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Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces.

Code ↔ Paper

4 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 4 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Neural decoding and performance evaluation ↔ classification_decoding.py, lines 47–131 · score 0.71 · Cross Entropy Loss, classifier, fold, concatenate, grasp, models
  2. [2] § Methods › Neural decoding and performance evaluation ↔ Evaluation_of_generalization_performance_and_noise_robustness.py, the whole file · a weak match · score 0.69 · noise robustness, white noise, neural signals, CC, SBP, MAND
  3. [3] § Methods › FENet feature extraction and decoding evaluation ↔ FENet_feature_extracting.py, lines 88–200 · score 0.63 · FENet, convolutional, dropout, decoder, electrodes, linear
  4. [4] § Methods › Neural feature extraction ↔ FPGA implementation/python_matlab_verify_sim/feature_ex.py, lines 27–34 · score 0.51 · 300–1000 Hz, raw signal, downsampled, bandpass, SBP, 300 Hz

Paper

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

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The authors' code

Python · 131 lines · 5.4 KB · GPL-3.0 · 1 match

  1. '''
  2. Related Figures:3d
  3. '''
  4. import os
  5. import torch
  6. import scipy.io
  7. import torch.nn as nn
  8. import numpy as np
  9. from neural_network_model import*
  11. def cut(feature_list,mark_list,lists=0):
  12. '''
  13. Based on mask segmentation features (temporal sequence)
  14. '''
  15. if lists==0:
  16. start_mark=0
  17. end_mark=start_mark
  18. res=[]
  19. while True:
  20. start_mark=end_mark
  21. while True:
  22. end_mark=end_mark+1
  23. if end_mark==len(mark_list) or mark_list[start_mark]!=mark_list[end_mark]:
  24. break
  25. res.append(feature_list[:,start_mark:end_mark])
  26. if end_mark==len(mark_list):
  27. break
  28. else:
  29. start_mark=0
  30. end_mark=start_mark
  31. n=len(mark_list)
  32. m=len(feature_list)
  33. res=[[] for i in range(m)]
  34. while True:
  35. start_mark=end_mark
  36. while True:
  37. end_mark=end_mark+1
  38. if end_mark==n or mark_list[start_mark]!=mark_list[end_mark]:
  39. break
  40. for i in range(m):
  41. res[i].append(feature_list[i][:,start_mark:end_mark])
  42. if end_mark==n:
  43. break
  44. return res
  47. if __name__ == "__main__":
  48. hz_list=['H:/grasp/MUA/']
  49. res_list=['MUA']
  50. dataset_list=['101210.mat','140703.mat']
  51. single_start_path='./single_start_net.pth'
  52. single_best_path='./single_best_net.pth'
  53. classify_start_path='./classify_start_net.pth'
  54. classify_best_path='./classify_best_net.pth'
  55. for w in range(len(hz_list)):
  56. net_model=Classify_Net
  57. loss_fn=nn.CrossEntropyLoss
  58. hidden_size=512
  59. output_size=4
  60. optimizer=torch.optim.Adam
  61. optimizer_kw={'lr':0.001}
  62. best_end_interval=10
  64. for q,dataset in enumerate(dataset_list):
  65. a=scipy.io.loadmat(hz_list[w]+dataset)
  66. a['bined_spk']=((a['bined_spk'].T-a['bined_spk'].mean(1))).T
  67. if q==0:
  68. a['bined_spk'][[1,3,28]]=0
  69. target_num=a['fold_num'].shape[1]
  70. trial_num=a['trial_target'].shape[0]
  71. net_args=[a['bined_spk'].shape[0],hidden_size,output_size]
  72. net_kw={}
  74. loss = np.zeros((trial_num,1))
  75. prediction = np.zeros(trial_num)
  77. single_loss_fn=loss_fn().cuda()
  78. single_net=net_model(*net_args,**net_kw).cuda()
  79. single_optimizer=optimizer(single_net.parameters(),**optimizer_kw)
  80. single_trainer=classify_trainer(single_net,single_optimizer,single_loss_fn)
  81. single_trainer.net_save(classify_start_path)
  83. for i_target in range(a['fold_num'].shape[1]):
  84. target_ind = np.where(a['trial_target']-1 == i_target)[0]
  85. bins_remove = np.concatenate([np.where(a['trial_mask']-1 == target_ind[i])[1] for i in range(len(target_ind))],axis=0)
  86. bined_spk_train=np.delete(a['bined_spk'],bins_remove,axis=1)
  87. trial_mask_train=np.delete(a['trial_mask'],bins_remove,axis=1)
  88. bined_spk_train=cut(bined_spk_train,trial_mask_train[0],lists=0)
  89. label_train=np.delete(a['label'].T,target_ind,axis=0)
  90. for i in range(len(bined_spk_train)):
  91. bined_spk_train[i]=torch.tensor(bined_spk_train[i],dtype=torch.float32).cuda()
  92. label_train=torch.tensor(label_train,dtype=torch.int64).cuda()
  94. bined_spk_test=a['bined_spk'][:,bins_remove]
  95. trial_mask_test=a['trial_mask'][:,bins_remove]
  96. bined_spk_test=cut(bined_spk_test,trial_mask_test[0],lists=0)
  97. label_test=a['label'].T[target_ind]
  98. for i in range(len(bined_spk_test)):
  99. bined_spk_test[i]=torch.tensor(bined_spk_test[i],dtype=torch.float32).cuda()
  100. label_test=torch.tensor(label_test,dtype=torch.int64).cuda()
  102. single_trainer.net_load(classify_start_path)
  103. loss_min=1e10
  104. iteration_best=0
  105. iteration=0
  107. while True:
  108. loss=[]
  109. single_trainer.train_one_turn(bined_spk_train, label_train)
  110. for i in range(len(bined_spk_test)):
  111. los,right=single_trainer.test(bined_spk_test[i], label_test[i])
  112. loss.append(los)
  113. loss_mean=np.array(loss).mean()
  114. if loss_mean<loss_min:
  115. loss_min=loss_mean
  116. iteration_best=iteration
  117. single_trainer.net_save(classify_best_path)
  118. print('{0}-{1}-Loss:{2}'.format(i_target,iteration,loss_mean))
  119. if iteration-iteration_best>best_end_interval:
  120. break
  121. iteration=iteration+1
  123. single_trainer.net_load(classify_best_path)
  124. for i in range(len(target_ind)):
  125. loss[target_ind[i],0],prediction[target_ind[i]] = single_trainer.test(bined_spk_test[i], label_test[i],return_res=1)
  128. create_path=dataset.split('/')[-1].split('.')[0]
  129. os.makedirs('./{0}/{1}'.format(res_list[w],create_path))
  130. np.save('./{0}/{1}/loss.npy'.format(res_list[w],create_path),loss)
  131. np.save('./{0}/{1}/prediction.npy'.format(res_list[w],create_path),prediction)

