Projekt förslag

Title: Brain signal pattern recognition

Theme: Algorithms, neuroinformatics, machine learning

General subject:

Pattern recognition and machine learning have significantly advanced the field of biological data analysis leading in consequence to the development of effective diagnostic tools and supporting research efforts. The contribution of novel pattern recognition methods has been particularly appreciated in brain data mining as this new approach allows for exploratory search for spatio-temporal patterns in large quantities of high-dimensional nonstationary recordings of brain activity.The emerging trend is to combine machine learning techniques with brain-inspired computing algorithms to address increasingly demanding objectives of brain signal analysis in novel applications.

Below you can find a set of alternative projects (they can be treated individually or in combination).

Possible projects:

• Survey of the state-of-the-art methods that involve a machine learning and/or brain-inspired connectionist approach to a well-defined class of brain pattern signal recognition problems 
• Select or propose a method with novel aspects, alternatively select and compare a few existing approaches (prototypes) to a specific brain signal pattern recognition problem, e.g. electroencephalograpic (EEG) signal classification for a brain-computer interface, search for epileptic seizure precursors in high-dimensional brain signal recordings, multivariate analysis and identification of distributed spatial patterns of brain activity for diagnostic purposes.
• Discuss key challenges, emerging trends and propose future applications for brain signal recognition methodology.

Supervisor: Pawel Herman