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NEURAL AND BRAIN-COMPUTER INTERFACES

CODE 106737
ACADEMIC YEAR 2022/2023
CREDITS
  • 8 cfu during the 1st year of 11159 BIOENGINEERING(LM-21) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-INF/06
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 2° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course is focused on the description and understanding of the functioning principles of the neural and brain machine interfaces. It will start with the definition of neural interface and state of the art in the field of neuro-electronic systems. The techniques for measuring the electrophysiological activity of excitable cells and tissues will be presented and explained. A brief introduction of advances signal processing for neural interfaces will be then provided, as a basis for the understanding of coding and decoding of information in neural interfaces. The state of the art in current neural interfaces, including invasive and non-invasive Brain Machine/Computer Interfaces and Neural Prostheses will be analyzed and discussed, by focusing on their materials, methods and current translational and clinical applications.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    Definizione di interfacce neurali e stato dell'arte nel campo dei sistemi neuro-elettronici. Tecniche per la misurazione dell'attività elettrofisiologica di cellule e tessuti eccitabili. Elaborazione avanzata dei segnali per interfacce neurali. Codifica e decodifica dell'informazione nelle interfacce neurali. Definizione di interfacce neurali unidirezionali e bidirezionali. Interfacce cervello-macchina e protesi neurali invasive e non invasive per il sistema nervosa centrale: materiali, metodi e applicazioni attuali

    AIMS AND LEARNING OUTCOMES

    Aim 1. Demonstration of theoretical models related to neuro-electronic interfaces. Learning outcome for Aim 1: Capability to understand and demonstrate theoretical models of the neuro-electronic interface.

    Aim 2. Description and analysis of systems including neuro-electronic interfaces and microtransducers for electrophysiology. Learning outcome for Aim 2: Design (fundamentals) of microtransducer for neural interfaces and resolution of simple problems related to neural interfaces and applications.

    Aim 3. Understanding of basic algorithms for the processing of experimental data from neural signals in the framework of neural interfaces applications. Learning outcome for Aim 3: Design and implementation of software tools for neuronal signal analysis for applications in the neural and brain computer interface framework.

    Aim 4. Definition of neural coding and decoding and analysis of the issues related to them. Learning outcome for Aim 4. Critical analysis of the current state of the art work in the field of neural and brain-machine interfaces.

    Aim 5. Solve problems arising in a real lab environment where electrophysiological experiments at an increased level of complexity are performed (from in vitro, to in vivo up to human experiments). Learning outcome for Aim 5. Operative abilities in the use of lab instruments for electrophysiological recordings and basic image acquisition and processing. Understanding and reproducing the basic steps to perform neural/brain interfaces and neuroprosthetics experiments.

    PREREQUISITES

    Fundamental of chemistry, biophysics, mathematics, electronics and computer science provided during the first three years of the Laurea in Biomedical Engineering.

    TEACHING METHODS

    • Frontal lectures
    • Supervised Exercises
    • Experimental Lab training

    SYLLABUS/CONTENT

    Modeling of the neuro-electronic interface: theoretical models of the solid-liquid interface; polarizable and not polarizable interface; Microtransducers and electrophysiological techniques; Micro Electrode, Silicon Transistors, Organic Transistors.

    Techniques for electrophysiology and applications: in-vitro and in-vivo electrophysiology, intra-cellular and extracellular measurements, patch clamp; single cell recordings, network electrophysiology; devices and applications.

    Techniques for analysis of neuronal signals in the framework of neural interfaces interacting with the brain: MUA, SUA & LFP definition; data processing and visualization for neural interfaces; LFP definition and basic processing.

    Code of information and information transmission: definition of neural code; rate vs time code; information theory applied to neural signals; recent approaches for neural coding; applications.

    Decoding of information and Brain-Machine-Interfaces: definition of BMIs; types of BMI; concept of decoding of activity and theoretical definition; clinical case studies and applications of Brain-Machine-Interfaces and neuroprosthesis.

    RECOMMENDED READING/BIBLIOGRAPHY

    • Bioelectronics: MOSFETs, biosensors, neurons, M. Grattarola e G. Massobrio, McGraw -Hill, (1998)
    • Principle of applied Biomedical instrumentation, Geddes and Baker, Wiley Interscience ed., 1989.
    • SPIKES: exploring the neural code, Riecke, Warland, de Reuyter van Steveninck, Bialek, MIT press, 1997.
    • Toward Replacement Parts For The Brain: Implantable Biomimetic Electronics As Neural Prostheses, T. Berger, D. Glanzman eds, MIT press 2005 
    • Analyzing Neural time series data: Theory and practice, Michael X. Cohen, MIT Press, (2014)
    • Niedermayers’ Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Schomer and Lopes da Silva, sixth ed. Lippincot Williams and wilkins, (2011)

    TEACHERS AND EXAM BOARD

    Exam Board

    MICHELA CHIAPPALONE (President)

    GABRIELE ARNULFO

    SERGIO MARTINOIA (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    The exam is constituted by a written and an oral examination (exercises on the topics presented during the frontal lessons and the supervised exercises) and by an optional presentation of a scientific paper as a Journal Club. The evaluation of the presentation, by the Professor and by two reviewers chosen among the student participating to the course, gives a bonus of up to 3 points.

    The experimental lab training do not concur to the final exam evaluation, but serve for the achievement of Aim5.

    The exam dates will be decided by the professor and communicated to the students with a proper timing.

    ASSESSMENT METHODS

    The written and the oral test will allow to evaluate the achievements of Aim1, Aim2 and Aim3.

    The Journal Club will assess the achievement of Aim4.

    The experimental lab training activities will allow to reach Aim5.

    Exam schedule

    Date Time Location Type Notes
    17/01/2023 09:00 GENOVA Esame su appuntamento
    14/02/2023 09:00 GENOVA Esame su appuntamento
    13/06/2023 09:00 GENOVA Esame su appuntamento
    18/07/2023 09:00 GENOVA Esame su appuntamento
    15/09/2023 09:00 GENOVA Esame su appuntamento

    FURTHER INFORMATION

    NA