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Optimal Size of Electrocorticography Grids for Classification of Hand Movements.

Code ↔ Paper

9 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 9 matches
  1. [1] § Materials and Methods › Classification ↔ subopt/hillclimbing.py, lines 467–560 · score 0.86 · min max scaler, cross validation, scikit-learn, SVC, Machine, SVM
  2. [2] § Materials and Methods › Classification ↔ subopt/exhaustive.py, lines 132–211 · score 0.86 · min max scaler, cross validation, scikit-learn, SVC, Machine, SVM
  3. [3] § Materials and Methods › Preprocessing ↔ examples/synth_data.py, lines 131–197 · score 0.80 · power feature, high frequency band, epochs, amplitude, noise, Preprocessing
  4. [4] § Materials and Methods › Performance-area Trade-off ↔ subopt/base.py, lines 315–377 · score 0.71 · inter electrode distance, electrode diameter, surface area, IED, height, width
  5. [5] § Materials and Methods › Exhaustive Subgrid Search ↔ subopt/exhaustive.py, lines 132–211 · score 0.69 · cross validation splits, exhaustive search, SVM, model, fold, trained
  6. [6] § Materials and Methods › Exhaustive Subgrid Search ↔ subopt/hillclimbing.py, lines 467–560 · score 0.63 · cross validation splits, SVM, model, fold, trained, stochastic
  7. [7] § Materials and Methods › Exhaustive Subgrid Search ↔ subopt/exhaustive.py, lines 23–124 · score 0.60 · possible subgrid, exhaustive search, iteratively, space, rows, configuration
  8. [8] § Results › Performance-area Analysis ↔ subopt/base.py, lines 315–377 · score 0.59 · Inter Electrode Distance, Electrode Diameter, surface area, IED, class, score
  9. [9] § Materials and Methods › Participants ↔ examples/synth_data.py, lines 14–49 · score 0.50 · sensorimotor cortex, ECoG

