Towards an API for EEG-Based Imagined Speech classification

In this paper, imagined speech classification is performed with an implementation in Python and using scikit-learn library, to create a toolbox intended for real-time classification. To this aim, the Discrete Wavelet Transform with the mother function Biorthogonal 2.2 is used to then compute the instantaneous and Teager energy distribution for feature extraction. Then, random forest is implemented as a classifier with 10-folds cross-validation. The set of experiments consists of imagined speech classification, linguistic activity and inactivity classification and subjects identification. The experiments were performed using a dataset of 27 subjects which imagined 33 repetitions of 5 words in Spanish up, down, left, right and select. The accuracy obtained with the models were 0.77, 0.78 and 0.98 respectively for each task. The high accuracy rates obtained as a result attest for the feasibility of the proposed method for subject identification.