Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces

Frontiers in Neuroscience

Chin-Teng Lin¹  Sai Kalyan Ranga Singanamalla² 

[1] Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia;Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia;

关键词: spiking neural network;    electroencephalography;    brain computer interface;    motor imagery;    data augmentation;   

DOI  :  10.3389/fnins.2021.651762

来源: DOAJ

【 摘 要 】

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 while 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 behavior, we propose an 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. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.

【 授权许可】

Unknown