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Reconstruction of Imagined Melody with Relative Pitch Decoding in Electrocorticography.

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
  1. [1] § Materials and Methods › Feature extraction ↔ code/Dec5_Major_Feature_Increment.py, lines 52–58 · score 0.80 · 12–30 Hz, 30–59 Hz, 8–12 Hz, 61–150 Hz, 1–4 Hz, HG
  2. [2] § Materials and Methods › Feature extraction ↔ code/Intersub_0_Major_Feature_selection.py, lines 51–57 · score 0.80 · 12–30 Hz, 30–59 Hz, 8–12 Hz, 61–150 Hz, 1–4 Hz, HG
  3. [3] § Materials and Methods › Classification and decoding models ↔ code/utils_ml_model.py, lines 51–78 · score 0.66 · nearest centroid, random forest, LDA, classification, Model

Paper

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The authors' code

Python · 262 lines · 9.3 KB · no license · 1 match

  1. """
  2. Author: Jii Kwon <[email hidden]>
  3. Seoul National University
  4. Human Brain Function Laboratory
  6. ECoG_Music : Wrapper method
  7. """
  8. #%% Go to root
  9. import os.path
  10. import os
  12. serv = input("Window:0 Linux:1 -")
  14. if serv == '0':
  15. serv_name = 'E:\\'
  16. else:
  17. serv_name = '/mnt/e'
  19. folder_std = os.path.join(serv_name, 'ECoG_Music', '2304_Music_Imagery', '2304_Music_Imagery_Decoding_v1')
  20. folder_scripts = os.path.join(folder_std, '00_scripts')
  22. os.chdir(folder_scripts)
  23. os.getcwd()
  25. #%% import library
  26. from utils_basic_music import np, plt, sio, pd, sns
  27. from utils_basic_music import basic
  28. from utils_etc_function import Utils
  29. from utils_ml_model import ml_models
  30. from tqdm import trange
  31. from collections import Counter
  32. import pickle
  33. import glob
  35. utils = Utils()
  36. ml_models = ml_models()
  37. basic = basic()
  38. #%% Setting Analysis Parameter [adjusting param]
  39. # Decoding param
  40. BASE = 'norm_sd1'
  41. mean_max = 'mean'
  42. kfold = 7
  44. random_state = 1209
  46. aug_val = 1.5 #1.2 # None or number
  47. param_set = None #Boolean : getting the gridsearch result
  48. scaling = 'min-max'
  50. adj_fold = 'v231108_sess1-8[aug_1.5_FI]_min_max_train'
  52. #%% Setting Analysis Basic Parameter [not necessaries of adjusting param]
  53. # Basic Param
  54. Sub_Nums = np.arange(1,11)
  55. bands = ['D', 'T', 'A', 'B', 'G', 'HG']
  56. band_frqs = [(1,4), (4,7), (8,12), (12,30), (30,59), (61,150)]
  58. file_inform = os.path.join(serv_name, 'ECoG_Music', 'Subject_info.mat')
  60. # Set folder name
  61. fold_select = os.path.join(folder_std, '01_select_electrode', 'case2_P1orP2')
  63. fold_class_FW = os.path.join(folder_std, '04_Clf_FW', adj_fold)
  64. fold_acc = basic.set_folder(os.path.join(folder_std, '05_Resul_accuracy'))
  65. fold_major = basic.set_folder(os.path.join(folder_std, '07_Major_Feature_Distribution_v240723','Increment_Value', adj_fold))
  67. if serv == 1: plt.rcParams['font.family'] = 'DejaVu Serif'
  68. else: plt.rcParams['font.family'] = 'Times New Roman'
  70. #%% Analysis
  71. # 1st stage: 각 영역당 몇개의 channel이 있는지
  72. total_elec = pd.DataFrame([])
  73. for sub_idx in trange(len(Sub_Nums)):
  74. sub_fname, SubName, NumCh, Ch_list, Bad_Ch = basic.info_sub(SubNum = Sub_Nums[sub_idx], file_inform=file_inform)
  76. info_elec = pd.read_csv(os.path.join(fold_select, sub_fname+'.csv'), index_col=0)
  77. info_elec = pd.DataFrame(info_elec['Name (aparc)'])
  78. total_elec = pd.concat((total_elec,info_elec), axis = 0, ignore_index=True)
  80. electrode_count = pd.DataFrame(total_elec['Name (aparc)'].value_counts())
  81. electrode_count.to_excel(os.path.join(fold_major, 'total_implanted_elec.xlsx'))
