OSCR

Ocular Speech Tracking Persists in Blindness, but Its Dynamics and Oculo-Cerebral Connectivity Depend on Visual Status.

Code ↔ Paper

8 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 8 matches
  1. [1] § Materials and Methods › MEG data preprocessing › Source projection of MEG data ↔ eog_pipeline/utils/eogmachine.py, lines 222–354 · score 0.92 · volume source space, template MRI, sphere model, eye source, BEM, LCMV
  2. [2] § Materials and Methods › MEG data preprocessing ↔ eog_pipeline/b00_do_all_eye.py, lines 103–147 · score 0.81 · MNE raw filter, Blackman filter, frontal channel, downsampled, preprocessing, source reconstruction
  3. [3] § Materials and Methods › Speech tracking › Coherence ↔ eog_pipeline/utils/eogmachine.py, lines 596–601 · score 0.70 · 0–0.5 s, constant detrending, baseline, linear, MNE, epochs
  4. [4] § Materials and Methods › MEG data preprocessing › Source projection of MEG data ↔ eog_pipeline/utils/utils_eog.py, lines 22–72 · score 0.66 · source space, fsaverage, BEM, ico, matrix, parcellation
  5. [5] § Materials and Methods › Speech tracking › Multivariate temporal response function ↔ eog_pipeline/b00_do_all_eye.py, lines 235–293 · score 0.61 · selective stopping, fold, l1, partition, boosting, concatenated
  6. [6] § Materials and Methods › Experimental design ↔ eog_pipeline/get_syllables.py, lines 10–18 · score 0.58 · syllable nuclei, syllable rate, Praat
  7. [7] § Materials and Methods › Speech tracking › Multivariate temporal response function ↔ eog_pipeline/b01_reviewer_sensorplot.py, lines 8–86 · score 0.56 · selective stopping, l1, partition, boosting, concatenated, error
  8. [8] § Materials and Methods › MEG data preprocessing › Speech envelope extraction ↔ eog_pipeline/b00_do_all_eye.py, lines 103–147 · score 0.51 · Chimera toolbox, pipelines, downsampled, filtered, Hilbert, MEG