classification_decoding.py at commit 601fe01, under GPL-3.0 · at the source

Overview

Authors: Guangxiang Xu1,2,3, Chenbin Yu2, Gengrong Shao2, Gang Pan1,4, Yueming Wang2,4, Yaoyao Hao1,2,4
ORCID iDs: Guangxiang Xu, Gang Pan, Yueming Wang, Yaoyao Hao
  1. The State Key Lab of Brain-Machine Intelligence, Zhejiang University,Hangzhou, China
  2. Nanhu Brain-computer Interface Institute, Hangzhou, China
  3. Department of Biomedical Engineering, Zhejiang University,Hangzhou, China
  4. College of Computer Science and Technology, Zhejiang University,Hangzhou, China
Institutions: Zhejiang University (China)
Journal: Communications biology, volume 9, issue 1, article 903
Dates: received 1 September 2025; accepted 17 April 2026; published online 29 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10144-9 · PMID 42049850 · PMCID PMC13333888 · OpenAlex W7157935271
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions
Keywords: Brain-machine interface, Neural decoding
MeSH: Action Potentials*, Brain-Computer Interfaces*, Signal Processing, Computer-Assisted*, Algorithms, Animals, Handwriting, Humans (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (62336007, U25D9015, STI2030, 2024C03001); Zhejiang University (SN-ZJU-SIAS-002); Fundamental Research Funds for the Central Universities
Citations: not cited yet (Europe PMC); 46 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

Yaoyao-Hao/MAND

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 601fe015c94d17082376068c21ced5960c64c8e5, 7 April 2026
Languages: Stata (28), Shell (24), Python (17), JavaScript (8), MATLAB (4), C (4)
Size: 342 files, 85 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, documentation
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: NumPy (16 files), SciPy (11 files), PyTorch (4 files), Matplotlib (3 files), scikit-learn (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
87 files
At the source: github.com/Yaoyao-Hao/MAND

Code availability statement

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Read it in the paper: doi.org/10.1038/s42003-026-10144-9.

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  • 85 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Data availability statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s42003-026-10144-9.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 7 MeSH terms, 3 funders, 40 references.

Cite

This paper

Xu, G., Yu, C., Shao, G., Pan, G., Wang, Y., & Hao, Y. (2026). Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces. Communications biology, 9(1), 903. https://doi.org/10.1038/s42003-026-10144-9

BibTeX

@article{xu2026low,
author = {Xu, Guangxiang and Yu, Chenbin and Shao, Gengrong and Pan, Gang and Wang, Yueming and Hao, Yaoyao},
title = {{Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {903},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10144-9},
url = {https://doi.org/10.1038/s42003-026-10144-9},
pmid = {42049850},
pmcid = {PMC13333888}
}

RIS

TY - JOUR
AU - Xu, Guangxiang
AU - Yu, Chenbin
AU - Shao, Gengrong
AU - Pan, Gang
AU - Wang, Yueming
AU - Hao, Yaoyao
TI - Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/29
VL - 9
IS - 1
SP - 903
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10144-9
UR - https://doi.org/10.1038/s42003-026-10144-9
LA - en
ER -

CSL-JSON

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"id": "10.1038/s42003-026-10144-9",
"type": "article-journal",
"title": "Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces",
"container-title": "Communications biology",
"author": [
{
"family": "Xu",
"given": "Guangxiang"
},
{
"family": "Yu",
"given": "Chenbin"
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{
"family": "Shao",
"given": "Gengrong"
},
{
"family": "Pan",
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}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "903",
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"PMID": "42049850",
"PMCID": "PMC13333888",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10144-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
29
]
]
}
}

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