. Cyber-physical systems and clinical applications.
The advent of the IV Industrial Revolution is marked by the rise of several technologies such as Computational Intelligence, Pervasive Computing / Internet of Things, and Biotechnology. Evolutionary Computing and Deep Learning allow the Machine Learning area to progress more and more, allowing the construction of more and more precise specialist systems with greater capacity for learning and generalization. These advances, when applied to the Neurosciences, have opened up more possibilities for understanding the functioning of the nervous system and the dynamics of nervous diseases.
The advances in Computational Intelligence, especially the new computational methods, algorithms and architectures, allow complex tasks to be carried out, such as: the construction of methods of diagnosis and treatment customized for diseases of the nervous system; the construction of intelligent prostheses and more precise brain-machine interfaces; the identification of emotions and the diagnosis of mental disorders through the automated analysis of signals of various kinds, and other applications.
We would like to acknowledge the contributions of Dr Sérgio Galdino, Federal University of Pernambuco, Brazil; Dr Fernando Sales, Universidade Federal de Pernambuco - UFPE, Brazil; Dr Meuse Nogueira Júnior, Universidade Federal de Pernambuco - UFPE, Brazil, Dr Sidney Lima, Universidade Federal de Pernambuco - UFPE, Brazil; Dr Bodhisattva Dash, Silicon Institute of Technology, Bhubaneswar, India and Dr Giselle Machado Magalhães Moreno, Brazil Universidade de São Paulo – USP, Brazil, Dr who helped shape the theme and refine the focus of this Research Topic.
This Research Topic is dedicated to machine learning methods and applications in applied neuroscience. The topics of interest are the following, although they are not limited to these:
1. Computational neuroscience: principles and applications;
2. Machine learning for diagnosis of diseases of the nervous system;
3. Machine learning to treat diseases of the nervous system;
4. Early diagnosis of Alzheimer's disease and dementia using intelligent systems;
5. Early diagnosis of Parkinson's disease using intelligent systems;
6. Affective Computing, recognition of emotions and applications;
7. Human-computer neuro interfaces;
8. Intelligent prostheses and machine learning;
9. Brain-machine interfaces and intelligent control systems;
10. Embodied approaches for neuroprostheses;
11. Embodied approaches for brain-computer interfaces;
12. Neurorobotics and neuro-controlled robots;
13. Cyber-physical systems and clinical applications.