Paper

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The authors' code

Python · 298 lines · 13 KB · no license · 3 matches

  1. #%%
  2. #srun --job-name=runIT --output=test_run.log --ntasks=2 --cpus-per-task=8 --mem-per-cpu=28G --time=00:30:00 pixi run python r04_doeysource.sh & disown
  4. import numpy as np
  5. import pandas as pd
  6. import argparse
  7. import glob
  8. from utils.utils_eog import *
  9. from mne_connectivity import seed_target_indices, spectral_connectivity_epochs
  10. from utils.utils_eog import *
  11. import os
  12. import joblib
  13. from utils.eogmachine import EogMachine
  15. from almkanal import (
  16. AlmKanal,
  17. Maxwell
  18. )
  19. #%%
  21. def main(downsample_f: float = 150,
  22. l_pass: float = 16,
  23. h_pass: float = 0.1,
  24. fmin_coh: float = 0.16,
  25. fmax_coh: float = 8):
  26. #%%
  27. parser = argparse.ArgumentParser(description="subject")
  28. parser.add_argument('subject_id', type=str, help="subject id that is processed")
  29. # parser.add_argument('--is-test', action='store_true', default=False, help="Sanity check?")
  31. args = parser.parse_args()
  32. subject_id = args.subject_id
  33. #%%
  34. downsample_f = 150
  35. l_pass = 16
  36. h_pass = 0.1
  37. fmin_coh = 0.16
  38. fmax_coh = 8
  39. study_name = 'ma_blindaudio'
  40. bids_root = f"/home/kbenz/data/BIDS/{study_name}"
  41. study_path = f'/home/kbenz/data/{study_name}/'
  42. output_path = f'{study_path}source_coh_4envs/'
  43. subjects_dir = '/home/kbenz/pcks/'
  45. er_path = '/home/kbenz/data/raw/STORYTIME/Measurement/empty20151210.fif'
  46. parc='HCPMMP1' # the parcellation to use, e.g., 'aparc' 'aparc.a2009s'
  47. do_preproc = True
  49. #%%
  50. print(f'👀 EOG machine started..🚀🚀🚀🚀')
  51. em = EogMachine(name=subject_id, output_path =output_path)
  52. em.report = []
  53. raw_brain_dir = f'{output_path}raw_source'
  54. #%%
  55. if do_preproc or os.path.exists(f'{raw_brain_dir}/{subject_id}_raw_{parc}.fif') == False:
  56. # load data, that is maxfiltered, and ica rejected heart + train
  57. fiff_f_names_all = glob.glob(f"{bids_root}/{subject_id}/ses-01/meg/**.fif")
  58. n_blocks = len(fiff_f_names_all)
  60. # until fabi implemented it:
  61. #get average head pos
  62. block_pos_l = []
  63. for block in np.arange(0, n_blocks):
  64. raw = mne.io.read_raw_fif(fiff_f_names_all[block], verbose = False) #Raw(subject_id, block_nr=block, preload=False)
  65. block_pos_l.append(raw.info["dev_head_t"]['trans'][:3, 3])
  66. # get most average headposition
  67. blocks_pos = np.array(block_pos_l)
  68. all_distances = np.sqrt(blocks_pos[:,0]**2 + blocks_pos[:,1]**2 + blocks_pos[:,2]**2)
  69. mean_distance = np.median(all_distances)
  70. mean_d_idx = (np.abs(all_distances - mean_distance)).argmin()
  71. destination = block_pos_l[mean_d_idx]
  72. em.report.append(f'Mean head position over all blocks was acquired.')
  74. pick_dict = {
  75. 'meg': True,
  76. 'stim': True,
  77. 'eog': False,
  78. 'ecg': False,
  79. 'eeg': False}
  81. ak = AlmKanal(
  82. pick_params=pick_dict,
  83. steps=[
  84. Maxwell(mw_destination = destination), #everyone stays in their head
  85. ],
  86. )
  87. em.report.append(f'Data was maxfiltered to the mean position.')
  89. raw_all, first_samples = [], []
  91. for block in np.arange(0, n_blocks):
  92. print(f'loading raw for block {block}')
  93. raw = mne.io.read_raw(fiff_f_names_all[block], preload=True, verbose =False)
  94. print(f'running maxfilter..')
  95. raw_tmp, akinfos = ak.run(raw)
  96. # get envelope based on bids
  97. events_metadata = pd.read_csv(fiff_f_names_all[block][:-7] + 'events.tsv', sep="\t")
  98. raw_ws = annotate_append(events_metadata, raw_tmp, bids_root, subject_id)
  99. import matplotlib.pyplot as plt
  100. # plt.plot(raw_ws.get_data()[-1, :])
  101. # plt.show()
  103. # append and concatenate files
  104. raw_all.append(raw_ws)
  106. print(raw_all)
  108. raw = mne.concatenate_raws(raw_all, on_mismatch='raise')
  109. em.report.append(f'When we used the Hilbert envelopes, which are less smooth, the previous results could not be replicated. Therefore, envelopes were extracted with the Chimera toolbox. They were appended to the raw data early to ensure the same preprocessing for stimulus- and neural data.')
  110. h_pass_ica = 0.5
  111. ica2, high_comps, raw_noc = run_ica(raw, h_pass_ica, l_pass, downsample_f, output_path, subject_id)
  113. em.report.append(f'ICA was run on the data with a lowpass filter of 1 Hz')
