Рекомендации по весьма актуальному сейчас подходу к анализу ЭЭГ и МЭГ - разделению осцилляций и апериодических 1/f компонентов:

Moritz Gerster, Gunnar Waterstraat, Vladimir Litvak, Klaus Lehnertz, Alfons Schnitzler, Esther Florin, Gabriel Curio, Vadim Nikulin. Separating neural oscillations from aperiodic 1/f activity: challenges and recommendations.
Neuroinformatics.
Accepted: 25 February 2022. Published online: 07 April 2022. https://doi.org/10.1007/s12021-022-09581-8

"In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them."

#methods #EEG #MEG #guidelines

"dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods"

Andreas Keil, Edward M. Bernat, Michael X. Cohen, Mingzhou Ding, Monica Fabiani, Gabriele Gratton, Emily S. Kappenman, Eric Maris, Kyle E. Mathewson, Richard T. Ward, Nathan Weisz. Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series. Psychophysiology, Volume 59, Issue 5 e14052. First published: 10 April 2022. https://doi.org/10.1111/psyp.14052 [ Free Access ]

#methods #EEG #MEG #guidelines

Olaf Hauk, Matti Stenroos, Matthias Treder. Towards an Objective Evaluation of EEG/MEG Source Estimation Methods - The Linear Approach. NeuroImage. Available online 4 April 2022, 119177. https://doi.org/10.1016/j.neuroimage.2022.119177 [Open access]

Highlights

• We provide a tutorial and evaluation of MNE-type and beamforming methods
• We highlight the importance of resolution matrix, point-spread and cross-talk
• We present intuitive resolution metrics to evaluate and compare methods
• We applied these tools to five MNE-type methods and two beamformers
• Point-spread localization error can be low but cross-talk is fundamentally limited

#methods #EEG #MEG

"many analyses in neuroscience neglect to test new theoretical models against known biological facts. Some of these analyses use circular reasoning to present existing knowledge as new discovery. ... I estimate that the problem has affected roughly three thousand studies over the last decade"

Rubinov, M. (2022, April 11). Circular and unified analysis in network neuroscience. https://doi.org/10.31219/osf.io/mdqak

#methods

Новое достижение c-VEP BCI: выбор сразу из 120 команд с помощью четырех специально подобранных псевдослучайных кодовых последовательностей зрительных стимулов. При этом на выбор одной команды уходила всего одна секунда.

Qingyu Sun, Li Zheng, Weihua Pei, Xiaorong Gao, Yijun Wang. A 120-Target Brain-Computer Interface Based on Code-Modulated Visual Evoked Potentials. Journal of Neuroscience Methods. Available online 12 April 2022, 109597. https://doi.org/10.1016/j.jneumeth.2022.109597

Но странно, почему опубликовано в довольно скромном и не очень профильном журнале, а не в JNE или каком-нибудь совсем пафосном. Может быть, смотреть на эти стимулы было слишком утомительно, как нередко получается с c-VEP? (Пока читал только абстракт).

#methods #noninvasive_BCIs #BCI_papers

"We propose augmentation-based source-free adaptation (ASFA), which consists of two parts: 1) source model training, where a novel data augmentation approach is proposed for MI EEG signals to improve the cross-subject generalization performance of the source model; and, 2) target model training, which simultaneously considers uncertainty reduction for domain adaptation and consistency regularization for robustness. ASFA only needs access to the source model parameters, instead of the raw EEG data, thus protecting the privacy of the source domain. ... This is the first work on completely source-free domain adaptation for EEG-based BCIs."

K. Xia, L. Deng, W. Duch and D. Wu. Privacy-Preserving Domain Adaptation for Motor Imagery-based Brain-Computer Interfaces. IEEE Transactions on Biomedical Engineering. 19 April 2022. https://doi.org/10.1109/TBME.2022.3168570

#methods #noninvasive_BCIs #BCI_classifiers #domain_adaptation

"we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor."

Musellim S, Han DK, Jeong JH, Lee SW. Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces. arXiv preprint arXiv:2204.07358. 2022 Apr 15. https://doi.org/10.48550/arXiv.2204.07358

#methods #noninvasive_BCIs #BCI_classifiers #domain_adaptation #BCI_papers

"most self-paced approaches apply a window function on the continuous EEG signal and split it into long segments for further analysis. As a result, the system has a high latency. To reduce the system latency, we propose an algorithm based on the time series prediction concept and use the data of the previously received time samples to predict the upcoming time samples. Our predictor is an encoder-decoder (ED) network built with long short-term memory (LSTM) units. The onsets of the MI commands are detected shortly by comparing the incoming signal with the predicted signal. The proposed method is validated on dataset IVc from BCI competition III. The simulation results show that the proposed algorithm improves the average F1-score achieved by the winner of the competition by 26.7% for latencies shorter than one second."

Ayoobi N, Sadeghian EB. A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm. arXiv preprint arXiv:2204.05450. 2022 Apr 12. https://arxiv.org/abs/2204.05450

#methods #noninvasive_BCIs #BCI_classifiers #self_paced_BCIs #BCI_papers

Новое руководство Стивена Лака, автора замечательных книг и ведущего курсов по обработке ЭЭГ данных, связанных с событиями (ERP), в открытом доступе:

Luck, S. J. (2022). Applied Event-Related Potential Data Analysis. LibreTexts. https://socialsci.libretexts.org/Bookshelves/Psychology/Book%3A_Applied_Event-Related_Potential_Data_Analysis_(Luck)

Стивен Лак об этой книге (длинный тред): https://twitter.com/stevenjluck/status/1526237286513266688?t=a72vJ8v_4FvvoVnhApQZ8g&s=19

"my approach in this book was inspired by Mike X Cohen’s excellent book, Analyzing Neural Time Series Data: Theory and Practice."

#neuro_edu #methods #manuals