This course focuses on the development of brain-machine interfaces (BMI), with a specific emphasis on EEG signal analysis and its application in controlling robotic devices. The key objectives are:
1. **Fundamental Knowledge**: Students will learn the basic principles of BMI systems, with a focus on real-time analysis, manipulation, and decoding of neurophysiological signals (e.g., EEG).
2. **Practical Skills**: The course includes hands-on laboratory work where students practice EEG signal analysis and BMI development, particularly using evoked potentials and motor imagery for controlling robotic devices.
3. **Theoretical and Technical Tools**: Students will gain expertise in signal processing, machine learning, and advanced techniques like decoding signals in non-Euclidean spaces and neuro-manifolds (Riemann).
4. **Interdisciplinary Application**: The course also integrates neurorobotics, with students learning to develop closed-loop BMI systems and explore Human-Robot Interaction.
5. **Assessment**: Evaluation is based on a group projects and written exam, assessing students' understanding of BMI technology, neurorobotics, and EEG signal processing.
The course requires basic MATLAB knowledge, with additional skills in biomedical signal processing, robotics, and programming in Python or C++.
1. **Fundamental Knowledge**: Students will learn the basic principles of BMI systems, with a focus on real-time analysis, manipulation, and decoding of neurophysiological signals (e.g., EEG).
2. **Practical Skills**: The course includes hands-on laboratory work where students practice EEG signal analysis and BMI development, particularly using evoked potentials and motor imagery for controlling robotic devices.
3. **Theoretical and Technical Tools**: Students will gain expertise in signal processing, machine learning, and advanced techniques like decoding signals in non-Euclidean spaces and neuro-manifolds (Riemann).
4. **Interdisciplinary Application**: The course also integrates neurorobotics, with students learning to develop closed-loop BMI systems and explore Human-Robot Interaction.
5. **Assessment**: Evaluation is based on a group projects and written exam, assessing students' understanding of BMI technology, neurorobotics, and EEG signal processing.
The course requires basic MATLAB knowledge, with additional skills in biomedical signal processing, robotics, and programming in Python or C++.
- Docente: Luca Tonin