Brain Computer Interfaces: Overview and Practical Examples
Course duration:6 h
Course consists of two parts. First two lectures give an overview on current advances in BCI field, applications, devices and experiments. After that we focus on one particular type of BCI, briefly introduce concept of machine learning and
its application to the BCI problem. During final practice session we will analyze piece of EEG data and see what it takes to extract useful information from it.
Lecture 1:Idea of BCI
What is BCI? Why to develop it?
Lecture gives an overview on major applications of BCI, describes different types of neuroimaging devices and most impressive experiments conducted in the field.
Lecture 2: Types of BCI
We will look at different ideas how to process information we read from the brain to solve BCI problem. After that we will focus on EEG-based devices and study their properties.
Lecture 3: Machine Learning and BCI
One way to extract information from the signal is through excessive use of Machine Learning (ML). During this lecture we will familiarize ourselves with this concept and see how our data and our goal can be described and solved in terms of ML.
Practice: Classification task on real data
Starting from raw pre-recorded piece of EEG-data we will go through all basic steps needed to feed our information to the classification algorithm.Then we will kindly ask it to extract useful information from the data and see what happens.
Prerequisites
There are no strict requirements, points below are "nice to have"
- Familiarity with basic algebra and probabilities might be handy
- Basic programming skills (Matlab/Octave)
Tutor
Mr. Ilya Kuzovkin
Country: Estonia
Place of employment: PhD student, University of Tartu, Estonia
Spheres of researches: Machine Learning in general and it's applications to Brain-Computer Interface systems. Data mining. Text algorithms. Cryptology. Bioinformatics.
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