A review on transfer learning in EEG signal analysis

Z Wan, R Yang, M Huang, N Zeng, X Liu - Neurocomputing, 2021 - Elsevier
Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …

Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states

AE Hramov, VA Maksimenko, AN Pisarchik - Physics Reports, 2021 - Elsevier
Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

Riemannian procrustes analysis: transfer learning for brain–computer interfaces

PLC Rodrigues, C Jutten… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Objective: This paper presents a Transfer Learning approach for dealing with the statistical
variability of electroencephalographic (EEG) signals recorded on different sessions and/or …

Error correction regression framework for enhancing the decoding accuracies of ear-EEG brain–computer interfaces

NS Kwak, SW Lee - IEEE transactions on cybernetics, 2019 - ieeexplore.ieee.org
Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface
(BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical …

Decoding working memory-related information from repeated psychophysiological EEG experiments using convolutional and contrastive neural networks

J Żygierewicz, RA Janik, IT Podolak… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Extracting reliable information from electroencephalogram (EEG) is difficult
because the low signal-to-noise ratio and significant intersubject variability seriously hinder …

Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain–computer interfaces

J Sosulski, M Tangermann - Journal of neural engineering, 2022 - iopscience.iop.org
Objective. Covariance matrices of noisy multichannel electroencephalogram (EEG) time
series data provide essential information for the decoding of brain signals using machine …

An effective model for human cognitive performance within a human-robot collaboration framework

KM Rabby, M Khan, A Karimoddini… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
With advances in technologies, robots can be employed in collaboration with human for
completing the shared objective (s). This paper proposes a novel time-variant human …

User Identity Protection in EEG-based Brain-Computer Interfaces: Supplementary Material

L Meng, X Jiang, J Huang, W Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
A brain-computer interface (BCI) establishes a direct communication pathway between the
brain and an external device. Electroencephalogram (EEG) is the most popular input signal …

General principles of machine learning for brain-computer interfacing

I Iturrate, R Chavarriaga, JR Millán - Handbook of clinical neurology, 2020 - Elsevier
Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into
commands that can be executed by an artificial device. This enables the possibility of …

UMM: Unsupervised mean-difference maximization

J Sosulski, M Tangermann - arXiv preprint arXiv:2306.11830, 2023 - arxiv.org
Many brain-computer interfaces make use of brain signals that are elicited in response to a
visual, auditory or tactile stimulus, so-called event-related potentials (ERPs). In visual ERP …