ArticleFrontiers in neuroinformatics2025
Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.
Article in Frontiers in neuroinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- MAF-SleepNet: A Multimodal Attention-Enhanced Fusion Network for Automatic Multi-Class Sleep Disorder Classification from Polysomnography.Diagnostics (Basel, Switzerland) · 2026Article
- Influence of EEG Signal Augmentation Methods on Classification Accuracy of Motor Imagery Events.Sensors (Basel, Switzerland) · 2026Article
- Leveraging Cross-Subject Transfer Learning and Signal Augmentation for Enhanced RGB Color Decoding from EEG Data.Brain sciences · 2026Article
- A survey on data augmentation for EEG-based emotion recognition and cognitive workload decoding.Frontiers in neuroscience · 2026Review
- Transformer-based deep learning model for predicting fNIRS short-channel signals.Neurophotonics · 2025Article
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Authors and funding
6 authors.
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Abstract
Analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI) recently demonstrated that deep neural networks (DNNs) can be effectively used for investigation of time sequences for physical actions (PA) classification. In this study, the relatively simple DNN with fully connected network (FCN) components and convolutional neural network (CNN) components was considered to classify finger-palm-hand manipulations each from the grasp-and-lift (GAL) dataset. The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size
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