  82. #%%
  83. major_feature = pd.DataFrame([])
  84. for sub_idx in trange(len(Sub_Nums)):
  85. sub_fname, SubName, NumCh, Ch_list, Bad_Ch = basic.info_sub(SubNum = Sub_Nums[sub_idx], file_inform=file_inform)
  87. clf_result = os.path.join(fold_acc, adj_fold+'.xlsx')
  88. clf_result = pd.read_excel(clf_result, sheet_name=sub_fname, index_col=0)
  90. train_acc = max(clf_result['train.1'])
  91. idx_max = clf_result[scaling].idxmax()
  92. best_model_name = clf_result['model.1'].iloc[idx_max]
  94. subfolders = basic.find_names(fold_class_FW, pattern = 'scaled_'+ scaling +'_sess', type_='dir')
  95. clf_max = subfolders[idx_max]
  97. clf_max = basic.load_pkl(os.path.join(fold_class_FW, clf_max,sub_fname+'.pkl'))
  98. test_acc = clf_max['test_acc']
  99. cv_result = clf_max['cv_result']
  100. cv_result = cv_result[best_model_name]
  102. chance_level = 1/6
  104. abs_increment = []
  105. abs_increment.append(cv_result[0][0]-chance_level)
  107. for ii in range(1,len(cv_result)):
  108. iii = cv_result[ii][0]-cv_result[ii-1][0]
  110. abs_increment.append(iii)
  112. rel_increment = []
  113. for num, value in enumerate(abs_increment):
  114. rel_increment.extend([value/(num+1)])
  117. fin_increment = []
  118. for idx in range(clf_result.loc[idx_max, 'indx.1']+1):
  119. fin_increment.append(sum(rel_increment[idx:clf_result.loc[idx_max, 'indx.1']+1]))
  121. sub_major_feat = clf_max['sorted_feature_name']
  122. sub_major_feat = sub_major_feat[:clf_result.loc[idx_max, 'indx.1']+1]
  124. sub_major_feat = pd.DataFrame(sub_major_feat, columns = ['Feature'])
  125. sub_major_feat[['Band', 'ChNum']] = sub_major_feat['Feature'].str.extract(r'(.*)\_Ch(\d+)')
  128. info_elec = pd.read_csv(os.path.join(fold_select, sub_fname+'.csv'), index_col=0)
  129. info_elec = pd.DataFrame(info_elec['Name (aparc)'])
  131. extract_Ch = []
  132. extract_Ch = [info_elec.loc[np.where(Ch_list == int(i))[0][0]].values[0] for i in sub_major_feat['ChNum']]
  133. extract_Ch = pd.DataFrame(extract_Ch, columns=['Area'])
  134. extract_Ch = pd.concat((extract_Ch, sub_major_feat['Band']), axis = 1)
  136. extract_Ch['Merged'] = extract_Ch['Area'] + '-' + extract_Ch['Band']
  138. extract_Ch['increment'] = pd.DataFrame(fin_increment)
  140. major_feature = pd.concat((major_feature,extract_Ch), axis = 0, ignore_index=True)
  142. major_feature.to_excel(os.path.join(fold_major, 'Total_result.xlsx'), index=False)
  144. #%%
  145. # 사람들이
  147. #%%
  148. import matplotlib.colors as mcolors
  151. processed = pd.read_excel(os.path.join(fold_major, 'Final.xlsx'))
  152. processed['Feature'] = processed['Feature'].str.replace('-', ' ')
  154. sum_processed = processed.sort_values(by='Sum', ascending=False)
  155. threshold = sum_processed['Sum'].quantile(0.9)
  157. df_top = sum_processed[sum_processed['Sum'] >= threshold]
  158. norm = mcolors.Normalize(vmin=df_top['Implant Count'].min(), vmax=df_top['Implant Count'].max())
  159. tt = pd.DataFrame([' ', ' ',' ',' '])
  160. # Choose a colormap
  161. colormap = plt.cm.Blues
  162. colors = colormap(norm(df_top['Implant Count']))
  163. colors = np.concatenate((colors, np.full((len(tt), 4), [0.8, 0.8, 0.8, 1])))
  165. all_features = pd.concat([df_top['Feature'], tt[0]], axis=0, ignore_index=True)
  166. all_sums = pd.concat([df_top['Sum']*100, pd.Series([0, 0, 0, 0])], axis=0)
  168. fig, ax = plt.subplots(figsize=(20, 12))
  170. ax.barh(all_features, all_sums, color=colors, edgecolor='black')
  171. ax.set_xlabel('Increment value', fontsize=20)
  172. ax.set_ylabel('Feature', fontsize=20)