Paper

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The authors' code

Python · 314 lines · 11 KB · MIT · 3 matches

  1. # -------------------------------------------------------------
  2. # Subgrid Optimization
  3. # Copyright (c) 2026
  4. # Dirk Keller, Nick Ramsey's Lab, University Medical Center Utrecht, University Utrecht
  5. # Licensed under the MIT License [see LICENSE for detail]
  6. # -------------------------------------------------------------
  8. from copy import copy
  9. from typing import Tuple, Union, Any, Optional, Type, List
  11. import numpy as np
  12. import pandas as pd
  13. from sklearn.base import MetaEstimatorMixin, TransformerMixin
  14. from sklearn.model_selection import BaseCrossValidator
  15. from sklearn.pipeline import Pipeline
  16. from sklearn.utils.validation import check_is_fitted as sklearn_is_fitted
  17. from tqdm import tqdm
  19. from .base import BaseOptimizer
  20. from .utils import compute_subgrid_dimensions
  23. class ExhaustiveSearch(MetaEstimatorMixin, TransformerMixin):
  24. """
  25. Performs an exhaustive search over all possible subgrids in a given channel grid,
  26. evaluating each using a specified objective function.
  28. Parameters:
  29. -----------
  30. :param channel_grid: numpy.ndarray
  31. The grid of channel IDs.
  32. :param func: callable
  33. The objective function, which takes a mask and a list of included channels and returns a metric score.
  34. :param verbose: bool, default = False
  35. If set to True, outputs progress messages during the optimization process.
  37. Returns:
  38. --------
  39. :return: None
  40. """
  42. def __init__(self, channel_grid, func, verbose=False) -> None:
  43. self.channel_grid = channel_grid
  44. self.objective = func
  45. self.verbose = verbose
  47. def run(self) -> Tuple[int, np.ndarray]:
  48. """
  49. Executes the exhaustive search to find the best subgrid spatially constrained
  50. channel combination.
  52. Iteratively searches through the whole subgrid space.
  53. Records and returns the evaluation history.
  55. Returns:
  56. --------
  57. :return: tuple
  58. A tuple containing the best score achieved, the corresponding mask
  59. for the best subgrid, and a DataFrame detailing the evaluation results
  60. for each configuration.
  61. """
  62. best_score = 0.0
  63. best_mask = None
  65. # Main loop over the number of starting positions
  66. height, width = self.channel_grid.shape
  67. subgrids = self.generate_subgrids(height, width)
  69. mask_template = np.zeros_like(self.channel_grid, dtype=bool)
  71. pbar = range(len(subgrids))
  72. if self.verbose:
  73. pbar = tqdm(
  74. pbar, desc="Exhaustive Search", postfix={"score": f"{best_score:.6f}"}
  75. )
  77. for idx in pbar:
  78. start_row, start_col, end_row, end_col = subgrids[idx]
  79. mask = copy(mask_template)
  81. mask[start_row:end_row, start_col:end_col] = True
  83. # Calculate the score for the current subgrid
  84. score = self.objective(mask)
  86. # Check if this is the best score so far
  87. if score > best_score:
  88. best_score = score
  89. best_mask = mask
  91. if self.verbose:
  92. pbar.set_postfix({"score": f"{best_score:.6f}"})
  94. return best_score, best_mask
  96. @staticmethod
  97. def generate_subgrids(
  98. grid_height: int, grid_width: int
  99. ) -> List[Tuple[int, int, int, int]]:
  100. """
  101. Generates all possible subgrids within a given grid height and width.
  102. Each subgrid is defined by its starting and ending coordinates.
  104. Parameters:
  105. -----------
  106. :param grid_height: int
  107. The height of the grid.
  108. :param grid_width: int
  109. The width of the grid.
  111. Returns:
  112. --------
  113. :return: List[Tuple[int, int, int, int]]
  114. A list of tuples, where each tuple contains the coordinates of a subgrid in the format
  115. (start_row, start_col, end_row, end_col).
  116. """
  117. subgrids = []
  118. for start_row in range(grid_height + 1):
  119. for start_col in range(grid_width + 1):
  120. for end_row in range(start_row, grid_height + 1):
  121. for end_col in range(start_col, grid_width + 1):
  122. if start_row < end_row and start_col < end_col:
  123. subgrids.append((start_row, start_col, end_row, end_col))
  124. return subgrids
  127. ########################################################################################
  128. ###################### Scikit-Learn: SpatialStochasticHillClimbing #####################
  129. ########################################################################################
  132. class SpatialExhaustiveSearch(BaseOptimizer):
  133. """
  134. Implements a spatially constrained exhaustive search for finding
  135. global best channel combinations within a grid based on a given metric.
  137. Parameters:
  138. -----------
  139. :param grid: numpy.ndarray
  140. The grid structure specifying how channels are arranged.
  141. :param estimator: Union[Any, Pipeline]
  142. The machine learning estimator or pipeline to evaluate
  143. channel combinations.
  144. :param metric: str, default = 'f1_weighted'
  145. The metric to optimize, compatible with scikit-learn metrics.
  146. :param cv: Union[BaseCrossValidator, int], default = 10
  147. Cross-validation splitting strategy, can be a fold number
  148. or a scikit-learn cross-validator.
  149. :param n_jobs: int, default = 1
  150. Number of parallel jobs to run during cross-validation.
  151. '-1' uses all available cores.
  152. :param seed: Optional[int], default = None
  153. Seed for randomness, ensuring reproducibility.
  154. :param verbose: Union[bool, int], default = False
