Noise Robustness of CNN and SNN for EEG Motor imagery classification

Abstract: As an able-bodied human, understanding what someone says during a phone call with a lot of background noise is usually a task that is quite easy for us as we are aware of what the information is we want to hear, e.g. the voice of the person we are talking to, and the information that is noise, e.g. music or ambient noise in the background. While dealing with noise of all kinds for most humans proves to be the easiest, it is a very hard task for algorithms to deal with noisy data. Unfortunately for some beneficial and interesting applications, like Brain Computer Interfaces (short BCI) based on Electroencephalography (short EEG) data, noise is a very prevalent problem that greatly hinders the progress of making BCIs for real-life applications. In this thesis, we investigate what effect noise added to EEG data has on the classification accuracy of one Spiking Neural Network and one Convolutional Neural Network based classifier for a motor imagery classification task. The thesis shows that already relatively small amounts of noise (10% of original data) can have strong effects on the classification accuracy of the chosen classifiers. It also provides evidence that SNN based models have a more stable classification accuracy for low amounts of noise. Still, their classification accuracy after that declines more rapidly, while CNN based classifiers show a more linear decline in classification accuracy