Ai Applied to Neurological Sciences and Brain-Computer Interfaces
A.Y. 2024/2025
Learning objectives
The main objective of the course is to provide the students with basic understanding of neurological diseases, introducing the main clinical features, as well as their functional neurophysiological correlates.
Further aim will be to provide an introduction to the basics of Brain Computer Interfaces (BCI) principally based on oscillatory EEG activity, but also on transient EP and ERP signals. The course will introduce the main methods for acquiring and processing electrophysiological data allowing the decoding of brain activity in real time for converting it into BCI control signals.
Further aim will be to provide an introduction to the basics of Brain Computer Interfaces (BCI) principally based on oscillatory EEG activity, but also on transient EP and ERP signals. The course will introduce the main methods for acquiring and processing electrophysiological data allowing the decoding of brain activity in real time for converting it into BCI control signals.
Expected learning outcomes
The students are expected to:
- Acquire basic knowledge on the major neurological disease and their clinical features
- Know the basic neural substrates of neurophysiological signals, and their alterations
- Identify the main medical applications of AI algorithms in neurological diseases
- Acquire knowledge on the available AI tools to promote early diagnosis of neurodegenerative diseases
- Explore basic principles for applications to drug discovery
- Evaluate potential applications for neuro-rehabilitative interventions
- Acquire basic knowledge of the various oscillatory and transient electrical signals of the brain
- Know which electrical marker might be more appropriate for assessing minimally conscious state, for 'mind reading', or robotic control
- Explore available techniques for EEG-based BCI applications for motor control and augmented communication.
- Acquire basic knowledge on the major neurological disease and their clinical features
- Know the basic neural substrates of neurophysiological signals, and their alterations
- Identify the main medical applications of AI algorithms in neurological diseases
- Acquire knowledge on the available AI tools to promote early diagnosis of neurodegenerative diseases
- Explore basic principles for applications to drug discovery
- Evaluate potential applications for neuro-rehabilitative interventions
- Acquire basic knowledge of the various oscillatory and transient electrical signals of the brain
- Know which electrical marker might be more appropriate for assessing minimally conscious state, for 'mind reading', or robotic control
- Explore available techniques for EEG-based BCI applications for motor control and augmented communication.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Lesson period
First semester
Course syllabus
Aim of this course is to provide the theoretical basis aimed at fostering an interdisciplinary and integrative interaction between clinicians, AI algorithm designers, medical-application-specific "chip scientists". The interaction between AI and cognitive neuroscience/neuropsychology will be discussed, with a focus on how information derived from neurophysiological data can guide AI to manage different final effectors through brain computer interfaces (BCI). Furthermore, the use of AI to advance the knowledge on brain functioning with the application of machine learning on brain signals will be discussed.
o The changing landscape of medical education, with specific focus on Neurology
o AI-assisted Detection & Diagnosis- Neuroimaging
o AI-assisted Neurorehabilitation
o Disease Management - Health Analytics
o Neurology and Aging
o AI-assisted neurotherapeutics- drug discovery in neurological disorders
o Brain computer interfaces (non invasive, semi-invasive, invasive, closed loop, open loop);
o Adaptive Interfaces
o Using Neural Measures to Predict Real-World Outcomes;
o Social/Affective neuroscience for human-machine interaction;
o Usability of BCIs in cognitive neuroscience;
o BCI in cognitive and neurological rehabilitation
o The changing landscape of medical education, with specific focus on Neurology
o AI-assisted Detection & Diagnosis- Neuroimaging
o AI-assisted Neurorehabilitation
o Disease Management - Health Analytics
o Neurology and Aging
o AI-assisted neurotherapeutics- drug discovery in neurological disorders
o Brain computer interfaces (non invasive, semi-invasive, invasive, closed loop, open loop);
o Adaptive Interfaces
o Using Neural Measures to Predict Real-World Outcomes;
o Social/Affective neuroscience for human-machine interaction;
o Usability of BCIs in cognitive neuroscience;
o BCI in cognitive and neurological rehabilitation
Prerequisites for admission
Understanding of the basic biology of the nervous system.
Basic Knowledge of how the brain functions, with a particular focus on electrophysiology.
Basic Knowledge of how the brain functions, with a particular focus on electrophysiology.
Teaching methods
Frontal lessons in English with slides and audio/video presentations. Presentation and discussion of ongoing data and research articles.
(a) nature of teaching: dispensing and interactive
(b) type of teaching activity: lecture
(c) hours possibly delivered remotely = none (except for emergency)
(a) nature of teaching: dispensing and interactive
(b) type of teaching activity: lecture
(c) hours possibly delivered remotely = none (except for emergency)
Teaching Resources
The course uses teaching materials made available through Ariel.
Handouts will be uploaded from time to time, and specialized readings will be suggested. Texts and reference works for the various teaching modules are available on Ariel.
Handouts will be uploaded from time to time, and specialized readings will be suggested. Texts and reference works for the various teaching modules are available on Ariel.
Assessment methods and Criteria
Oral colloquium.
M-PSI/02 - PSYCHOBIOLOGY AND PHYSIOLOGICAL PSYCHOLOGY - University credits: 3
MED/26 - NEUROLOGY - University credits: 3
MED/26 - NEUROLOGY - University credits: 3
Lessons: 48 hours
Professors:
De Icco Roberto, Pisani Antonio, Proverbio Alice Mado, Terzaghi Michele
Educational website(s)