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Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Tracking alpha/mu dynamics ↔ EKFOT.m, the whole file · a weak match · score 0.68 · initial damping, process noise, measurement noise, covariance, optimized, oscillator
  2. [2] § Materials and methods › Extended Kalman filter based oscillator tracking ↔ EKFOT.m, the whole file · a weak match · score 0.65 · measurement noise covariances, Jacobians, damped, linearize, prediction, optimized
  3. [3] § Materials and methods › Extended Kalman filter based oscillator tracking ↔ SIM_EKFOT.m, the whole file · a weak match · score 0.54 · measurement noise covariances, linearize, uncertainty, prediction, trajectories, oscillator

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 85 lines · 2.4 KB · MIT · 2 matches

  1. function [J, Up] = EKFOT(X, Y, ignore, Fs)
  2. %EKFOT EKF oscillator tracking cost for GA optimization.
  3. %
  4. % [J, Up] = EKFOT(X, Y, ignore, Fs)
  5. %
  6. % Inputs
  7. % X : parameter vector [R2_ct, Q_ct, omega0, alpha0, P0scale]
  8. % R2_ct : continuous-time measurement noise intensity
  9. % Q_ct : continuous-time process noise intensity (scalar, shared)
  10. % omega0 : initial angular frequency [rad/s]
  11. % alpha0 : initial damping [1/s]
  12. % P0scale: scale factor for initial covariance
  13. % Y : signal (1 x N) or (N x 1)
  14. % ignore : number of initial samples to ignore in cost
  15. % Fs : sampling rate [Hz]
  16. %
  17. % Method
  18. % AR(2) resonator with time-varying omega, alpha estimated by EKF:
  19. % a1 = 2*exp(-alpha*Ts)*cos(omega*Ts)
  20. % a2 = -exp(-2*alpha*Ts)
  21. % State z = [x(k); x(k-1); omega; alpha]
  22. % y(k) = x(k) + v(k)
  23. %
  24. % Cost
  25. % J = ||innovation(ignore:end)|| / ||Y(ignore:end)||
  27. % --- Continuous -> discrete scaling ---
  28. Ts = 1/Fs;
  29. R2_ct = X(1);
  30. Q_ct = X(2);
  31. R2 = R2_ct * Ts; % measurement noise variance per sample
  32. R1 = Q_ct * Ts * eye(4); % process noise covariance per sample
  34. omega = X(3); % [rad/s] (despite old comment "rad/sample")
  35. alpha = X(4); % [1/s]
  36. pin = X(end); % P0 scale
  38. % --- Init ---
  39. z = [0; 0; omega; alpha]; % [x(k); x(k-1); omega; alpha]
  40. P = pin * eye(4);
  41. H = [1 0 0 0];
  43. N = numel(Y);
  44. Y = double(Y(:)'); % ensure row
  45. e = zeros(1, N);
  47. for k = 3:N
  48. % --- Predict ---
  49. a11 = 2*exp(-z(4)*Ts)*cos(z(3)*Ts);
  50. a12 = -exp(-2*z(4)*Ts);
  51. A2 = [a11, a12; 1, 0];
  53. x_pred = A2 * z(1:2);
  54. z_pred = [x_pred; z(3); z(4)];
  56. % --- Linearize (Jacobian) ---
  57. F = eye(4);
  58. F(1:2,1:2) = A2;
  60. F(1,3) = -2*exp(-z(4)*Ts)*sin(z(3)*Ts)*Ts * z(1);
  61. F(1,4) = -2*Ts*exp(-z(4)*Ts)*cos(z(3)*Ts)*z(1) ...
  62. + 2*Ts*exp(-2*z(4)*Ts)*z(2);
  64. P_pred = F*P*F' + R1;
  66. % --- Update ---
  67. y_pred = H * z_pred;
  68. inov = Y(k) - y_pred;
  70. S = H*P_pred*H' + R2; % scalar
  71. K = (P_pred*H') / S; % 4x1
  73. z = z_pred + K*inov;
  74. P = (eye(4)-K*H)*P_pred;
  76. % --- Wrap omega (discrete-time angle wrapping) ---
  77. z(3) = wrapToPiLocal(z(3)*Ts)/Ts;
  79. e(k) = inov;
  80. end
  82. Up = e(ignore:end);
  83. J = norm(Up) / norm(Y(ignore:end));
  85. end

