scholarly journals Data Augmentation for Deep-Learning-Based Electroencephalography

2020 ◽  
Author(s):  
Elnaz Lashgari ◽  
Dehua Liang ◽  
Uri Maoz
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Data Augmentation ◽  
Mental Workload ◽  
Seizure Detection ◽  
Generative Adversarial Networks ◽  
Sleep Stages ◽  
Sliding Windows ◽  
Visual Tasks ◽  
Decoding Accuracy

-BackgroundData augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.-New methodWe review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?-ResultsDA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. -Comparing with existing methodsPercent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average.-ConclusionsDA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis.

scholarly journals Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods

2021 ◽  
Vol 11 (9) ◽  
pp. 842
Author(s):  
Shruti Atul Mali ◽  
Abdalla Ibrahim ◽  
Henry C. Woodruff ◽  
Vincent Andrearczyk ◽  
Henning Müller ◽  
...  
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Data Augmentation ◽  
Image Data ◽  
Clinical Decision ◽  
Generative Adversarial Networks ◽  
Imaging Protocol ◽  
Image Domain ◽  
Style Transfer ◽  
Augmentation Techniques

Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.

scholarly journals Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting

2021 ◽  
Author(s):  
Qingyi Pan ◽  
Wenbo Hu ◽  
Ning Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Augmentation ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Experimental Results ◽  
Learning Methods ◽  
Forecasting Accuracy ◽  
Sliding Windows ◽  
Scheme Of Series

It is important yet challenging to perform accurate and interpretable time series forecasting. Though deep learning methods can boost forecasting accuracy, they often sacrifice interpretability. In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. By extracting series images from sliding windows of the time series, we design series saliency as a mixup strategy with a learnable mask between the series images and their perturbed versions. Series saliency is model agnostic and performs as an adaptive data augmentation method for training deep models. Moreover, by slightly changing the objective, we optimize series saliency to find a mask for interpretable forecasting in both feature and time dimensions. Experimental results on several real datasets demonstrate that series saliency is effective to produce accurate time-series forecasting results as well as generate temporal interpretations.

scholarly journals StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1497
Author(s):  
Harold Achicanoy ◽  
Deisy Chaves ◽  
Maria Trujillo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Industrial Applications ◽  
Generative Models ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Augmentation Strategy ◽  
Synthetic Images ◽  
The Impact

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

scholarly journals Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bo Pan ◽  
Wei Zheng
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Frequency Band ◽  
Computational Models ◽  
Data Augmentation ◽  
Learning Model ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Before And After ◽  
Deep Learning Model

Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition.

scholarly journals Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data

2021 ◽  
Vol 4 ◽  
Author(s):  
Sajila D. Wickramaratne ◽  
Md.Shaad Mahmud
Keyword(s):  
Deep Learning ◽  
Near Infrared ◽  
Data Augmentation ◽  
Learning Task ◽  
Generative Adversarial Networks ◽  
Functional Near Infrared Spectroscopy ◽  
Learning Classifier ◽  
Adversarial Networks ◽  
Task Classification ◽  
Specific Category

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used for mapping the functioning human cortex. fNIRS can be widely used in population studies due to the technology’s economic, non-invasive, and portable nature. fNIRS can be used for task classification, a crucial part of functioning with Brain-Computer Interfaces (BCIs). fNIRS data are multidimensional and complex, making them ideal for deep learning algorithms for classification. Deep Learning classifiers typically need a large amount of data to be appropriately trained without over-fitting. Generative networks can be used in such cases where a substantial amount of data is required. Still, the collection is complex due to various constraints. Conditional Generative Adversarial Networks (CGAN) can generate artificial samples of a specific category to improve the accuracy of the deep learning classifier when the sample size is insufficient. The proposed system uses a CGAN with a CNN classifier to enhance the accuracy through data augmentation. The system can determine whether the subject’s task is a Left Finger Tap, Right Finger Tap, or Foot Tap based on the fNIRS data patterns. The authors obtained a task classification accuracy of 96.67% for the CGAN-CNN combination.

scholarly journals A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 15945-15954 ◽  
Author(s):  
Zhiwen Zhang ◽  
Feng Duan ◽  
Jordi Sole-Casals ◽  
Josep Dinares-Ferran ◽  
Andrzej Cichocki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Data Augmentation ◽  
Learning Approach

Data Augmentation Using GANs for 3D Applications

2020 ◽  
pp. 229-269
Author(s):  
Ioannis Maniadis ◽  
Vassilis Solachidis ◽  
Nicholas Vretos ◽  
Petros Daras
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Data Availability ◽  
Generative Adversarial Networks ◽  
Property A ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Learning Techniques ◽  
The Subject ◽  
3D Applications

Modern deep learning techniques have proven that they have the capacity to be successful in a wide area of domains and tasks, including applications related to 3D and 2D images. However, their quality depends on the quality and quantity of the data with which models are trained. As the capacity of deep learning models increases, data availability becomes the most significant. To counter this issue, various techniques are utilized, including data augmentation, which refers to the practice of expanding the original dataset with artificially created samples. One approach that has been found is the generative adversarial networks (GANs), which, unlike other domain-agnostic transformation-based methods, can produce diverse samples that belong to a given data distribution. Taking advantage of this property, a multitude of GAN architectures has been leveraged for data augmentation applications. The subject of this chapter is to review and organize implementations of this approach on 3D and 2D imagery, examine the methods that were used, and survey the areas in which they were applied.

scholarly journals Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

2021 ◽  
Vol 13 (22) ◽  
pp. 4590
Author(s):  
Yunpeng Yue ◽  
Hai Liu ◽  
Xu Meng ◽  
Yinguang Li ◽  
Yanliang Du
Keyword(s):  
Deep Learning ◽  
High Precision ◽  
Ground Penetrating Radar ◽  
Data Augmentation ◽  
Least Square ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Radar Images ◽  
Adversarial Networks ◽  
Ground Penetrating

Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

scholarly journals Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG

2021 ◽  
Vol 15 ◽  
Author(s):  
Yu Pei ◽  
Zhiguo Luo ◽  
Ye Yan ◽  
Huijiong Yan ◽  
Jing Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Data Augmentation ◽  
Brain Area ◽  
Comparative Investigation ◽  
Classification Performance ◽  
Training Data ◽  
Learning Technology ◽  
Data Generation ◽  
Public Datasets

The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation (DA). Here, we proposed a DA method for the motor imagery (MI) EEG signal called brain-area-recombination (BAR). For the BAR, each sample was first separated into two ones (named half-sample) by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification (p < 0.01). To make a comparative investigation, we selected two common DA methods (noise-added and flipping), and the BAR method beat them (p < 0.05). Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM (p < 0.01), note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI.

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