  173. #ax.set_title('Top 20% of Sum Values with Different Bar Colors')
  174. ax.invert_yaxis() # To display the highest values at the top
  175. ax.grid(axis='x', linestyle='--', alpha=0.6)
  176. ax.tick_params(axis='both', labelsize=22)
  178. # Add colorbar
  179. sm = plt.cm.ScalarMappable(cmap=colormap, norm=norm)
  180. sm.set_array([])
  181. cbar = fig.colorbar(sm, ax=ax, label='Implant Count')
  182. cbar.ax.tick_params(labelsize=18)
  183. cbar.set_label('# of Electrode Implant', fontsize=22)
  184. plt.tight_layout()
  185. plt.savefig(os.path.join(fold_major, 'Increment_Value.png'), dpi = 300)
  186. plt.show()
  188. #%%
  189. average = processed.sort_values(by='Average', ascending=False)
  191. threshold = average['Average'].quantile(0.8)
  192. df_top = average[average['Average'] >= threshold]
  193. norm = mcolors.Normalize(vmin=df_top['Implant Count'].min(), vmax=df_top['Implant Count'].max())
  195. # Choose a colormap
  196. colormap = plt.cm.Blues
  198. # Create a figure and axes
  199. fig, ax = plt.subplots(figsize=(10, 6))
  201. # Create a horizontal bar plot
  202. ax.barh(df_top['Feature'], df_top['Average'], color=colormap(norm(df_top['Implant Count'])), edgecolor='black')
  203. ax.set_xlabel('Average')
  204. ax.set_ylabel('Feature')
  205. ax.set_title('Top 20% of Sum Values with Different Bar Colors')
  206. ax.invert_yaxis() # To display the highest values at the top
  207. ax.grid(axis='x', linestyle='--', alpha=0.6)
  209. # Add colorbar
  210. sm = plt.cm.ScalarMappable(cmap=colormap, norm=norm)
  211. sm.set_array([])
  212. fig.colorbar(sm, ax=ax, label='Implant Count')
  214. plt.show()
  216. #%% All data
  219. import matplotlib.colors as mcolors
  222. processed = pd.read_excel(os.path.join(fold_major, 'Final.xlsx'))
  223. processed['Feature'] = processed['Feature'].str.replace('-', ' ')
  225. sum_processed = processed.sort_values(by='Sum', ascending=False)
  226. threshold = sum_processed['Sum'].quantile(0.9)
  228. df_top = sum_processed#[sum_processed['Sum']]
  229. norm = mcolors.Normalize(vmin=df_top['Implant Count'].min(), vmax=df_top['Implant Count'].max())
  230. #tt = pd.DataFrame([' ', ' ',' ',' '])
  231. # Choose a colormap
  232. colormap = plt.cm.Blues
  233. colors = colormap(norm(df_top['Implant Count']))
  234. colors = np.concatenate((colors, np.full((len(tt), 4), [0.8, 0.8, 0.8, 1])))
  236. all_features = df_top['Feature']
  237. #all_features = pd.concat([df_top['Feature'], tt[0]], axis=0, ignore_index=True)
  238. all_sums = df_top['Sum']*100
  239. #all_sums = pd.concat([df_top['Sum']*100, pd.Series([0, 0, 0, 0])], axis=0)
  241. for ii in range(int(138/23)):
  242. fig, ax = plt.subplots(figsize=(22, 25))
  244. ax.barh(all_features[ii*23:(1+ii)*23], all_sums[ii*23:(1+ii)*23], color=colors, edgecolor='black')
  245. ax.set_xlabel('Increment value', fontsize=24)
  246. ax.set_ylabel('Feature', fontsize=24)
  247. #ax.set_title('Top 20% of Sum Values with Different Bar Colors')
  248. ax.invert_yaxis() # To display the highest values at the top
  249. ax.grid(axis='x', linestyle='--', alpha=0.6)
  250. ax.tick_params(axis='both', labelsize=24)
  251. ax.tick_params(axis='y', labelsize=28)
  252. plt.xlim([0, 20])
  253. # Add colorbar
  254. sm = plt.cm.ScalarMappable(cmap=colormap, norm=norm)
  255. sm.set_array([])
  256. cbar = fig.colorbar(sm, ax=ax, label='Implant Count')
  257. cbar.ax.tick_params(labelsize=24)
  258. cbar.set_label('# of Electrode Implant', fontsize=24)
  259. plt.tight_layout()
  261. plt.savefig(os.path.join(fold_major, 'Increment_Value'+str(ii)+'.png'), dpi = 300)
  262. plt.show()

