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PORTALE DELLA DIDATTICA

Neuroengineering

01RXNMV

A.A. 2022/23

Course Language

Inglese

Degree programme(s)

Master of science-level of the Bologna process in Ingegneria Biomedica - Torino

Course structure
Teaching Hours
Lezioni 39
Esercitazioni in laboratorio 21
Lecturers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Agostini Valentina - Corso 1 Professore Associato ING-INF/06 39 0 0 0 7
Agostini Valentina - Corso 2 Professore Associato ING-INF/06 39 0 21 0 7
Co-lectuers
Espandi

Context
SSD CFU Activities Area context
ING-INF/06 6 B - Caratterizzanti Ingegneria biomedica
2022/23
Applying engineering to neuroscience, the silver thread of the course will be the analysis of the human brain at different level of integration: from the single cell to small neural networks up to organ level, quantitatively measuring brain metabolism and studying complex brain functions such as neural control of muscle synergies, visual-sensory integration, and dual tasking.
Applying engineering to neuroscience, the silver thread of the course will be the analysis of the human brain at different level of integration: from the single cell to small neural networks up to organ level, quantitatively measuring brain metabolism and studying complex brain functions such as neural control of muscle synergies, visual-sensory integration, and dual tasking.
The student will acquire the knowledge of currently available and emerging technologies for interfacing with the human brain. The student will obtain the ability to acquire real brain signals and process these signals using Matlab algorithms. Soft skills will also be developed such as: the ability to work in a team to deal with a laboratory assignment mimicking a real-world problem; the competence to analyze information in the literature and apply that information to a novel problem; and the capability to communicate effectively the methodological choices adopted to solve a problem.
The student will acquire the knowledge of currently available and emerging technologies for interfacing with the human brain. The student will obtain the ability to acquire real brain signals and process these signals using Matlab algorithms. Soft skills will also be developed such as: the ability to work in a team to deal with a laboratory assignment mimicking a real-world problem; the competence to analyze information in the literature and apply that information to a novel problem; and the capability to communicate effectively the methodological choices adopted to solve a problem.
Basic knowledge of mathematics, physics, informatics, and mechanical, chemical and electrical bioengineering as learned in the 3-year program of Biomedical Engineering.
Basic knowledge of mathematics, physics, informatics, and mechanical, chemical and electrical bioengineering as learned in the 3-year program of Biomedical Engineering.
1. Functional neuroanatomy and neurophysiology 2. Neurobiological engineering: from the single cell to small networks 2.1 Single cell transmembrane potential detection, voltage clamp technique 2.2 Microelectrode Arrays (MEAs) to study electrical activity of cell networks 2.3 Non-implantable MEAs (in vitro) 2.4 Implantable MEAs (in vivo) LAB: Neuronal network simulation 3. Measuring the brain function and metabolism: from large networks to organ level 3.1 Near Infrared Spectroscopy (NIRS) 3.2 Functional Magnetic Resonance Imaging (fMRI) 3.3 Brief notes on Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and tractography LAB: Acquiring and processing NIRS signal from the pre-frontal cortex under different types of stimuli 4. Stimulating the brain: neuromodulation techniques 4.1 Non-invasive techniques: Transcranial Direct Current Stimulation (tDCS), tACS, Transcranial magnetic stimulation (TMS) 4.3 Invasive techniques: Deep Brain Stimulation (DBS), Intracranial Cortical Stimulation (ICS), Vague Nerve Stimulation (VNS) 5. Integration of CNS and PNS: from controlling single muscle force generation to performing complex functional activities 5.1 Motor control 5.2 Muscle synergies 5.2 Clinical applications LAB: Non-Negative Matrix factorization (NNMF) to extract muscle synergies during gait 6. Brain Machine Interfaces: from thought to action 6.1 BCI classification: Invasive, semi-invasive, non-invasive, stimulating, bi-directional 6.2 EEG-based BCIs: Visual Evoked Potentials (VEPs), Slow Cortical Potentials (SCPs), P300 oddball paradigm, sensorimotor rhythms (SMR) 7. Neuroprostheses (motor/sensory prostheses): a vision on the future 7.1 Pre-clinical research: retinal prosthesis (bionic eye) 8. Multisensory integration: single functions for performing complex tasks 8.1 Vision neuroscience and integration of vision with vestibular and sensory systems 8.2 Upright standing 8.3 Postural sway and posturography 8.4 Cognitive neuroengineering: dual task LAB: Processing of COP signals from a force platform during a postural balance task with eyes open and closed 9. Neurorehabilitation using virtual reality: recovering complex functions 9.1 Haptic interfaces and manipulators 9.2 Robotic rehabilitation and exoskeletons
