Universitat Internacional de Catalunya

Computing, Robotics and Bionics 1

Computing, Robotics and Bionics 1
First semester
Main language of instruction: English

Other languages of instruction: Catalan, Spanish,

Teaching staff

An appointment with the teacher must be arranged by institutional email.


The first course will focus on the field of artificial intelligence applied to the biomedical world. The basic algorithms of supervised and unsupervised machine learning will be studied.

The second part of the course will focus on the field of the neural engineering, a discipline at the frontier between neuroscience and engineering. On one side, understanding how the brain works allows developing engineering applications and therapies of extremely high impact. On the other side, development of new measurement and data analysis techniques contributes to advance our knowledge about the brain.



Pre-course requirements

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

First year subjects 


Second year subjects


Fundamentals of Electronic Systems

Signals and Systems


Third year subjects

Neuroscience Applied to Orthoprosthesis (Simultaneous. Recommended, but not mandatory)

*It is required to have achieved a good level of coding and computational thinking


  1. To describe the differences between Artificial Intelligence and Machine Learning.
  2. To evaluate the current and potential limitations of Artificial Intelligence.
  3. To know how to distinguish between supervised and unsupervised Machine Learning.
  4. To understand the functioning of a brain-computer interface (BCI).
  5. To know the main techniques for acquiring neuronal signals.


  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.

Learning outcomes

Know and be able to use the main clustering and classification algorithms.

Decide and know how to apply the most appropriate algorithm to process a new data set.

Describe and know how to apply the spike sorting algorithm in records of intracellular electrical activity.

Have the ability to apply basic preprocessing to an electroencephalogram signal (EEG). 

Have the ability to apply basic preprocessing to a bold functional MRI signal (fMRI).




Block 1. Computation. Classical Machine Learning.

0. Introduction to 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). 

1.2 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 (regression and classification).

2.1 Regression algorithms.

2.1.1 Regression. Linear, non-liner and logistic.

2.1.2 Generalized Linear Model (GLM).

2.1.3 Regression trees.

2.2 Classification algorithms.

2.1.1 Support Vector Machines (SVM).

2.1.2 k-Nearest Neighbor (kNN).

2.1.3 Naïve Bayes.

2.1.4 Linear discriminant Analysis (LDA).

2.1.5 Neural networks.

2.1.6 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 neuroprosthetics.

1. Brain Computer Interfaces (BCI).

2. Origin of the brain signal.

3. Recording and processing the brain activity.

3.1 Electrical brain measures.

        3.1.1. Intracellular and extracellular recordings. Single Unit Activity (SUA).      Local Field Potentials (LFP) and Multi Unit Activity (MUA).

3.1.2 Extracellular recordings. Electroencephalogram (EEG). Electrocorticography (ECoG) (Epidural, Subdural).

3.2. Magnetic brain measures.

3.2.1 Magnetic Resonance Imaging (MRI).

3.3. Metabolic brain measures.

3.3.1 Functional Magnetic Resonance Imaging (fMRI).

3.3.2 Functional near-infrared spectroscopy (fNIRS).


Experimental activities: spike Sorting, raster plot decoding, tuning curves and population vector, EMG signal processing, BOLD Signal (fMRI) processing.


Teaching and learning activities

In person

Cooperative learning plays a significant role in the Bachelor’s degree in Bioengineering, its approach is based on organising activities inside the classroom so they become both a social and an academic learning experience. This type of learning depends on an exchange of information between students, who are motivated both to achieve their own learning and to increase the achievements of others. This activity covers practicums undertaken in a laboratory environment. 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. An activity for outside the classroom. During this activity, students complete exercises autonomously, without the presence of a lecturer/professor. At this stage many questions always arise, but since they cannot be asked immediately then the student has to make more effort to understand them 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. Seminars are a didactic meeting in which a specialist, in this case the lecturer/professor, or a prestigious professional, etc. interacts with attendees in relation to shared work, seeking the dissemination of knowledge or to share a project/research project Group work is an essential tool in today’s society. In the field of bioengineering in which design and production processes are not carried out by an individual, it is essential to learn how to work as part of a team Individual work, involving study, the search for information, data processing and the internalisation of knowledge will allow students to consolidate their learning. CB1 CB2 CB3 CB4 CB5 CE1 CE12 CE15 CE16 CE17 CE19 CE21 CE3 CE5 CE8 CG1 CG10 CG2 CG3 CG4 CG5 CG6 CG7 CG8 CG9 CT2 CT3 CT4 CT5 CT6 CT7

Evaluation systems and criteria

In person

The final mark for the subject will be calculated as follows:

Nota=0,4·Nef +0,3·Nlab+0,3·Ntreb


Nef : Final exam mark

Nlab : Lab mark

Ntreb : Coursework mark


 No partial exam.

To apply for the apt, it is essential to carry out the subject’s laboratory practicums.


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 possible).
  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] Duda et al. 2000. Pattern Classification. Second Edition.. Wiley-Interscience publication.

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

[3] 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.


Evaluation period

E: exam date | R: revision date | 1: first session | 2: second session:
  • E1 13/01/2023 P2A01 10:00h