Classification of Electroencephalogram (EEG) signals during the performance a two class Motor Imagery (MI) tasks by different subjects.
In this project we present different methods to handle such problem, using both ML and DL approaches, and test them on data from 2008 BCI competition data set 2b.
The first method relies on feature extraction through Filter Bank Common Spatial Pattern (FBCSP) and classification via Support Vector Machine (SVM).
The second one uses the features extracted with FBCSP and classifies them with the use of a Long Short Term Memory (LSTM) architecture.
Priscilla Cortese
Alessandro Piani