Curated Papers > Highly Cited Authoritative Literature on "Brain-Computer Interfaces"

EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces.

Literature Information

DOI	10.1088/1741-2552/aace8c
PMID	29932424
Journal	Journal of neural engineering
Impact Factor	3.8
JCR Quartile	Q2
Publication Year	2018
Times Cited	504
Keywords	Brain-Computer Interface, Convolutional Neural Network, Electroencephalogram, Feature Extraction, Classification
Literature Type	Journal Article, Research Support, U.S. Gov't, Non-P.H.S.
ISSN	1741-2552
Pages	056013
Issue	15(5)
Authors	Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, Brent J Lance

TL;DR

This study introduces EEGNet, a compact convolutional neural network designed to classify EEG signals across multiple brain-computer interface (BCI) paradigms, demonstrating superior generalization and performance compared to existing methods, especially with limited training data. The findings highlight EEGNet's ability to learn interpretable features relevant to diverse BCI tasks, thus enhancing the adaptability of EEG-based communication systems.

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Brain-Computer Interface · Convolutional Neural Network · Electroencephalogram · Feature Extraction · Classification

Abstract

OBJECTIVE Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible.

APPROACH In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR).

MAIN RESULTS We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms. In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features.

SIGNIFICANCE Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.

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Primary Questions Addressed

1. How does EEGNet's performance compare to other existing CNN architectures specifically designed for EEG signal classification?
2. What are the implications of using depthwise and separable convolutions in EEGNet for feature extraction in different BCI paradigms?
3. In what ways can EEGNet be adapted or improved for real-time applications in brain-computer interfaces?
4. What challenges might arise when applying EEGNet to new or less-studied BCI paradigms, and how could they be addressed?
5. How do the visualization techniques employed in EEGNet contribute to the interpretability of the model's decision-making process in EEG analysis?

Key Findings

Key Insights

1. Research Background and Objective
   Brain-computer interfaces (BCIs) represent a groundbreaking method for enabling direct communication between humans and computers through neural activity, primarily utilizing electroencephalogram (EEG) signals. These signals vary significantly across different BCI paradigms, often necessitating tailored feature extractors and classifiers for each unique signal type. This specialization limits the adaptability of existing models across various tasks. The goal of this research is to develop a single, compact convolutional neural network (CNN) architecture, termed EEGNet, that can effectively classify EEG signals from multiple BCI paradigms while maintaining a high level of performance and compactness.

2. Main Methods and Findings
   EEGNet employs depthwise and separable convolutions to create a model specifically designed for EEG signal processing. This architecture integrates established EEG feature extraction techniques, enhancing its applicability across different BCI tasks. The performance of EEGNet was rigorously evaluated against state-of-the-art algorithms across four distinct BCI paradigms: P300 visual-evoked potentials, error-related negativity (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR). The results demonstrate that EEGNet not only generalizes effectively across these paradigms, but it also achieves performance levels comparable to leading reference algorithms, even with limited training data.

3. Core Conclusions
   The findings indicate that EEGNet is capable of learning a diverse array of interpretable features across various BCI tasks, highlighting its robustness and flexibility as a single model for EEG signal classification. This capability is particularly significant given the challenges associated with training data limitations, suggesting that EEGNet can function effectively in scenarios where data availability is constrained.

4. Research Significance and Impact
   The introduction of EEGNet presents a significant advancement in the field of BCI, as it facilitates the use of a unified model across multiple paradigms, thereby reducing the need for paradigm-specific configurations. This not only streamlines the implementation of BCIs but also broadens their applicability in real-world scenarios. Furthermore, the ability to visualize the learned features of EEGNet enhances interpretability, making it easier for researchers and practitioners to understand the underlying processes of the model. The availability of the model on GitHub promotes further research and development in this area, potentially accelerating advancements in EEG-based BCI technologies and applications.

Literatures Citing This Work

1. Deep learning with convolutional neural networks for EEG decoding and visualization. - Robin Tibor Schirrmeister;Jost Tobias Springenberg;Lukas Dominique Josef Fiederer;Martin Glasstetter;Katharina Eggensperger;Michael Tangermann;Frank Hutter;Wolfram Burgard;Tonio Ball - Human brain mapping (2017)
2. Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks. - Ryan Hefron;Brett Borghetti;Christine Schubert Kabban;James Christensen;Justin Estepp - Sensors (Basel, Switzerland) (2018)
3. Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification. - Sławomir Opałka;Bartłomiej Stasiak;Dominik Szajerman;Adam Wojciechowski - Sensors (Basel, Switzerland) (2018)
4. Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals. - Yi Guo;Sinan Gok;Mesut Sahin - Frontiers in neuroscience (2018)
5. EEG classification of driver mental states by deep learning. - Hong Zeng;Chen Yang;Guojun Dai;Feiwei Qin;Jianhai Zhang;Wanzeng Kong - Cognitive neurodynamics (2018)
6. A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence. - Gabriel A Silva - Frontiers in neuroscience (2018)
7. A New Method for Detecting P300 Signals by Using Deep Learning: Hyperparameter Tuning in High-Dimensional Space by Minimizing Nonconvex Error Function. - Seyed Vahab Shojaedini;Sajedeh Morabbi;MohammadReza Keyvanpour - Journal of medical signals and sensors (2018)
8. Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals. - Zied Tayeb;Juri Fedjaev;Nejla Ghaboosi;Christoph Richter;Lukas Everding;Xingwei Qu;Yingyu Wu;Gordon Cheng;Jörg Conradt - Sensors (Basel, Switzerland) (2019)
9. Machine learning for MEG during speech tasks. - Demetres Kostas;Elizabeth W Pang;Frank Rudzicz - Scientific reports (2019)
10. Adaptive neural network classifier for decoding MEG signals. - Ivan Zubarev;Rasmus Zetter;Hanna-Leena Halme;Lauri Parkkonen - NeuroImage (2019)

... (494 more literatures)

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