Universitat Internacional de Catalunya - BarcelonaComputing, Robotics and Bionics 1
Main language of instruction: English
Other languages of instruction: Catalan, Spanish
| Sem.1 | MO | 16:00 18:00 | I3 | |
| Sem.1 | TH | 18:00 20:00 | I3 |
Head instructor
Dr. Xavier MARIMON - xmarimon@uic.es
Office hours
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.
To access to the course it is required to have completed the following subjects:
First year subjects
Calculus
Second year subjects
Computing*
Fundamentals of Electronic Systems
Signals and Systems
Biostatistics
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
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.
3.1.1.1 Single Unit Activity (SUA).
3.1.1.2 Local Field Potentials (LFP) and Multi Unit Activity (MUA).
3.1.2 Extracellular recordings.
3.1.2.1 Electroencephalogram (EEG).
3.1.2.2 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.
| TRAINING ACTIVITY | METHODOLOGY | COMPETENCES |
| 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 |
The final mark for the subject will be calculated as follows:
Nota=0,4·Nef +0,3·Nlab+0,3·Ntreb
where
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] 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.
E: exam date | R: revision date | 1: first session | 2: second session: