Subject

Computing, Robotics and Bionics 1

  • code 13547
  • course 3
  • term Semester 1
  • type op
  • credits 6

Main language of instruction: English

Other languages of instruction: Catalan, Spanish

Timetable
group M
 Sem.1  TU 16:00 18:00 
 Sem.1  FR 16:00 18:00 

Teaching staff

Introduction

The course will focus on the field of the neural engineering, a discipline at the frontier between neuroscience and engineering.  First, fundamental concepts of neuroengineering will be studied, including: interfaces with the nervous system, neuroprosthesis, brain-computer interfaces (BCI) and electrophysiology records. Seminary classes are developed for theoretical training, computer practices. Biological neuronal systems and monitoring in the development of work and studies by students. On the other side,  measurement and analysis techniques of neural data will be introduced applying artificial intelligence and signal processing. Seminar-type classes for theoretical training are combined with computer practices. Biological neuronal systems and monitoring in the development of work and studies by students.

 

 

 

Pre-course requirements

To access the course it is required to have completed the following subjects:

Course 1 subjects 

Calculation

Course 2 subjects

Computing

Fundamentals and Electronic Systems

Signal and Systems Theory

Biostatistics

Course 3 subjects

Neuroscience Applied to Orthoprosthesis

Objectives

  • Saber distinguir entre machine learning supervisado y no supervisado.
  • Conocer y saber usar los principales algoritmos de clustering y clasificación.
  • Evaluar las limitaciones actuales y potenciales de la inteligencia artificial.
  • Describir i saber aplicar el algoritmo de spike sorting en registros de actividad eléctrica intracelular.
  • Tener la capacidad de aplicar el pre procesamiento básico a una señal de electroencefalograma y una señal bold de resonancia magnética funcional.
Assess / Evaluate 

Competences / Learning outcomes of the degree programme

  • CE1 - To solve the maths problems that arise in the field of Bioengineering. The ability to apply knowledge of geometry, calculate integrals, use numerical methods and achieve optimisation.
  • CE12 - To undertake a professional project in the field of Bioengineering-specific technologies in which knowledge acquired through teaching is synthesised and incorporated.
  • CE15 - The ability to undertake a project through the use of data sources, the application of methodologies, research techniques and tools specific to Bioengineering, give a presentation and publicly defend it to a specialist audience in a way that demonstrates the acquisition of the competences and knowledge that are specific to this degree programme.
  • CE16 - To apply specific Bioengineering terminology both verbally and in writing in a foreign language.
  • CE17 - To be able to identify the engineering concepts that can be applied in the fields of biology and health.
  • CE3 - To apply fundamental knowledge on using and programming computers, operating systems, databases and IT programs to the field of Bioengineering.
  • CE7 - To know how to recognise anatomy and physiology when applied to the structures Bioengineering involves.
  • CG1 - To undertake projects in the field of Bioengineering that aim to achieve a concept and a design, as well as manufacture prosthetics and orthotics that are specific to a certain pathology or need.
  • CG4 - To resolve problems based on initiative, be good at decision-making, creativity, critical reasoning and communication, as well as the transmission of knowledge, skills and prowess in the field of Bioengineering
  • CG5 - To undertake calculations, valuations, appraisals, expert reports, studies, reports, work plans and other similar tasks.
  • CG7 - To analyse and evaluate the social and environmental impact of technical solutions
  • CT3 - To know how to communicate learning results to other people both verbally and in writing, and well as thought processes and decision-making; to participate in debates in each particular specialist areas.
  • CT4 - To be able to work as a member of an interdisciplinary team, whether as a member or by management tasks, with the aim of contributing to undertaking projects based on pragmatism and a feeling of responsibility, taking on commitment while bearing the resources available in mind.
  • CT5 - To use information sources in a reliable manner. To manage the acquisition, structuring, analysis and visualisation of data and information in your specialist area and critically evaluate the results of this management.
  • CT6 - To detect gaps in your own knowledge and overcome this through critical reflection and choosing better actions to broaden your knowledge.
  • CT7 - To be fluent in a third language, usually English, with a suitable verbal and written level that is in line with graduate requirements.
  • CB2 - Students must know how to apply their knowledge to their work or vocation in a professional way and have the competences that are demonstrated through the creation and defence of arguments and the resolution of problems within their field of study.
  • CB3 - Students must have the ability to bring together and interpret significant data (normally within their area of study) and to issue judgements that include a reflection on important issues that are social, scientific or ethical in nature.
  • CB4 - Students can transmit information, ideas, problems and solutions to specialist and non-specialist audiences.
  • CG10 - To know how to work in a multilingual and multidisciplinary environment.
  • CG2 - To promote the values that are specific to a peaceful culture, thus contributing to democratic coexistence, respect for human rights and fundamental principles such as equality and non-discrimination.
  • CG3 - To be able to learn new methods and theories and be versatile so as to adapt to new situations.
  • CT2 - The ability to link welfare with globalisation and sustainability; to acquire the ability to use skills, technology, the economy and sustainability in a balanced and compatible manner.