  114. em.report.append(f'Data was downsampled to {downsample_f} Hz.')
  115. em.report.append(f'ECG channel was automatically rejected using find_bads_ecg in MNE with a threshold of 0.5')
  116. em.report.append(f'The identified channels were applied to the copied raw data, which were filtered between {h_pass_ica} and {l_pass} Hz using a Blackman filter and the default MNE.raw.filter settings.')
  118. em.add_meg_data_noth(raw_noc)
  119. em.add_ica2(ica2)
  120. em.ica_eog_nr = high_comps
  122. em.report.append(f'We tried to find consistent ICA eye components using a semiautomatic approach, including correlation of frontal channels, cross-correlation, etc. However, as the participants were blindfolded and blind, we were not successful in consistently identifying them across groups. To ensure consistency over groups, no ICA-eye components were rejected. See topographies in the supplementals. In subsequent analysis, we also suggest that the ICA-eye component is highly related to the auditory cortices, as they are also symmetrical. Since we also ran source reconstruction for the eyes, this is supposed to disentangle the sources.')
  124. # get conditions of experiment
  125. data_list = [data for data in em.data_noth.crop_by_annotations() if len(data)>2]
  126. em.condis = np.unique([str(data.annotations).split(' ')[-2] for data in data_list])
  127. del data_list
  129. #set your channel however you want, here manually because automatic did not work for the blind(folded) blinks
  130. # not sure if it works, bc now we are redoing the maxfiltering..
  131. info_frame = pd.read_csv('/home/kbenz/code/kb_blindaudio/eog_pipeline/participants.tsv', sep ='\t')
  132. em.ica_eog_nr = int(info_frame.loc[info_frame['participant_id'] == subject_id, 'ica'])
  135. # get faked eog channel from ica
  136. em.get_fake_eog_and_filter()
  139. # # properties are only for emtyroom preproc, in line with the other ones
  140. # filters = em.get_source_eyes(subjects_dir, subject_id, em.do_epochs(em.data_noth), l_freq=0.1, h_freq=16, sfreq=150, ep_len = 6, er_path = er_path)
  142. # # # get timeseries of the source fake eog channel
  143. # # # and also of areas of interest
  144. # # # here I use the superiortemporal and pericalcarine areas, but you can choose any other
  145. # subject_name = f'{subject_id}_from_template'
  146. # # Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
  147. # labels_parc = mne.read_labels_from_annot(subject_name, parc=parc, subjects_dir=f'{subjects_dir}/freesurfer/')
  149. # # use now all regions
  150. # my_labels = np.unique([labelsX.name[:-3] for labelsX in labels_parc])
  151. # areas = tuple(my_labels)
  153. # time_src = em.get_timeseries_src(em.data_noth.copy(), filters, subjects_dir, areas= areas, picks_from_old = ['speech_envelope', 'spe_env_chi_ma', 'speenv_fake_chi', 'spe_env_fake_hil'], parc=parc)
  154. # if not os.path.exists(raw_brain_dir):
  155. # # Create a new directory because it does not exist
  156. # os.makedirs(raw_brain_dir)
  158. # em.data_noth.save(f'{raw_brain_dir}/{subject_id}_raw_sensors.fif', overwrite=True)
  159. # time_src.save(f'{raw_brain_dir}/{subject_id}_raw_{parc}.fif', overwrite=True)
  161. # elif os.path.exists(f'{raw_brain_dir}/{subject_id}_raw_{parc}.fif'):
  162. # time_src = mne.io.read_raw_fif(f'{raw_brain_dir}/{subject_id}_raw_{parc}.fif')
  163. # em.add_meg_data_noth(mne.io.read_raw_fif(f'{raw_brain_dir}/{subject_id}_raw_sensors.fif'))
  164. # else:
  165. # raise Exception(f"🚨participant {subject_id} was not preprocessed before, do this first 🚨")
  166. # print(em.report)
  167. # #%%
  168. # em.condis = ['nat', 'voc1', 'voc8']
  169. # #%% get sensor coherence
  170. # from copy import deepcopy
  171. # data_coh_raw = deepcopy(em.data_noth).pick(picks =['mag','misc'])
  172. # data_dict_sensors = em.get_raw_bycond(data_coh_raw)
  173. # del(data_coh_raw)
  174. # # coherence_sensors = em.get_coherence(data_dict = data_dict_sensors, fmin_coh = fmin_coh, fmax_coh = fmax_coh, starters_a =('spe'), starters_b = ('MEG'), folder_name = 'coh_mags') #lfreq, hfreq
  175. # # joblib.dump(coherence_sensors, f'{output_path}coh_mags/{subject_id}_coh_meg.pckl')
  178. # # #%%
  179. # data_dict_orig = em.get_raw_bycond(time_src)
  180. # #%% get imaginary part of coherence for eye brain connectivity
  182. # coh_type = 'imcoh'
  183. # coh_brain_dir = f'{output_path}{coh_type}_{parc}'
  184. # output_file = f'{coh_brain_dir}/{subject_id}_{coh_type}_meg.pckl'
  186. # if not os.path.exists(output_file):
