scholarly journals Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiaodong Zhang ◽  
Zhufeng Lu ◽  
Teng Zhang ◽  
Hanzhe Li ◽  
Yachun Wang ◽  
...  
Keyword(s):  
Test Performance ◽  
Real Data ◽  
Neural Mass Model ◽  
Mass Model ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Equivalent Dipole ◽  
Neural Mass ◽  
Qualitative Level ◽  
Surface Eeg

Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI.

scholarly journals Mean-Field Models for EEG/MEG: From Oscillations to Waves

2021 ◽  
Author(s):  
Áine Byrne ◽  
James Ross ◽  
Rachel Nicks ◽  
Stephen Coombes
Keyword(s):  
Gap Junction ◽  
Large Scale ◽  
Mean Field ◽  
Coarse Grained ◽  
Neural Mass Model ◽  
Neuron Network ◽  
Mass Model ◽  
Mean Field Model ◽  
Neural Mass ◽  
The Rich

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

Principal dynamic mode analysis of neural mass model for the identification of epileptic states

2016 ◽  
Vol 26 (11) ◽  
pp. 113118 ◽  
Author(s):  
Yuzhen Cao ◽  
Liu Jin ◽  
Fei Su ◽  
Jiang Wang ◽  
Bin Deng
Keyword(s):  
Mode Analysis ◽  
Neural Mass Model ◽  
Dynamic Mode ◽  
Mass Model ◽  
Neural Mass

scholarly journals Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

2021 ◽  
pp. 1-7
Author(s):  
Sheikh Md. Rabiul Islam ◽  
◽  
Md. Shakibul Islam ◽  
Keyword(s):  
Signal Generation ◽  
Neural Mass Model ◽  
Pathophysiological Mechanism ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Mass Model ◽  
Detailed Model ◽  
Simulation Based ◽  
Neural Mass ◽  
The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

scholarly journals JANE: Jointly Adversarial Network Embedding

2020 ◽  
Author(s):  
Liang Yang ◽  
Yuexue Wang ◽  
Junhua Gu ◽  
Chuan Wang ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
Link Prediction ◽  
Real Data ◽  
Semantic Space ◽  
Network Embedding ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Adversarial Network ◽  
Node Clustering ◽  
Topology Information ◽  
Embedding Methods

Motivated by the capability of Generative Adversarial Network on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module. The overall objective function of JANE is defined in a min-max form, which can be optimized via alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).

Sound Field Reconstruction in Rooms with Deep Generative Models

2021 ◽  
Vol 263 (5) ◽  
pp. 1527-1538
Author(s):  
Xenofon Karakonstantis ◽  
Efren Fernandez Grande
Keyword(s):  
Plane Waves ◽  
Sound Field ◽  
Real Data ◽  
Generative Models ◽  
Random Wave ◽  
Generative Adversarial Network ◽  
Underlying Distribution ◽  
Adversarial Network ◽  
Reconstruction Methods ◽  
Free Region

The characterization of Room Impulse Responses (RIR) over an extended region in a room by means of measurements requires dense spatial with many microphones. This can often become intractable and time consuming in practice. Well established reconstruction methods such as plane wave regression show that the sound field in a room can be reconstructed from sparsely distributed measurements. However, these reconstructions usually rely on assuming physical sparsity (i.e. few waves compose the sound field) or trait in the measured sound field, making the models less generalizable and problem specific. In this paper we introduce a method to reconstruct a sound field in an enclosure with the use of a Generative Adversarial Network (GAN), which s new variants of the data distributions that it is trained upon. The goal of the proposed GAN model is to estimate the underlying distribution of plane waves in any source free region, and map these distributions from a stochastic, latent representation. A GAN is trained on a large number of synthesized sound fields represented by a random wave field and then tested on both simulated and real data sets, of lightly damped and reverberant rooms.

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