Deep EEG super-resolution: Upsampling EEG spatial resolution with Generative Adversarial Networks

Corley, Isaac; Huang, Yufei

doi:10.1109/bhi.2018.8333379

Cited by 59 publications

(31 citation statements)

References 11 publications

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“…DL can effectively deal with nonlinear and non-stationary data, and learn underlying features from signals. Some deep learning methods are employed for the classification of EEG signals (Cecotti and Graser, 2010 ; Bashivan et al, 2015 ; Corley and Huang, 2018 ). Convolutional neural networks (CNNs) have been widely used in MI-EEG classification on account of their ability to learn features from local receptive fields.…”

Section: Introductionmentioning

confidence: 99%

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

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A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

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Section: Introductionmentioning

confidence: 99%

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

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“…In terms of initializing such classifier models, our approach has the capability of constructing a subject-invariant baseline as well. To further extend this idea, besides a discriminative approach, ongoing recent work explores EEG data augmentation using GANs [56]- [61]. Such data augmentation would provide significant insights for model training with subject-invariant augmented EEG data, which is basically a generative approach to our problem of interest.…”

Section: Discussionmentioning

confidence: 99%

Learning Invariant Representations From EEG via Adversarial Inference

et al. 2020

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Discovering and exploiting shared, invariant neural activity in electroencephalogram (EEG) based classification tasks is of significant interest for generalizability of decoding models across subjects or EEG recording sessions. While deep neural networks are recently emerging as generic EEG feature extractors, this transfer learning aspect usually relies on the prior assumption that deep networks naturally behave as subject-(or session-) invariant EEG feature extractors. We propose a further step towards invariance of EEG deep learning frameworks in a systemic way during model training. We introduce an adversarial inference approach to learn representations that are invariant to inter-subject variabilities within a discriminative setting. We perform experimental studies using a publicly available motor imagery EEG dataset, and state-ofthe-art convolutional neural network based EEG decoding models within the proposed adversarial learning framework. We present our results in cross-subject model transfer scenarios, demonstrate neurophysiological interpretations of the learned networks, and discuss potential insights offered by adversarial inference to the growing field of deep learning for EEG. INDEX TERMS Adversarial learning, brain-computer interface, deep neural networks, electroencephalogram, invariant representation, motor imagery.

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“…In [9], the authors propose deep EEG super-resolution using a GAN. The model is applied to a small number of EEG channel data to interpolate other channel signals using motor imagery dataset from [28].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…This can be for a variety of reasons including the requirement for careful per-subject and per-session calibration. This makes EEG BCI experiments time-consuming, expensive and difficult to operate within the usually short amount of time experimental subjects can perform EEG experiments [9]. In addition to these issues, Fig.…”

Section: Introductionmentioning

confidence: 99%

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

Aznan

Atapour-Abarghouei

Bonner

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subject and session data variance, long and arduous calibration processes and predictive generalisation issues across different subjects or sessions. This implies that many downstream applications, including Steady State Visual Evoked Potential (SSVEP) based classification systems, can suffer from a shortage of reliable data. Generating meaningful and realistic synthetic data can therefore be of significant value in circumventing this problem. We explore the use of modern neural-based generative models trained on a limited quantity of EEG data collected from different subjects to generate supplementary synthetic EEG signal vectors, subsequently utilised to train an SSVEP classifier. Extensive experimental analysis demonstrates the efficacy of our generated data, leading to improvements across a variety of evaluations, with the crucial task of cross-subject generalisation improving by over 35% with the use of such synthetic data.

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Deep EEG super-resolution: Upsampling EEG spatial resolution with Generative Adversarial Networks

Cited by 59 publications

References 11 publications

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Learning Invariant Representations From EEG via Adversarial Inference

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

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