  187. # # Create directory if it doesn't exist
  188. # if not os.path.exists(coh_brain_dir):
  189. # os.makedirs(coh_brain_dir)
  191. # # Only compute and save if file doesn't exist
  192. # coherence_brain = em.get_coherence(
  193. # data_dict=data_dict_orig,
  194. # fmin_coh=fmin_coh,
  195. # fmax_coh=fmax_coh,
  196. # starters_a=('eye_1'),
  197. # starters_b=('rh', 'lh'),
  198. # folder_name='coh_meg',
  199. # coh_type=coh_type
  200. # )
  201. # joblib.dump(coherence_brain, output_file)
  203. # # print('coherence in HCPMMP1 is saved here')
  204. # # # print('coherence is saved')
  205. # # coherence_eyes = em.get_coherence(data_dict = data_dict_orig, fmin_coh = fmin_coh, fmax_coh = fmax_coh, starters_a =('spe'), starters_b = ('ICA', 'eye'), folder_name = 'coh_meg') #lfreq, hfreq
  207. # # coh_eye_dir = f'{output_path}eog_pca_coh'
  208. # # if not os.path.exists(coh_eye_dir):
  209. # # # Create a new directory because it does not exist
  210. # # os.makedirs(coh_eye_dir)
  211. # # joblib.dump(coherence_eyes, f'{coh_eye_dir}/{subject_id}_coh_meg.pckl')
  213. # #%% get sensor boosting for concatenated blocks
  214. # boosting_kwargs= {'tstart':-0.4,
  215. # 'tstop' : 0.8,
  216. # 'error' :'l1',
  217. # 'basis': 0.050,
  218. # 'partitions':4,
  219. # 'test':1,
  220. # 'selective_stopping':True}
  221. # # sns_data_lbl = em.data_noth.copy().pick(picks = ['mag']).ch_names
  222. # # env_name = 'speenv_fake_chi' # 'spe_env_fake_hil' # 'spe_env_chi_ma' # 'spe_env_fake_hil'
  223. # # nat_trf_long = dict.fromkeys(em.condis)
  224. # # for cond in em.condis:
  225. # # one_long = mne.concatenate_raws(data_dict_sensors[cond])
  226. # # nat_trf_long[cond] = EogMachine.get_trf(one_long, boosting_kwargs, sns_data_lbl, env_name)
  227. # # del one_long
  228. # # trf_all_dir = f'{output_path}trfs_mag{env_name}'
  230. # # if not os.path.exists(trf_all_dir):
  231. # # # Create a new directory because it does not exist
  232. # # os.makedirs(trf_all_dir)
  233. # # joblib.dump(nat_trf_long, f'{trf_all_dir}/{subject_id}_trfs_long.pckl')
  235. # #%%
  236. # #%% Test for boosting with many partitions
  237. # # for sensors
  238. # partitions = 50
  239. # boosting_kwargs= {'tstart':-0.4,
  240. # 'tstop' : 0.8,
  241. # 'error' :'l1',
  242. # 'basis': 0.050,
  243. # 'partitions': partitions,
  244. # 'test':1,
  245. # 'selective_stopping':True}
  246. # sns_data_lbl = em.data_noth.copy().pick(picks = ['mag']).ch_names
  247. # env_name = 'spe_env_chi_ma' # 'spe_env_fake_hil' # 'spe_env_chi_ma' # 'spe_env_fake_hil'
  248. # nat_trf_long = dict.fromkeys(em.condis)
  249. # data_coh_raw = deepcopy(em.data_noth).pick(picks =['mag','misc'])
  250. # data_dict_sensors = em.get_raw_bycond(data_coh_raw)
  253. # trf_all_dir = f'{output_path}trfs_mag_{partitions}parts'
  254. # output_file = f'{trf_all_dir}/{subject_id}_trfs_long.pckl'
  256. # if not os.path.exists(output_file):
  258. # print('STARTING CRAZY K-FOLD NOW')
  259. # for cond in em.condis:
  260. # one_long = mne.concatenate_raws(data_dict_sensors[cond])
  261. # nat_trf_long[cond] = EogMachine.get_trf(one_long, boosting_kwargs, sns_data_lbl, env_name)
  262. # del one_long
  264. # if not os.path.exists(trf_all_dir):
  265. # os.makedirs(trf_all_dir)
  267. # joblib.dump(nat_trf_long, output_file)
  269. # # %%for source and eye data
  270. # subject_name = f'{subject_id}_from_template'
  271. # # Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
  272. # labels_parc = mne.read_labels_from_annot(subject_name, parc=parc, subjects_dir=f'{subjects_dir}/freesurfer/')
  274. # src_data_lbl = time_src.copy().pick(picks = 'eog').ch_names # [label.name for label in labels_parc] +
  275. # trf_long_src = dict.fromkeys(em.condis)
  276. # data_dict_orig2 = em.get_raw_bycond(time_src)
  278. # trf_src_dir = f'{output_path}trfs_eye{env_name}{partitions}parts'
  279. # output_file = f'{trf_src_dir}/{subject_id}_trfs_long.pckl'
  281. # if not os.path.exists(output_file):
  282. # print('second CRAZY K-FOLD started')
  283. # for cond in em.condis:
  284. # one_long = mne.concatenate_raws(data_dict_orig2[cond].copy())
  285. # trf_long_src[cond] = EogMachine.get_trf(one_long, boosting_kwargs, src_data_lbl, env_name)
  286. # del one_long
  288. # if not os.path.exists(trf_src_dir):
  289. # os.makedirs(trf_src_dir)
  291. # joblib.dump(trf_long_src, output_file)
  293. print('finished, hope all good!')
  295. #%%
  296. if __name__ == "__main__":
  297. main()

