Brain Signals + Evolutionary Computation = Human Competitive Brain Computer Interfaces


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The keyboard and mouse provide us with reliable, but unnatural forms of input, being primitive transducer of muscular movement. People who lack muscle control cannot used them.  Wouldn’t it be nice some day to be able to replace the mouse and keyboard with systems capable of directly interpreting the intentions of computer users from their brain activity?

This is the goal of the field of Brain-Computer Interfaces (BCI). Unfortunately, this goal is hampered by a number of problems: brain signals are typically extremely noisy, they vary in location and temporal dynamics from subject to subject, they depend on the age, tiredness, attention, food and drug intake of subjects, etc. So, even the best BCIs are extremely slow and prone to misinterpret user intentions.

Theoretical part:

In this lecture I will briefly review the different approaches to BCI, with particular attention to non-invasive EEG-based BCIs, highlighting their difficulties and limitations. I will then illustrate a number of cases from our own research in the Essex BCI group, where evolutionary algorithms and genetic programming in particular have helped develop systems which are competitive with human-designed ones, thereby accelerating the development of practical BCI technology.

Lab:

TBA

The tutorial will be given by Prof. Riccardo Poli.