A Data Augmentation Scheme for Geometric Deep Learning in Personalized Brain–Computer Interfaces

Kalaganis, Fotis; Laskaris, Nikolaos A.; Chatzilari, Elisavet; Nikolopoulos, Spiros; Kompatsiaris, Ioannis

doi:10.1109/access.2020.3021580

Cited by 21 publications

(15 citation statements)

References 30 publications

(23 reference statements)

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“…While we allow for t > 1, the purpose of our data-preprocessing scheme is to simplify the batchingnot to augment the data. In particular, in contrast to data-augmentation schemes designed for deep learning in data-scarse applications [19], we do not generate new, artificial samples.…”

Section: Methods For Targeted Deep Learningmentioning

confidence: 99%

Targeted Deep Learning: Framework, Methods, and Applications

Huang¹,

Lederer²

2021

Preprint

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Deep learning systems are typically designed to perform for a wide range of test inputs. For example, deep learning systems in autonomous cars are supposed to deal with traffic situations for which they were not specifically trained. In general, the ability to cope with a broad spectrum of unseen test inputs is called generalization. Generalization is definitely important in applications where the possible test inputs are known but plentiful or simply unknown, but there are also cases where the possible inputs are few and unlabeled but known beforehand. For example, medicine is currently interested in targeting treatments to individual patients; the number of patients at any given time is usually small (typically one), their diagnoses/responses/... are still unknown, but their general characteristics (such as genome information, protein levels in the blood, and so forth) are known before the treatment. We propose to call deep learning in such applications targeted deep learning. In this paper, we introduce a framework for targeted deep learning, and we devise and test an approach for adapting standard pipelines to the requirements of targeted deep learning. The approach is very general yet easy to use: it can be implemented as a simple data-preprocessing step. We demonstrate on a variety of real-world data that our approach can indeed render standard deep learning faster and more accurate when the test inputs are known beforehand.Preprint. Under review.

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Section: Methods For Targeted Deep Learningmentioning

confidence: 99%

Targeted Deep Learning: Framework, Methods, and Applications

Huang¹,

Lederer²

2021

Preprint

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“…. Finally, the graph interpolation is a Dirichlet problem on the graph, and its solution depends on the following linear equation (Kalaganis et al, 2020):…”

Section: Graph Empirical Mode Decompositionmentioning

confidence: 99%

Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis

Chen

Hao

et al. 2022

Front. Neurosci.

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Gifted children and normal controls can be distinguished by analyzing the structural connectivity (SC) extracted from MRI data. Previous studies have improved classification accuracy by extracting several features of the brain regions. However, the limited size of the database may lead to degradation when training deep neural networks as classification models. To this end, we propose to use a data augmentation method by adding artificial samples generated using graph empirical mode decomposition (GEMD). We decompose the training samples by GEMD to obtain the intrinsic mode functions (IMFs). Then, the IMFs are randomly recombined to generate the new artificial samples. After that, we use the original training samples and the new artificial samples to enlarge the training set. To evaluate the proposed method, we use a deep neural network architecture called BrainNetCNN to classify the SCs of MRI data with and without data augmentation. The results show that the data augmentation with GEMD can improve the average classification performance from 55.7 to 78%, while we get a state-of-the-art classification accuracy of 93.3% by using GEMD in some cases. Our results demonstrate that the proposed GEMD augmentation method can effectively increase the limited number of samples in the gifted children dataset, improving the classification accuracy. We also found that the classification accuracy is improved when specific features extracted from brain regions are used, achieving 93.1% for some feature selection methods.

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“…In this part, we introduce some cases of DA application to passive BCIs. Kalaganis et al (2020) proposed a DA method based on graphempirical mode decomposition (EMD) to generate EEG data, which combines the advantage of multiplex network model and graph variant of classical empirical mode decomposition. They designed a sustained attention driving task in a virtual reality environment, while realizing the automatic detection for the state of humans using graph CNN.…”

Section: Data Augmentation Strategy For Eeg Classificationmentioning

confidence: 99%

Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review

He¹,

Liu²,

Zhu

et al. 2021

Front. Hum. Neurosci.

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Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

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A Data Augmentation Scheme for Geometric Deep Learning in Personalized Brain–Computer Interfaces

Cited by 21 publications

References 30 publications

Targeted Deep Learning: Framework, Methods, and Applications

Targeted Deep Learning: Framework, Methods, and Applications

Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis

Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review

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