Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions [PDF]
Frontiers in Human NeuroscienceThis paper presents a systematic literature review, providing a comprehensive taxonomy of Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) techniques within the context of Few-Shot Learning (FSL) for EEG signal ...
Chirag Ahuja, Divyashikha Sethia
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A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification
IEEE Access, 2019 Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has become a hotspot in the research field of brain computer interface (BCI).
Gaowei Xu +8 more
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EEGGAN-Net: enhancing EEG signal classification through data augmentation [PDF]
Frontiers in Human NeuroscienceBackgroundEmerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities.
Jiuxiang Song +4 more
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IEEE Access, 2020 The analysis of EEG signal is a relevant problem in health informatics, and its development can help in detection of epileptic's seizures. The diagnosis is based on classification of EEG signal.
Jan Rabcan +3 more
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Motor imagery EEG signal classification with a multivariate time series approach. [PDF]
Biomed Eng Online, 2023 Background Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy.
Velasco I +4 more
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Transferred Subspace Learning Based on Non-negative Matrix Factorization for EEG Signal Classification [PDF]
Frontiers in Neuroscience, 2021 EEG signal classification has been a research hotspot recently. The combination of EEG signal classification with machine learning technology is very popular.
Aimei Dong, Zhigang Li, Qiuyu Zheng
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Motor imagery EEG signal classification using novel deep learning algorithm. [PDF]
Sci RepElectroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring.
Mathiyazhagan S, Devasena MSG.
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Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification. [PDF]
Front Neurol, 2020 Electroencephalogram (EEG) signals contain vital information on the electrical activities of the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy diagnosis, accurate classification of different epileptic states, is of
Gao Y, Gao B, Chen Q, Liu J, Zhang Y.
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Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms. [PDF]
Sensors (Basel), 2021 Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources.
Aquino-Brítez D +6 more
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An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection. [PDF]
Sci RepDetection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes.
Kantipudi MVVP +4 more
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