Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain–computer interface

Wonjun Ko, Eunjin Jeon, Jee Seok Yoon, Heung Il Suk

Research output: Contribution to journalArticlepeer-review

Abstract

Convolutional neural networks (CNNs), which can recognize structural/configuration patterns in data with different architectures, have been studied for feature extraction. However, challenges remain regarding leveraging advanced deep learning methods in BCIs. We focus on problems of small-sized training samples and interpretability of the learned parameters and leverages a semi-supervised generative and discriminative learning framework that effectively utilizes synthesized samples with real samples to discover class-discriminative features. Our framework learns the distributional characteristics of EEG signals in an embedding space using a generative model. By using artificially generated and real EEG signals, our framework finds class-discriminative spatio-temporal feature representations that help to correctly discriminate input EEG signals. It is noteworthy that the framework facilitates the exploitation of real, unlabeled samples to better uncover the underlying patterns inherent in a user’s EEG signals. To validate our framework, we conducted experiments comparing our method with conventional linear models by utilizing variants of three existing CNN architectures as generator networks and measuring the performance on three public datasets. Our framework exhibited statistically significant improvements over the competing methods. We investigated the learned network via activation pattern maps and visualized generated artificial samples to empirically justify the stability and neurophysiological plausibility of our model.

Original languageEnglish
Article number4587
JournalScientific reports
Volume12
Issue number1
DOIs
Publication statusPublished - 2022 Dec

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain–computer interface'. Together they form a unique fingerprint.

Cite this