Application of transfer learning in EEG decoding based on brain-computer interfaces: a review
The algorithms of electroencephalography (EEG) decoding are mainly based on machine
learning in current research. One of the main assumptions of machine learning is that …
learning in current research. One of the main assumptions of machine learning is that …
Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions
This paper presents a systematic literature review, providing a comprehensive taxonomy of
Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) …
Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) …
Generalized neural decoders for transfer learning across participants and recording modalities
SM Peterson, Z Steine-Hanson, N Davis… - Journal of Neural …, 2021 - iopscience.iop.org
Objective. Advances in neural decoding have enabled brain-computer interfaces to perform
increasingly complex and clinically-relevant tasks. However, such decoders are often …
increasingly complex and clinically-relevant tasks. However, such decoders are often …
Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data
Exoskeleton-based support for patients requires the learning of individual machine-learning
models to recognize movement intentions of patients based on the electroencephalogram …
models to recognize movement intentions of patients based on the electroencephalogram …
Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization
Objective. Recently, transfer learning (TL) and deep learning (DL) have been introduced to
solve intra-and inter-subject variability problems in brain-computer interfaces (BCIs) …
solve intra-and inter-subject variability problems in brain-computer interfaces (BCIs) …
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data
As the field of deep learning has grown in recent years, its application to the domain of raw
resting-state electroencephalography (EEG) has also increased. Relative to traditional …
resting-state electroencephalography (EEG) has also increased. Relative to traditional …
Interpretable functional specialization emerges in deep convolutional networks trained on brain signals
J Hammer, RT Schirrmeister, K Hartmann… - Journal of neural …, 2022 - iopscience.iop.org
Objective. Functional specialization is fundamental to neural information processing. Here,
we study whether and how functional specialization emerges in artificial deep convolutional …
we study whether and how functional specialization emerges in artificial deep convolutional …
A causal perspective on brainwave modeling for brain–computer interfaces
Objective. Machine learning (ML) models have opened up enormous opportunities in the
field of brain–computer Interfaces (BCIs). Despite their great success, they usually face …
field of brain–computer Interfaces (BCIs). Despite their great success, they usually face …
EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitation
N Kueper, SK Kim, EA Kirchner - arXiv preprint arXiv:2402.17790, 2024 - arxiv.org
Background: For an individualized support of patients during rehabilitation, learning of
individual machine learning models from the human electroencephalogram (EEG) is …
individual machine learning models from the human electroencephalogram (EEG) is …
Deep multimodal representation learning for noninvasive neural speech decoding
Decoding speech directly from brain activity is a rapidly developing research area with the
potential to improve communication for those unable to speak. Traditionally, neural …
potential to improve communication for those unable to speak. Traditionally, neural …