Dec5_Major_Feature_Increment.py at commit 9720d6a, no license · at the source

Overview

Authors: Jii Kwon1, Youmin Shin2, June Sic Kim3, Eunju Jeong4, Sung-Phil Kim5, Eun Jung Lee6, Chun Kee Chung7
ORCID iDs: Jii Kwon, Chun Kee Chung
  1. Department of Brain & Cognitive Sciences, Seoul National University, Seoul 08826, Republic of Korea
  2. Seoul AI School, Seoul School of Integrated Sciences and Technologies (aSSIST) University, Seoul 03767, Republic of Korea
  3. Research Institute of Basic Sciences, Seoul National University, Seoul 08826, Republic of Korea
  4. Department of Music Therapy, Graduate School, Ewha Womans University, Seoul 03760, Republic of Korea
  5. Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
  6. Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
  7. Neuroscience Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
Institutions: Seoul National University (South Korea); Seoul School of Integrated Sciences and Technologies (South Korea); Ewha Womans University (South Korea); Ulsan National Institute of Science and Technology (South Korea); Seoul National University Hospital (South Korea)
Journal: eNeuro, volume 13, issue 8, pages ENEURO.0289-25.2026
Dates: received 5 August 2025; accepted 25 June 2026; published online 6 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0289-25.2026 · PMID 42547451 · PMCID PMC13456956 · OpenAlex W7172328079
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Machine learning
Keywords: electrocorticography, music imagery, neural decoding, relative pitch, superior temporary gyrus
MeSH: Electrocorticography*, Imagination*, Music*, Pitch Perception*, Acoustic Stimulation, Adult, Brain Mapping, Female, Humans, Male, Young Adult (* major topic)
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Samsung electronics (SRFC-IT 1902-08); The Ministry of Trade, Industry &amp; Energy (20012355)
Citations: not cited yet (Europe PMC); 45 references in the paper

Abstract

Music imagery involves internally generated auditory experiences, yet decoding imagined melodic content from neural activity remains challenging. Here, we examined whether imagined melodies can be decoded from human electrocorticography (ECoG) recordings using a relative pitch framework. Ten epilepsy patients performed a music imagery task involving familiar melodies presented in multiple tonalities (3 males and 7 females; mean age, 27.3 ± 4.69 years). Neural features from imagery-responsive cortical sites were used to train intrasubject models to decode relative pitch classes at the single-note level. Although single-note decoding accuracy was modest, it reliably exceeded chance. Sequential integration of decoded pitch classes enabled reconstruction of melodic contours that preserved the relative structure of imagined melodies. These results provide a proof-of-concept that imagined melodic structure can be accessed from intracranial neural recordings when framed in terms of relative pitch, underscoring the importance of sequence-level approaches for decoding internally generated auditory content.

