Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces - Singanamalla, Sai Kalyan Ranga; Lin, Chin-Teng (Frontiers in Neuroscience, 2021)
With the advent of advanced machine learning methods, the performance of Brain-computer interfaces (BCIs) has improved unprecedentedly. However, Electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time-consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of samples for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG samples with as few available trials as possible and generate multi-class whilst retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as Spiking Neural Network (SNN), a biologically closer artificial neural network, communicates via spiking behaviour, we propose a spiking neural network (SNN)-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original samples. The network was employed for augmenting Motor Imagery (MI) and steady-state visually evoked potential (SSVEP) data. This artificial data is further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.