  155. Enables verbose output during the optimization process.
  157. Methods:
  158. --------
  159. - fit:
  160. Fit the model to the data, search through the spatial
  161. constrained channel combinations.
  162. - transform:
  163. Apply the mask obtained from the search to transform the data.
  164. - run:
  165. Execute the spatial exhaustive search.
  166. - evaluate_candidates:
  167. Evaluates the selected features using cross-validation or train-test split.
  168. - objective_function:
  169. Evaluate each candidate configuration and return their scores.
  170. - elimination_plot:
  171. Generate and save a plot visualizing the performance across different subgrid sizes.
  172. - importance_plot:
  173. Generate and save a heatmap visualizing the importance of each channel.
  175. Notes:
  176. ------
  177. This implementation is semi-compatible with the scikit-learn
  178. framework, which builds around two-dimensional feature matrices.
  179. To use this transformation within a scikit-learn Pipeline, the
  180. four dimensional data must eb flattened after the first dimension
  181. [samples, features]. For example, scikit-learn's FunctionTransformer can
  182. achieve this.
  184. Examples:
  185. ---------
  186. The following example shows how to retrieve a feature mask for
  187. a synthetic data set.
  189. # >>> import numpy as np
  190. # >>> from sklearn.svm import SVC
  191. # >>> from sklearn.pipeline import Pipeline
  192. # >>> from sklearn.preprocessing import MinMaxScaler
  193. # >>> from sklearn.datasets import make_classification
  194. # >>> from subopt.exhaustive import SpatialExhaustiveSearch
  195. # >>> X, y = make_classification(n_samples=100, n_features=8 * 4 * 100)
  196. # >>> X = X.reshape((100, 8, 4, 100))
  197. # >>> grid = np.arange(1, 33).reshape(X.shape[1:3])
  198. # >>> estimator = Pipeline([('scaler', MinMaxScaler()), ('svc', SVC())])
  200. # >>> shc = SpatialExhaustiveSearch(grid, estimator, verbose=True)
  201. # >>> shc.fit(X, y)
  202. # >>> print(shc.mask_)
  203. array([[False True False False], [False False False False], [ True True False False], [False False False True],
  204. [False False False False], [False False False False], [False False True False], [False False False False]])
  205. # >>> print(shc.score_)
  206. 26.966666666666672
  208. Returns:
  209. --------
  210. :return: None
  211. """
  213. def __init__(
  214. self,
  215. # General and Decoder
  216. grid: np.array,
  217. estimator: Union[Any, Pipeline],
  218. metric: str = "f1_weighted",
  219. cv: Union[BaseCrossValidator, int] = 10,
  220. # Misc
  221. n_jobs: int = 1,
  222. seed: Optional[int] = None,
  223. verbose: Union[bool, int] = False,
  224. ) -> None:
  225. super().__init__(grid, estimator, metric, cv, n_jobs, seed, verbose)
  227. def fit(
  228. self, X: np.ndarray, y: np.ndarray = None
  229. ) -> Type["SpatialExhaustiveSearch"]:
  230. """
  231. Fit method optimizes the channel combination with
  232. Spatial Exhaustive Search.
  234. Parameters:
  235. -----------
  236. :param X: numpy.ndarray
  237. Array-like with dimensions [samples, channel_height, channel_width, time]
  238. :param y: numpy.ndarray, default = None
  239. Array-like with dimensions [targets].
  241. Return:
  242. -----------
  243. :return: Type['SpatialExhaustiveSearch']
  244. """
  245. self.X_ = X
  246. self.y_ = y
  248. self.iter_ = int(0)
  249. self.result_grid_ = []
  251. self.solution_, self.mask_, self.score_ = self.run()
  253. # Conclude the result grid (Calculate the Size and Height)
  254. self.result_grid_ = pd.concat(self.result_grid_, axis=0, ignore_index=True)
  255. self.result_grid_[["Height", "Width"]] = self.result_grid_["Mask"].apply(
  256. lambda mask: pd.Series(compute_subgrid_dimensions(mask))
  257. )
  258. columns = list(self.result_grid_.columns)
  259. size_index = columns.index("Size")
  260. new_order = (
  261. columns[: size_index + 1]
  262. + ["Height", "Width"]
  263. + columns[size_index + 1 : -2]
  264. )
  265. self.result_grid_ = self.result_grid_[new_order]
  267. return self
  269. def transform(self, X: np.ndarray, y: np.ndarray = None) -> np.ndarray:
  270. """
  271. Transforms the input with the mask obtained from
  272. the solution of Spatial Exhaustive Search.
  274. Parameters:
  275. -----------
  276. :param X: numpy.ndarray
  277. Array-like with dimensions [samples, channel_height, channel_width, time]
  278. :param y: numpy.ndarray, default = None
  279. Array-like with dimensions [targets].
  281. Return:
  282. -----------
  283. :return: numpy.ndarray
  284. Returns a filtered array-like with dimensions
  285. [samples, channel_height, channel_width, time]
  286. """
  287. sklearn_is_fitted(self)
  289. return X[:, self.mask_, :]
  291. def run(self) -> Tuple[np.ndarray, np.ndarray, float]:
  292. """
  293. Executes the Spatial Exhaustive Search.
  295. Parameters:
  296. --------
  297. :return: Tuple[numpy.ndarray, float]
  298. The best channel configuration and its score.
  300. Returns:
  301. --------
  302. :return: Tuple[numpy.ndarray, numpy.ndarray, float, pandas.DataFrame]
  303. A tuple with the solution, mask, the evaluation scores and the optimization history.
  304. """
  305. # Initialize and run the SSHC optimizer
  306. es = ExhaustiveSearch(
  307. func=self.objective_function, channel_grid=self.grid, verbose=self.verbose
  308. )
  309. score, mask = es.run()
  311. solution = mask.reshape(-1).astype(float)
  312. best_state = mask
  313. best_score = score * 100
  314. return solution, best_state, best_score