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 3 matches between paragraphs and lines of code.

JII-Kwon/ECoG_Music_Imagined_Classification

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9720d6ad23b97737c5da3a83d381986371aa6af6, 31 January 2026
Languages: Python (12), MATLAB (2)
Size: 15 files, 14 scripts
Software Heritage: not archived
Found in: “Code accessibility”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SciPy (6 files), NumPy (5 files), pandas (3 files), EEGLAB (2 files), Matplotlib (2 files), scikit-learn (2 files), imbalanced-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
15 files
At the source: github.com/JII-Kwon/ECoG_Music_Imagined_Classification

Code accessibility

The code described in the paper is freely available online at https://github.com/JII-Kwon/ECoG_Music_Imagined_Classification. The code is available as Extended Data 1 (https://doi.org/10.1523/ENEURO.0289-25.2026.d1).

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 14 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

No dataset and no data link were found in the paper.

Data Availability

The data that support the findings of this study are available from the corresponding authors upon request.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 11 MeSH terms, 2 funders, 44 references.

Cite

This paper

Kwon, J., Shin, Y., Kim, J. S., Jeong, E., Kim, S.-P., Lee, E. J., & Chung, C. K. (2026). Reconstruction of Imagined Melody with Relative Pitch Decoding in Electrocorticography. eNeuro, 13(8), ENEURO.0289-25.2026. https://doi.org/10.1523/eneuro.0289-25.2026

BibTeX

@article{kwon2026reconstruction,
author = {Kwon, Jii and Shin, Youmin and Kim, June Sic and Jeong, Eunju and Kim, Sung-Phil and Lee, Eun Jung and Chung, Chun Kee},
title = {{Reconstruction of Imagined Melody with Relative Pitch Decoding in Electrocorticography}},
journal = {eNeuro},
year = {2026},
month = aug,
volume = {13},
number = {8},
pages = {ENEURO.0289--25.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0289-25.2026},
url = {https://doi.org/10.1523/eneuro.0289-25.2026},
pmid = {42547451},
pmcid = {PMC13456956}
}

RIS

TY - JOUR
AU - Kwon, Jii
AU - Shin, Youmin
AU - Kim, June Sic
AU - Jeong, Eunju
AU - Kim, Sung-Phil
AU - Lee, Eun Jung
AU - Chung, Chun Kee
TI - Reconstruction of Imagined Melody with Relative Pitch Decoding in Electrocorticography
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/08/07
VL - 13
IS - 8
SP - ENEURO.0289
EP - 25.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0289-25.2026
UR - https://doi.org/10.1523/eneuro.0289-25.2026
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Reconstruction of Imagined Melody with Relative Pitch Decoding in Electrocorticography",
"container-title": "eNeuro",
"author": [
{
"family": "Kwon",
"given": "Jii"
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{
"family": "Shin",
"given": "Youmin"
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{
"family": "Kim",
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{
"family": "Jeong",
"given": "Eunju"
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"given": "Sung-Phil"
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{
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"given": "Chun Kee"
}
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"container-title-short": "eNeuro",
"volume": "13",
"issue": "8",
"page": "ENEURO.0289-25.2026",
"DOI": "10.1523/eneuro.0289-25.2026",
"PMID": "42547451",
"PMCID": "PMC13456956",
"ISSN": "2373-2822",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/eneuro.0289-25.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
7
]
]
}
}

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In common: imbalanced-learn, EEGLAB, scikit-learn, 2 other tools, 1 reference

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