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Course syllabus - Neurotechnology

Scope

7.5 credits

Course code

ELA411

Valid from

Autumn semester 2020

Education level

Second cycle

Progressive Specialisation

A1F (Second cycle, has second-cycle course/s as entry requirements).

Main area(s)

School

School of Innovation, Design and Engineering

Ratified

2020-01-24

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2020-09-09
    • Latest update: 2020-09-09

    Articles

    Lebedev, M.A.; Nicolelis, M.A.L.

    Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

    Physiol. Rev, 2017

    Mandatory

    Parajuli, N.; Sreenivasan, N.; Bifulco, P.; Cesarelli, M.; Savino, S.; Niola, V.; Esposito, D.; Hamilton, T.J.; Naik, G.R.; Gunawardana, U.; et al.

    Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

    Sensors (Basel), 2019

    Mandatory

    Sitaram, R.; Ros, T.; Stoeckel, L.; Haller, S.; Scharnowski, F.; Lewis-Peacock, J.; Weiskopf, N.; Blefari, M.L.; Rana, M.; Oblak, E.; et al.

    Closed-loop brain training: the science of neurofeedback

    Nature Reviews Neuroscience 18, 2017

    Mandatory

Objectives

The purpose of the course is to provide students with deeper knowledge, understanding, and practical insight in physiologically connected health robotics with focus on brain-robot interfaces.

Learning outcomes

After completing the course, the student shall to be able to:

1. have insight and be able to critically review frontier research within different applicative areas of health robotics,
2. show an understanding of critical parameters of interfaces both in terms of physiological signal and control signal to robot and also
3. show in-depth knowledge within a chosen application of health robotics, be able to practically implement and describe its function.

Course content

Health robotics, physiological control signals, brain-robot interface, prothesis control with muscle- and/or brain-activity, feedback, rehabilitation robotics, human-robot interaction, ethical aspects

Specific requirements

Advanced signal processing 7.5 hp, Measurement technology 7.5 hp, Probability and statistical theory 7.5 hp, Complex Electronic Systems 7.5 hp and Learning systems 7.5 hp. In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English, exemption is made from the requirement in Swedish course B/Swedish course 3.

Examination

Seminars (SEM1), 3 credits, examines the learning objectives 1 and 2, marks Fail (U) or Pass (G).
Project (PRO1), 4.5 credits, examines the learning objectives 2 and 3, marks Fail (U), 3, 4 or 5.

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Study guide

Grade

Grading scale: 5, 4, 3