DSML KZ members present a double-input CNN for P300 spellers

The 24th International Conference on Digital Signal Processing (DSP2023) took place in Greece this summer, featuring papers on digital signal processing and machine learning. DSML KZ members Zangar Yermaganbet @goofymonarch and @Ayana_Mussabayeva, together with NU professors, presented a paper on a double-input CNN used in spelling systems for people with amyotrophic lateral sclerosis.

A P300 Speller flashes symbols on a screen. When the user sees the symbol they want to type light up, it is treated as the target response. The task becomes binary classification: either the signal is a target and the user wants to type the highlighted symbol, or it is not.

The authors generate two types of spectrograms from EEG brain signals using independent component analysis and the Fourier transform, then pass the spectrograms through a double-input CNN.

The reported results show that the method is more effective than classical classifiers used in BCI systems, such as SVM and LDA.

The full paper is openly available on ResearchGate.