1. Functional neuroanatomy and neurophysiology 2. Neurobiological engineering: from the single cell to small networks 2.1 Single cell transmembrane potential detection, voltage clamp technique 2.2 Microelectrode Arrays (MEAs) to study electrical activity of cell networks LAB: Processing of intra-operative recordings acquired during STN-DBS neurosurgery 3. Measuring the brain function and metabolism: from large networks to organ level 3.1 Near Infrared Spectroscopy (NIRS) 3.2 Functional Magnetic Resonance Imaging (fMRI) 3.3 Brief notes on Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and tractography LAB: Acquiring and processing NIRS signal from the pre-frontal cortex under different types of stimuli 4. Stimulating the brain: neuromodulation techniques 4.1 Non-invasive techniques: Transcranial Direct Current Stimulation (tDCS), tACS, Transcranial magnetic stimulation (TMS) 4.3 Invasive techniques: Deep Brain Stimulation (DBS), Intracranial Cortical Stimulation (ICS), Vague Nerve Stimulation (VNS) 5. Integration of CNS and PNS: from controlling single muscle force generation to performing complex functional activities 5.1 Motor control 5.2 Muscle synergies 5.2 Clinical applications LAB: Non-Negative Matrix factorization (NNMF) to extract muscle synergies during gait 6. Brain Machine Interfaces: from thought to action 6.1 BCI classification: Invasive, semi-invasive, non-invasive, stimulating, bi-directional 6.2 EEG-based BCIs: Visual Evoked Potentials (VEPs), Slow Cortical Potentials (SCPs), P300 oddball paradigm, sensorimotor rhythms (SMR) 7. Neuroprostheses (motor/sensory prostheses): a vision on the future 7.1 Pre-clinical research: retinal prosthesis (bionic eye) 8. Multisensory integration: single functions for performing complex tasks 8.1 Vision neuroscience and integration of vision with vestibular and sensory systems 8.2 Upright standing: postural sway and posturography 8.3 Cognitive neuroengineering: dual task LAB PROJECT: To be defined
Frontal lessons (39 h) + 4 Labs (21 h). During the Labs the students will work in teams of 4 persons. The frequency to the labs is mandatory to take the final examination.
Frontal lessons (39 h) + 4 Labs (21 h), including 1 final "Lab-Project" whose solution will be evaluated. During the Labs the students will work in teams of 4 persons. The frequency to the Labs is mandatory to take the final examination.
Slides, articles and laboratory assignments provided by the teacher. Data and signals acquired during lab sessions + Matlab code to get some practice on how to process signals and analyze/interpret data.
Slides, articles and laboratory assignments provided by the teacher. Data and signals acquired during lab sessions + Matlab code to get some practice on how to process signals and analyze/interpret data. Suggested book: J. Wolpaw and E. Wolpaw - "Brain-Computer Interfaces. Principles and Practice", Oxford University Press, USA
ModalitÓ di esame: Elaborato scritto prodotto in gruppo; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Group essay; Computer-based written test in class using POLITO platform;
... The exam is aimed to verify the acquisition of the knowledge and skills described in the Expected Learning Outcomes. It will be composed of 2 parts: - WRITTEN TEST with structured (multiple choice, true/false), semi-structured (exercises, table completion,...) and open-ended questions about the topics covered during the frontal lessons and the Labs. The written test lasts 1 hour. During the exam it is not allowed to keep notes, or other Course materials, while it is allowed to use a calculator. (Language: English). - LABORATORY REPORTS. After each Lab, the Lab-team of 4 students will produce a Powerpoint presentation with a textual and/or graphical description of the methods used to solve the Lab assignments, the results obtained, and the discussion of the results obtained. Feedback and scores about the Lab reports will be provided during the Course.(Language: English). The final mark, expressed in thirtieths, will be obtained as the sum of the two following scores: - WRITTEN TEST: up to 23/33 points (minimum acceptable score to pass: 10) - LABORATORY REPORTS: up to 10/33 points. If the final mark is equal to or greater than 31.5 the Laude will be assigned. The exam scores will be communicated on the didactic web portal, as well as the date in which the students will be able to view the written test and ask for explanations.
Gli studenti e le studentesse con disabilitÓ o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'UnitÓ Special Needs, al fine di permettere al/la docente la declinazione pi¨ idonea in riferimento alla specifica tipologia di esame.
Exam: Group essay; Computer-based written test in class using POLITO platform;
EXPECTED LEARNING OUTCOMES The student will acquire the knowledge of currently available and emerging technologies for interfacing with the human brain. The student will obtain the ability to acquire real brain signals and process these signals using Matlab algorithms. Soft skills will also be developed such as: the ability to work in a team to deal with a laboratory assignment mimicking a real-world problem; the competence to analyze information in the literature and apply that information to a novel problem; and the capability to communicate effectively the methodological choices adopted to solve a problem. The exam is aimed to verify the acquisition of the knowledge and skills described in the Expected Learning Outcomes. It will be composed of 2 parts: - LABORATORY REPORT. The Lab-team of 4 students will produce a report with a textual and/or graphical description of the methods used to solve the "Lab-problem", the results obtained, represented through Matlab figures/plots/graphs produced by the algorithms they developed during the labs, and the interpretation and discussion of the results obtained. (Language: English). - WRITTEN TEST with structured (multiple choice, true/false), semi-structured (exercises, table completion,...) and open-ended questions about the topics covered during the frontal lessons and the Labs. The written test lasts 1 hour. During the exam it is not allowed to keep notes, or other Course materials, while it is allowed to use a calculator. (Language: English). During the exam it is not allowed to keep notes, or other Course materials, while it is allowed to use a calculator. The final mark, expressed in thirtieths, will be obtained as the sum of the two following scores: - WRITTEN TEST: up to 23/33 points (minimum acceptable score to pass: 10) - LABORATORY REPORTS: up to 10/33 points. If the final mark is equal to or greater than 31.5 the Laude will be assigned. The exam scores will be communicated on the didactic web portal, as well as the date in which the students will be able to view the written test and ask for explanations.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
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