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

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.

[1]  Mubarak Shah,et al.  Generative Adversarial Networks Conditioned by Brain Signals , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[2]  Fabien Lotte.  Generating Artificial EEG Signals To Reduce BCI Calibration Time , 2011 .

[3]  Klaus-Robert Müller,et al.  A convolutional neural network for steady state visual evoked potential classification under ambulatory environment , 2017, PloS one.

[4]  Francisco J. Pelayo,et al.  Trends in EEG-BCI for daily-life: Requirements for artifact removal , 2017, Biomed. Signal Process. Control..

[5]  Gao Xiaorong,et al.  Outcome of the BCI-competition 2003 on the Graz data set , 2003 .

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  John Thomas,et al.  Deep learning-based classification for brain-computer interfaces , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[8]  Hayit Greenspan,et al.  GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.

[9]  David Haussler,et al.  Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.

[10]  Rajesh P. N. Rao.  Brain-Computer Interfacing: An Introduction , 2010 .

[11]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.

[12]  Toby P. Breckon,et al.  On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[13]  Ying Liu,et al.  Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks , 2018, ArXiv.

[14]  Ruslan Salakhutdinov,et al.  Learning Deep Generative Models , 2009 .

[15]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[16]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[17]  Daan Wierstra,et al.  Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.

[18]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[19]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[20]  Louis B. Rall,et al.  Automatic differentiation , 1981 .

[21]  Günter Edlinger,et al.  Can Dry EEG Sensors Improve the Usability of SMR, P300 and SSVEP Based BCIs? , 2012 .

[22]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[23]  Wei-Ye Zhao,et al.  Data Encoding Visualization based Cognitive Emotion Recognition with AC-GAN Applied for Denoising , 2018, 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

[24]  Yun Luo,et al.  EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[25]  Søren K. Andersen,et al.  Driving steady-state visual evoked potentials at arbitrary frequencies using temporal interpolation of stimulus presentation , 2015, BMC Neuroscience.

[26]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[27]  Omid Dehzangi,et al.  Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification , 2018, BioMed research international.

[28]  Yufei Huang,et al.  Deep EEG super-resolution: Upsampling EEG spatial resolution with Generative Adversarial Networks , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[29]  Daniel Sánchez Morillo,et al.  Dry EEG Electrodes , 2014, Sensors.

[30]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[31]  Tonio Ball,et al.  EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals , 2018, ArXiv.

[32]  Jdel.R. Millan,et al.  On the need for on-line learning in brain-computer interfaces , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[33]  Toby P. Breckon,et al.  Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[34]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[35]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.