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Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Raza, Haider and Chowdhury, Anirban and Bhattacharyya, Saugat and Samothrakis, Spyros (2020) Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance. In: International Joint Conference on Neural Networks (IJCNN 2020), 2020-07-19 - 2020-07-24, Glasgow. (In Press)

WCCI_2020_NSL.pdf - Accepted Version

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Research and development of new machine learning techniques to augment the performance of Brain-computer Interfaces (BCI) have always been an open area of interest among researchers. The need to develop robust and generalised classifiers has been one of the vital requirements in BCI for realworld application. EEGNet is a compact CNN model that had been reported to be generalised for different BCI paradigms. In this paper, we have aimed at further improving the EEGNet architecture by employing Neural Structured Learning (NSL) that taps into the relational information within the data to regularise the training of the neural network. This would allow the EEGNet to make better predictions while maintaining the structural similarity of the input. In addition to better performance, the combination of EEGNet and NSL is more robust, works well with smaller training samples and requires on separate feature engineering, thus saving the computational cost. The proposed approach had been tested on two standard motor imagery datasets: the first being a two-class motor imagery dataset from Graz University and the second is the 4-class Dataset 2a from BCI competition 2008. The accuracy has shown that our combined EEGNet an NSL approach is superior to the sole EEGNet model.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 23 Mar 2020 10:56
Last Modified: 15 Jan 2022 01:32

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