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   Articles with "eeg decoding" as a keyword



EEG decoding of the target speaker in a cocktail party scenario: considerations regarding dynamic switching of talker location.

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recommendations! Published in 2019 at "Journal of neural engineering"

DOI: 10.1088/1741-2552/ab0cf1

Abstract: OBJECTIVE It has been shown that attentional selection in a simple dichotic listening paradigm can be decoded offline by reconstructing the stimulus envelope from single-trial neural response data. Here, we test the efficacy of this… read more here.

Keywords: improve decoding; envelope; attention; eeg decoding ... See more keywords

Foundation models for EEG decoding: current progress and prospective research

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recommendations! Published in 2025 at "Journal of Neural Engineering"

DOI: 10.1088/1741-2552/ae17e9

Abstract: Objective. Electroencephalography (EEG) records the spontaneous electrical activity in the brain. Despite the growing application of deep learning in EEG decoding, traditional methods still rely heavily on supervised learning, which is often limited by task… read more here.

Keywords: eeg decoding; eeg fms; research; models eeg ... See more keywords

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3035347

Abstract: Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain–computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain… read more here.

Keywords: motor imagery; spatial representation; eeg; eeg decoding ... See more keywords

WavTSK: An Interpretable Fuzzy Network With Learnable Wavelet-Based Feature Extraction for Motor Imagery EEG Decoding

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recommendations! Published in 2025 at "IEEE Transactions on Fuzzy Systems"

DOI: 10.1109/tfuzz.2025.3623122

Abstract: Decoding motor imagery (MI) from electroencephalogram (EEG) signals is a cornerstone of brain–computer interface (BCI) systems. However, existing methods often face a critical tradeoff between decoding accuracy and model interpretability, limiting their applicability in real-world… read more here.

Keywords: learnable wavelet; eeg decoding; feature; fuzzy ... See more keywords

EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization

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recommendations! Published in 2022 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"

DOI: 10.1109/tnsre.2022.3230250

Abstract: Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail to capture long-term dependencies for EEG decoding. In this paper, we propose a compact Convolutional Transformer, named… read more here.

Keywords: visualization; eeg conformer; eeg decoding; convolutional transformer ... See more keywords

Enhanced Online Continuous Brain-Control by Deep Learning-Based EEG Decoding

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recommendations! Published in 2025 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"

DOI: 10.1109/tnsre.2025.3591254

Abstract: Objective: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces… read more here.

Keywords: deep learning; brain; based eeg; eeg decoding ... See more keywords

MSEI-ENet: A Multi-Scale EEG-Inception Integrated Encoder Network for Motor Imagery EEG Decoding

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recommendations! Published in 2025 at "Brain Sciences"

DOI: 10.3390/brainsci15020129

Abstract: Background: Due to complex signal characteristics and distinct individual differences, the decoding of a motor imagery electroencephalogram (MI-EEG) is limited by the unsatisfactory performance of suboptimal traditional models. Methods: A subject-independent model named MSEI-ENet is… read more here.

Keywords: motor imagery; eeg decoding; eeg inception; multi scale ... See more keywords