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Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces.

Sai Kalyan Ranga Singanamalla¹, Chin-Teng Lin^1,2

¹Computational Intelligence and Brain Computer Interface Lab, School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.

Frontiers in Neuroscience

|April 19, 2021

Summary

This summary is machine-generated.

This study introduces a novel Spiking Neural Network (SNN) approach to generate synthetic electroencephalography (EEG) signals. This method effectively augments multi-class brain-computer interface (BCI) data with minimal original samples, improving classification performance.

Keywords:

brain computer interface data augmentation electroencephalography motor imagery spiking neural network

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Area of Science:

Neuroscience and Artificial Intelligence
Brain-Computer Interface (BCI) signal processing

Background:

Machine learning has advanced Brain-Computer Interfaces (BCIs), but electroencephalography (EEG) data acquisition is challenging due to setup complexity, artifacts, and time constraints for deep learning.
Existing artificial EEG signal generation methods often fail to preserve crucial signal biomarkers or require extensive training data, limiting their utility for multi-class augmentation.

Purpose of the Study:

To develop a generative model capable of creating synthetic multi-class EEG samples from limited original data while retaining essential signal biomarkers.
To address the limitations of current EEG data augmentation techniques for advanced machine learning classifiers in BCIs.

Main Methods:

Proposed a Spiking Neural Network (SNN) approach utilizing surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals.
The SNN model was trained on limited original samples to generate synthetic data for motor imagery (MI) and steady-state visually evoked potential (SSVEP) tasks.
Generated artificial EEG data were validated using classification and correlation metrics against original data.

Main Results:

The SNN-based generative model successfully reconstructed and augmented multi-class EEG signals, preserving key biomarkers even with few original samples.
Validation metrics confirmed the resemblance of synthetic data to original EEG signals.
The augmented dataset significantly enhanced the classification performance for motor imagery tasks.

Conclusions:

Spiking Neural Networks offer a viable solution for generating high-fidelity, multi-class synthetic EEG data from minimal samples.
This SNN-based augmentation strategy effectively overcomes limitations of existing methods, improving BCI performance and reducing data acquisition burdens.