ML-BCI: Machine Learning for Brain Computer Interface (2008)

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CCG07-UC3M/ESP-3286. Project funded by CAM and UC3M (2008)

This project belongs to the brain-computer interface (BCI) research field. According to the project proposal, we have developed a tool for aquiring and processing electroencefalographic (EEG) data in real time. It also learns patterns from the EEG data by means of Neural Networks and allows a person to use his thoughts to control a cursor on the screen.

We have also proposed and tested new ideas to improve the accuracy of EEG classification for BCIs. In particular, we have developped a method based on transition detection between thoughts. Transition detection improves accuracy by reducing the number of classes. The moving window also improves accuracy by computing the final classification from a set of predictions (the window), instead of a single one.

The group has also started to apply evolutionary multiobjective machine learning techniques to EEG classification.

 Brain signals in the screen


 User wearing the BCI sensors