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

ABSTRACT: 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

SUBMITTER: Singanamalla SKR 

PROVIDER: S-EPMC8047134 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature