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Regularization Enhances EEG-based Spatial Filtering for Single Trial Regression. 2017-7th International Brain-Computer Interface Con-ference

Andreas Meinel, Fabien Lotte, Michael Tangermann, Andreas Meinel, Fabien Lotte, Michael Tangermann, Tikhonov, A Meinel, F Lotte, M Tangermann
2017 unpublished
We introduce a framework for applying Tikhonov regular-ization to SPoC by restricting the solution space of filters.  ...  When capturing brain activity by an electroencephalogram (EEG), the Source Power Comodulation (SPoC) algorithm enables to compute spatial filters for the decoding of a continuous variable.  ...  For some aspects of the data analysis, the BBCI Toolbox was utilized [20] .  ... 

Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms

Fabien Lotte, Cuntai Guan
2011 IEEE Transactions on Biomedical Engineering  
Overall, the best RCSP algorithms were CSP with Tikhonov Regularization and Weighted Tikhonov Regularization, both proposed in this paper.  ...  One of the most popular feature extraction algorithms for Brain-Computer Interfaces (BCI) is Common Spatial Patterns (CSP).  ...  Blankertz for providing the electrode coordinates of BCI competition data, and Dr. Hamadicharef, Ms. Rosendale and anonymous reviewers for their constructive comments.  ... 
doi:10.1109/tbme.2010.2082539 pmid:20889426 fatcat:oiu4khktv5c3dfwsf3kqpb5oay

BCILAB: a platform for brain–computer interface development

Christian Andreas Kothe, Scott Makeig
2013 Journal of Neural Engineering  
Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for BCI methods development and testing by providing an organized collection of over 100 pre-implemented methods and  ...  The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), etc., and to derive real-time  ...  The U.S Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.  ... 
doi:10.1088/1741-2560/10/5/056014 pmid:23985960 fatcat:uu6n4bmq3nflhkdfgrvuyihdle

Manipulating Single-trial Motor Performance in Chronic Stroke Patients by Closed-loop Brain State Interaction

Andreas Meinel, Jan Sosulski, Stephan Schraivogel, Janine Reis, Michael Tangermann
2021 IEEE transactions on neural systems and rehabilitation engineering  
Based on a data-driven framework, we extracted robust oscillatory brain states from pre-trial intervals, which are predictive for the upcoming motor performance on the level of single trials.  ...  Based on the brain state estimate, i.e. whether the brain state predicts a good or bad upcoming performance, we implemented a novel gating strategy for the start of trials by selecting specifically suitable  ...  This fusion of features could be realized by an additional regression model and may allow for enhancing the trial-wise performance prediction.  ... 
doi:10.1109/tnsre.2021.3108187 pmid:34437067 fatcat:7tqg624ktrczfppome6mpxobwi

Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters

Andreas Meinel, Henrich Kolkhorst, Michael Tangermann
2019 IEEE transactions on neural systems and rehabilitation engineering  
Most machine learning approaches for the filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters.  ...  As the analysis method is not limited to a specific spatial filtering algorithm, it could be utilized for a wide range of neurotechnological applications, e.g., to select and monitor functionally relevant  ...  Spatial filtering is a widely established class of algorithms for single-trial EEG analysis [15] , aiming at the enhancement of the intrinsically low signal-to-noise ratio of multi-channel brain activity  ... 
doi:10.1109/tnsre.2019.2894914 pmid:30703030 fatcat:kch6e6bwjjddje7k2kisg772wq

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Amardeep Singh, Ali Abdul Hussain, Sunil Lal, Hans W. Guesgen
2021 Sensors  
This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system.  ...  We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.  ...  Park and Lee [138] extended the FBCSP with regularization. They obtained an optimized spatial filter for each frequency band using information from other subjects' trials.  ... 
doi:10.3390/s21062173 pmid:33804611 pmcid:PMC8003721 fatcat:xgqftpxyajfgtny4mml77k5kfy

EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices [article]

Simon Geirnaert, Servaas Vandecappelle, Emina Alickovic, Alain de Cheveigné, Edmund Lalor, Bernd T. Meyer, Sina Miran, Tom Francart, Alexander Bertrand
2021 arXiv   pre-print
In this paper, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.  ...  Based on these new insights, a multitude of auditory attention decoding (AAD) algorithms have been proposed, which could, combined with the appropriate speaker separation algorithms and miniaturized EEG  ...  In [3] , a first successful speech-based AAD algorithm based on unaveraged single-trial EEG data was proposed.  ... 
arXiv:2008.04569v3 fatcat:o4wlcmjiqbfztgsns3kkm5fq7q

Multisensory integration of dynamic emotional faces and voices: method for simultaneous EEG-fMRI measurements

Patrick D. Schelenz, Martin Klasen, Barbara Reese, Christina Regenbogen, Dhana Wolf, hb Yutaka Kato, Klaus Mathiak
2013 Frontiers in Human Neuroscience  
Combined EEG-fMRI analysis correlates time courses from single electrodes or independent EEG components with the hemodynamic response.  ...  EEG analysis showed high data quality with less than 10% trial rejection.  ...  SINGLE TRIAL EEG-fMRI COUPLING Single-trial induced alpha power at each event for 200-400 ms was calculated for the occipital and prefrontal cortex POI.  ... 
doi:10.3389/fnhum.2013.00729 pmid:24294195 pmcid:PMC3827626 fatcat:iceu22jw65akpjm3762quma6s4

Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research

Michael J. Crosse, Nathaniel J. Zuk, Giovanni M. Di Liberto, Aaron R. Nidiffer, Sophie Molholm, Edmund C. Lalor
2021 Frontiers in Neuroscience  
(EEG) data.  ...  Cognitive neuroscience, in particular research on speech and language, has seen an increase in the use of linear modeling techniques for studying the processing of natural, environmental stimuli.  ...  ACKNOWLEDGMENTS We would like to thank Andrew Anderson for his comments and suggestions on this manuscript.  ... 
doi:10.3389/fnins.2021.705621 pmid:34880719 pmcid:PMC8648261 fatcat:jk47xbfpljhkfeoc4teou5sjp4

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

Xin Huang, Yilu Xu, Jing Hua, Wenlong Yi, Hua Yin, Ronghua Hu, Shiyi Wang
2021 Frontiers in Neuroscience  
In an electroencephalogram- (EEG-) based brain–computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way.  ...  For each kind of signal processing approaches, we introduce their concepts and representative methods.  ...  Step 2: Task-unrelated spatial filters ŵ ∈ R N c 2 ×N f used for a target device can be estimated as ŵ = (xx T ) −1 xz T , where N c 2 and x ∈ R N c 2 ×N sa are the number of channels and a single trial  ... 
doi:10.3389/fnins.2021.733546 pmid:34489636 pmcid:PMC8417074 fatcat:6jyujrx4oncexmaiikwyfyzfwm

Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

Sven Dahne, Felix Bieszmann, Wojciech Samek, Stefan Haufe, Dominique Goltz, Christopher Gundlach, Arno Villringer, Siamac Fazli, Klaus-Robert Muller
2015 Proceedings of the IEEE  
In this paper we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as LFP, EEG, MEG  ...  The methods we discuss allow to extract information from neural data, which ultimately contributes to (a) better neuroscientific understanding, (b) enhance diagnostic performance and (c) discover neural  ...  In the study presented in [49] an EEG-based classifier for the detection of bandpower changes in a specific frequency range was derived based on a temporal (i.e. bandpass) filter as well as a spatial  ... 
doi:10.1109/jproc.2015.2425807 fatcat:q35q6gqbtjgypdkb35odjufdvu

Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

David Pascucci, Maria Rubega, Joan Rué-Queralt, Sebastien Tourbier, Patric Hagmann, Gijs Plomp
2021 Network Neuroscience  
Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors.  ...  We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively.  ...  The authors thank Guru Prasad Padmasola and Charles Quairiaux for providing guidelines and useful material for the analysis of the rat benchmark data, and Larry W.  ... 
doi:10.1162/netn_a_00218 pmid:35733424 pmcid:PMC9205420 fatcat:afy5trmgoffgppmiouqjuk3omq

RT-NET: real-time reconstruction of neural activity using high-density electroencephalography

Roberto Guarnieri, Mingqi Zhao, Gaia Amaranta Taberna, Marco Ganzetti, Stephan P. Swinnen, Dante Mantini
2020 Neuroinformatics  
RT-NET estimates a spatial filter for artifact removal and source activity reconstruction using a calibration dataset.  ...  This spatial filter is then applied to the hdEEG data as they are acquired, thereby ensuring low latencies and computation times.  ...  et al. 2013 ) and a spatial filter based on ICA (Guarnieri et al. 2018) , respectively.  ... 
doi:10.1007/s12021-020-09479-3 pmid:32720212 fatcat:svnxbx7bkfdwlmpnc6dqmff2ly

Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

B. J. Edelman, J. Meng, D. Suma, C. Zurn, E. Nagarajan, B. S. Baxter, C. C. Cline, B. He
2019 Science Robotics  
In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by more than 500% in the more realistic continuous pursuit task.  ...  Here, we present and validate a noninvasive framework using electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking.  ...  In the current work, we used Tikhonov regularization (Eq. 6).  ... 
doi:10.1126/scirobotics.aaw6844 pmid:31656937 pmcid:PMC6814169 fatcat:iqugppkd3bhvro7hoj4mjmbq64

Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm

Tuomas P. Mutanen, Johanna Metsomaa, Sara Liljander, Risto J. Ilmoniemi
2018 NeuroImage  
Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data.  ...  A B S T R A C T Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise-and artifactcontaminated channels and trials.  ...  Jaakko Nieminen for valuable suggestions regarding the manuscript. In addition, we would like to thank Prof. Sarvas for providing tools for the MATLAB demo package.  ... 
doi:10.1016/j.neuroimage.2017.10.021 pmid:29061529 fatcat:pnafrsagtraztekpwluhuvm54q
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