EEG data augmentation using Wasserstein GAN

2020 ◽  
Author(s):  
Ghaith Bouallegue ◽  
Ridha Djemal
Keyword(s):  
Data Augmentation ◽  
Eeg Data

EEG data augmentation: towards class imbalance problem in sleep staging tasks

2020 ◽  
Vol 17 (5) ◽  
pp. 056017
Author(s):  
Jiahao Fan ◽  
Chenglu Sun ◽  
Chen Chen ◽  
Xinyu Jiang ◽  
Xiangyu Liu ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Class Imbalance ◽  
Sleep Staging ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Eeg Data

scholarly journals EEG data augmentation for emotion recognition with a multiple generator conditional Wasserstein GAN

2021 ◽  
Author(s):  
Aiming Zhang ◽  
Lei Su ◽  
Yin Zhang ◽  
Yunfa Fu ◽  
Liping Wu ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Data Augmentation ◽  
Real Data ◽  
Training Data ◽  
Quality Data ◽  
Emotion Classification ◽  
High Quality ◽  
Proposed Model ◽  
Eeg Data ◽  
Substantial Progress

AbstractEEG-based emotion recognition has attracted substantial attention from researchers due to its extensive application prospects, and substantial progress has been made in feature extraction and classification modelling from EEG data. However, insufficient high-quality training data are available for building EEG-based emotion recognition models via machine learning or deep learning methods. The artificial generation of high-quality data is an effective approach for overcoming this problem. In this paper, a multi-generator conditional Wasserstein GAN method is proposed for the generation of high-quality artificial that covers a more comprehensive distribution of real data through the use of various generators. Experimental results demonstrate that the artificial data that are generated by the proposed model can effectively improve the performance of emotion classification models that are based on EEG.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

The Relation Between Aerobic Fitness and Cognitive Performance

2016 ◽  
Vol 30 (3) ◽  
pp. 102-113 ◽  
Author(s):  
Chun-Hao Wang ◽  
Chun-Ming Shih ◽  
Chia-Liang Tsai
Keyword(s):  
Cognitive Performance ◽  
Aerobic Fitness ◽  
Reaction Times ◽  
Mediation Model ◽  
Brain Potentials ◽  
Fitness Level ◽  
Eeg Data ◽  
Attention Performance ◽  
P3 Amplitude ◽  
The Relationship

Abstract. This study aimed to assess whether brain potentials have significant influences on the relationship between aerobic fitness and cognition. Behavioral and electroencephalographic (EEG) data was collected from 48 young adults when performing a Posner task. Higher aerobic fitness is related to faster reaction times (RTs) along with greater P3 amplitude and shorter P3 latency in the valid trials, after controlling for age and body mass index. Moreover, RTs were selectively related to P3 amplitude rather than P3 latency. Specifically, the bootstrap-based mediation model indicates that P3 amplitude mediates the relationship between fitness level and attention performance. Possible explanations regarding the relationships among aerobic fitness, cognitive performance, and brain potentials are discussed.

Analysis of the Multi-Channel Epileptiform EEG Using the Markov Chains Formalism

1989 ◽  
Vol 28 (03) ◽  
pp. 160-167 ◽  
Author(s):  
P. Penczek ◽  
W. Grochulski
Keyword(s):  
Dynamic Behaviour ◽  
Joint Probability ◽  
Drug Effects ◽  
Anticonvulsant Drug ◽  
Channel System ◽  
Markov Network ◽  
Eeg Data ◽  
Multi Level ◽  
Joint Probability Functions ◽  
Dependent State

Abstract:A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation
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