Syllabus

Block 1. Computation. Classical Machine Learning


1. Unsupervised learning (Clustering and dimensionality reduction/factorization)

1.1 Clustering algorithms

1.1.1 k-means clustering

1.1.2 Hierarchical clustering

1.1.3 Spectral clustering

1.1.4 Gaussian Mixture Model clustering (GMM)

1.1.5 Hiden Markov model clustering

1.2 Dimensionality reduction and factorization

1.2.1 Principal components analysis (PCA)

1.2.2 Non-Negative Matrix Factorization (NNMF)

1.2.3 Factor Analysis (FA)

 

2. Supervised learning (classification regression)

2.1 Regression algorithms

2.1.1 Linear regression

2.1.2 Non-liner regression

2.1.3 Logistic regression

2.1.4 Generalized Linear Model (GLM)

2.1.5 Regression trees

2.1.6 Gaussian Process Regression Model

2.2 Classification algorithms

2.1.1 Linear discriminant Analysis (LDA)

2.1.2 Support Vector Machines (SVM)

2.1.3 k-Nearest Neighbor (kNN)

2.1.4 Decision trees

2.1.5 Naïve Bayes

2.1.6 Neural networks

2.1.7 Bagged/Boosted decision trees

 

3. Blind source Separation

3.1 Independent Component Analysis (ICA)

3.2 Fast Independent Component Analysis (Fast-ICA)

 

Block 2. Bionics. Brain function and motor neuroprosthetics

 

1. Origin of the brain signal

 

2. Recording brain activity

2.1 Electrical brain measures

2.1.1 Extracellular recordings

2.1.1.1 Electroencephalogram (EEG)

2.1.1.2 Electrocorticography (ECoG) (Epidural, Subdural)

2.1.2 Intracellular recordings

2.1.2.1 Single Unit Activity (SUA)              

2.1.2.2 Local Field Potentials (LFP) and Multi Unit Activity (MUA)

2.2. Magnetic brain measures

2.2.1 Magnetic Resonance Imaging (MRI)

2.3. Metabolic brain measures

 2.3.1 Functional Magnetic Resonance Imaging (fMRI)

 2.3.2 Functional near-infrared spectroscopy (fNIRS)

 

3. Analysis of brain activity

3.1 Spike Sorting

3.2 Raster plot decoding

3.3 Peri-Stimulus Time Histogram (PSTH)

3.4. Spike density function

3.5 Tuning curves and population vector

3.6 EMG signal processing

3.7 BOLD Signal (fMRI) processing

 

4. Origin of the muscular signal

 

5. Recording muscular activity

5.1 Electromyography (EMG)

5.2 PNS neural interfaces

 

6. Analysis of muscular activity

6.1 Muscular synergies from EMG signal

6.2 EMG processing and prosthetics arm control

6.3 Cuff ENG cumulative signal processing

Teaching and learning activities

In person

   
TRAINING ACTIVITY

Project-oriented learning is a method based on experiential and reflective learning in which the research process on a particular subject is of great importance. The aim is to resolve complex problems based on open solutions or tackle difficult issues that allow new knowledge to be generated and new skills to be developed by students.
Lectures are the setting for: learning and managing the terminology and language structures related to each scientific field. Practicing and developing oral and written communication skills. And learning how to analyse the bibliography and literature on Bioengineering. Using guidelines to identify and understand the main ideas during lectures. This academic activity has been an essential tool in education since it first began and should have a significant presence within the framework of this degree programme.
Reading texts with the aim of engaging critical thinking plays a fundamental role in learning for citizens who are both aware and responsible.
An activity for outside the classroom. This activity means students can allow their knowledge to settle and rest, which is always necessary before beginning a new task.
The professor sets out exercises and problems, helps students to progress in terms of the engineering process the design involves, and guides the student, thus partial goals are achieved that facilitate the incorporation of the theoretical knowledge acquired.
METHODOLOGY

Practical classes allow students to interact at first hand with the tools they will need to use in their work. In small groups or individually practical demonstrations will be carried out based on the theoretical knowledge acquired during the theory classes.
In theory classes the fundamental and scientific knowledge that forms the basis of the knowledge and rigour that engineering studies require must be established.
This teaching method is based on reflection, it can provide students with useful knowledge and skills to tackle problems efficiently in a shorter period of time.
Individual work, involving study, the search for information, data processing and the internalisation of knowledge will allow students to consolidate their learning.
COMPETENCES

CB1 CB2 CB3 CB4 CB5 CE12 CE13 CE15 CE17 CE20 CE8 CG10 CG2 CG3 CG4 CG6 CG7 CT2 CT3 CT4 CT5 CT6 CT7
 

Evaluation systems and criteria

In person

The final grade of the subject will be obtained as

Nota=0,4·Nef +0,3·Nlab+0,3·Ntreb

where

Nef : Final exam grade

Nlab : Lab grade

Ntreb : Course work grade

 

 No partial exam.

To apply for the apt, it is essential to carry out the laboratory practices of the subject.

 

Important considerations:

  1. Plagiarism, copying or any other action that may be considered cheating will be zero in that evaluation section. Besides, plagiarism during exams will mean the immediate failing of the whole subject.
  2. In the second-sitting exams, the maximum grade students will be able to obtain is "Excellent" (grade with honors distinction will not be posible).
  3. Changes of the calendar, exam dates or the evaluation system will not be accepted.
  4. Exchange students (Erasmus and others) or repeaters will be subjected to the same conditions as the rest of the students.

Bibliography and resources

[1] Dornhege, G. Millán, J.d.R., Hinterberger, T., McFarland, D.J., and Müller, K.-R. (eds.) (2007). Towards Brain-Computing Interfacing. Cambridge, MA: MIT Press.

[2] Wolpaw, J. and Wolpaw E.W. (eds.) (2012). Brain-Computer Interfaces: Principles and Practice. Oxford University Press.

[3] Farina et al. Introduction to Neural Engineering for Motor Rehabilitation. IEEE Press Series on Biomedical Engineering Book.

Evaluation period

E: exam date | R: revision date | 1: first session | 2: second session:

  • E1 08/01/2020 14:00h A05
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