exhaustive.py at commit d1d856f, under MIT · at the source

Overview

Authors: Elena C Offenberg1, Dirk Keller1,2, Siamak Mehrkanoon2, Julia Berezutskaya1, Mariska J Vansteensel1, Mariana P Branco1,3
ORCID iDs: Elena C Offenberg, Dirk Keller, Siamak Mehrkanoon, Julia Berezutskaya, Mariska J Vansteensel, Mariana P Branco
  1. Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
  2. Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
  3. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
Institutions: Utrecht University (Netherlands); University Medical Center Utrecht (Netherlands); Radboud University Nijmegen (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands)
Journal: Neuroinformatics, volume 24, issue 3, article 52
Dates: received 10 April 2026; accepted 28 July 2026; published online 7 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12021-026-09807-z · PMID 42563025 · PMCID PMC13447543 · OpenAlex W7196935738
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), epilepsy (population), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Machine learning, Statistics, Preprocessing
Keywords: Electrocorticography, Hand movement, Grid size, Classification, Brain-computer interfaces
MeSH: Brain-Computer Interfaces*, Electrocorticography*, Hand*, Adult, Electrodes, Implanted, Epilepsy, Female, Humans, Male, Movement, Young Adult (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HORIZON EUROPE Framework Programme (101070939); Dutch Research Council (NWO) (024.005.022, 17619, 19072)
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Implanted Brain-Computer Interfaces (BCIs) hold significant promise as a replacement for conventional assistive technologies for people with extensive motor impairments. In recent years, significant progress has been made in enhancing BCI performance, bringing clinically viable systems within reach. Some of these developments rely on increasingly large numbers of high spatial-density electrocorticography (ECoG) electrodes, which enable the extraction of spatially detailed information from extensive brain areas, but may also elevate surgical burden, posing a challenge for the clinical adoption of implanted BCIs. To mitigate this risk, we conducted an exhaustive investigation of hand movement classification performance involving 4, 5, and 8 classes in nine individuals with epilepsy, exploring all possible rectangular ECoG subgrids within the 32-, 64-, and 128-channel grids that were implanted in these individuals. Our findings reveal that the surface area of ECoG grids can be substantially reduced by 75–94% compared to the original grids, without a meaningful decline in classification performance, if electrodes are placed over informative areas. Classification performance across datasets was stable for progressively smaller subgrids until a critical threshold of approximately 60mm2 was reached, below which performance declined substantially. We show that smallest subgrids with an area above the threshold achieved equally high classification F1 scores (range 81.64-99.71%) as the full grids with an area > 230mm2 (range 82.85-96.75%). We conclude that ECoG-based BCI can be used to accurately decode up to seven different hand movements from well-located grids with a small number of electrodes, paving the way for smaller and safer BCI implants.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12021-026-09807-z.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

UMCU-RIBS/Subgrid-Optimization

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d1d856fc2fc4d457bebe8315c3dc93c13c4d5e1a, 4 August 2026
Languages: Python (7)
Size: 15 files, 7 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, license file, environment (Dockerfile, project.toml), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: NumPy (6 files), pandas (4 files), scikit-learn (4 files), SciPy (2 files), Matplotlib (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/UMCU-RIBS/Subgrid-Optimization/

Code Availability

The code describing the subgrid search algorithm (SpatialExhaustiveSearch.py), which evaluates all possible subgrid combinations within the ECoG grid, is available in GitHub upon publication. https://github.com/UMCU-RIBS/Subgrid-Optimization/.

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

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  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 11 MeSH terms, 2 funders, 56 references.

Cite

This paper

Offenberg, E. C., Keller, D., Mehrkanoon, S., Berezutskaya, J., Vansteensel, M. J., & Branco, M. P. (2026). Optimal Size of Electrocorticography Grids for Classification of Hand Movements. Neuroinformatics, 24(3), 52. https://doi.org/10.1007/s12021-026-09807-z

BibTeX

@article{offenberg2026optimal,
author = {Offenberg, Elena C and Keller, Dirk and Mehrkanoon, Siamak and Berezutskaya, Julia and Vansteensel, Mariska J and Branco, Mariana P},
title = {{Optimal Size of Electrocorticography Grids for Classification of Hand Movements}},
journal = {Neuroinformatics},
year = {2026},
month = aug,
volume = {24},
number = {3},
pages = {52},
publisher = {Springer Science+Business Media},
issn = {1539-2791},
doi = {10.1007/s12021-026-09807-z},
url = {https://doi.org/10.1007/s12021-026-09807-z},
pmid = {42563025},
pmcid = {PMC13447543}
}

RIS

TY - JOUR
AU - Offenberg, Elena C
AU - Keller, Dirk
AU - Mehrkanoon, Siamak
AU - Berezutskaya, Julia
AU - Vansteensel, Mariska J
AU - Branco, Mariana P
TI - Optimal Size of Electrocorticography Grids for Classification of Hand Movements
T2 - Neuroinformatics
J2 - Neuroinformatics
PY - 2026
DA - 2026/08/07
VL - 24
IS - 3
SP - 52
SN - 1539-2791
PB - Springer Science+Business Media
DO - 10.1007/s12021-026-09807-z
UR - https://doi.org/10.1007/s12021-026-09807-z
LA - en
ER -

CSL-JSON

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