EKFOT.m at commit e37663f, under MIT · at the source

Overview

Authors: Kyriaki Kostoglou1, Gernot R Müller-Putz1,2
ORCID iDs: Kyriaki Kostoglou
  1. Institute of Neural Engineering, Graz University of Technology, Graz, Austria
  2. BioTechMed, Graz, Austria
Institutions: Graz University of Technology (Austria); BioTechMed-Graz (Austria)
Journal: PLoS computational biology, volume 22, issue 4, article e1014112
Dates: received 10 August 2025; accepted 9 March 2026; published online 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014112 · PMID 41920806 · PMCID PMC13065009 · OpenAlex W7147058873
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Machine learning
MeSH: Alpha Rhythm*, Brain-Computer Interfaces*, Sensorimotor Cortex*, Calibration, Computational Biology, Electroencephalography, Humans (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Brain-computer interfaces (BCIs) depend on the reliable decoding of brain activity, yet key rhythms like alpha and mu are not spectrally static and can shift with cognitive and motor demands. Here, we investigated within-session changes in instantaneous alpha/mu frequency and magnitude during motor-related BCI calibration using an oscillator-tracking framework based on an extended Kalman filter (EKF). We applied this method to four public EEG datasets spanning motor execution and imagery tasks. Across all datasets, we observed consistent increases in mu instantaneous frequency and magnitude over central sensorimotor regions, indicative of motor engagement and possible training-related neuroplasticity. In contrast, posterior and surrounding cortical areas often showed alpha slowing, suggestive of declining vigilance or cognitive fatigue, or alternatively, resource reallocation via inhibition of task-irrelevant regions. These opposing spatial trends underscore the functional heterogeneity of alpha-band activity across the cortex. Our results highlight the potential of real-time frequency tracking not only to improve decoding accuracy but also to monitor neurophysiological state changes and guide adaptive adjustments in BCI calibration paradigms.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

kyriakikostoglou/EKF-Oscillator-Tracking

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f6af8352d39a6ad99d40618acfcff9fc37a6a295, 1 April 2026
Languages: MATLAB (6)
Size: 8 files, 6 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Optimization Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/kyriakikostoglou/EKF-Oscillator-Tracking

graz-bci/EKF-Oscillator-Tracking

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e37663f3bc5bc04c754dab0747afceb8a7d326c8, 1 April 2026
Languages: MATLAB (6)
Size: 8 files, 6 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Optimization Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/graz-bci/EKF-Oscillator-Tracking

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

All datasets used in this study are publicly available. The Schalk2004 dataset is available from PhysioNet (https://physionet.org/content/eegmmidb/1.0.0/). The Dreyer2023 dataset is available on the general-purpose open-access repository Zenodo (https://doi.org/10.5281/zenodo.8089820). The Schwarz2020 and the Pulferer2022 dataset can be accessed via the BNCI Horizon 2020 database (http://bnci-horizon-2020.eu/database/data-sets, #27 and #32, respectively). No new data was generated in this study. The analysis code is publicly available at: https://github.com/kyriakikostoglou/EKF-Oscillator-Tracking and https://github.com/graz-bci/EKF-Oscillator-Tracking.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 7 MeSH terms, 62 references.

Cite

This paper

Kostoglou, K., & Müller-Putz, G. R. (2026). Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training. PLoS computational biology, 22(4), e1014112. https://doi.org/10.1371/journal.pcbi.1014112

BibTeX

@article{kostoglou2026opposing,
author = {Kostoglou, Kyriaki and Müller-Putz, Gernot R},
title = {{Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training}},
journal = {PLoS computational biology},
year = {2026},
month = apr,
volume = {22},
number = {4},
pages = {e1014112},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014112},
url = {https://doi.org/10.1371/journal.pcbi.1014112},
pmid = {41920806},
pmcid = {PMC13065009}
}

RIS

TY - JOUR
AU - Kostoglou, Kyriaki
AU - Müller-Putz, Gernot R
TI - Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/04/01
VL - 22
IS - 4
SP - e1014112
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014112
UR - https://doi.org/10.1371/journal.pcbi.1014112
LA - en
ER -

CSL-JSON

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"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "4",
"page": "e1014112",
"DOI": "10.1371/journal.pcbi.1014112",
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