b00_do_all_eye.py at commit 546ae01, no license · at the source

Overview

Authors: Kaja Rosa Benz1,2, Larissa Reitinger1, Fabian Schmidt1, Davide Bottari3, Anne Hauswald1,4, Olivier Collignon4,5, Nathan Weisz1,4,6
ORCID iDs: Kaja Rosa Benz, Larissa Reitinger, Davide Bottari, Anne Hauswald, Olivier Collignon, Nathan Weisz
  1. Centre for Cognitive Neuroscience, Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg 5020, Austria
  2. Basque Center on Cognition, Brain and Language, Donostia - San Sebastián 20009, Spain
  3. IMT School for Advanced Studies Lucca, Lucca 55100, Italy
  4. Center for Mind/Brain Sciences, University of Trento, Rovereto 38068, Italy
  5. Institute of Neuroscience (IoNS), UCLouvain, Louvain-la-Neuve B-1348, Belgium
  6. Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University Salzburg, Salzburg 5020, Austria
Institutions: University of Salzburg (Austria); IMT School for Advanced Studies Lucca (Italy); University of Trento (Italy); UCLouvain (Belgium)
Journal: eNeuro, volume 13, issue 7, pages ENEURO.0041-26.2026
Dates: received 4 February 2026; accepted 26 May 2026; published online 7 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0041-26.2026 · PMID 42379881 · PMCID PMC13349464 · OpenAlex W4415405851
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.21.682842
Status: code verified
Categories: MEG (modality), human (organism), other condition (population), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Physiology & signal measures, fMRI & imaging
Keywords: blind, eye movements, speech tracking, vocoding
MeSH: Blindness*, Eye Movements*, Speech Perception*, Adult, Aged, Female, Humans, Magnetoencephalography, Male, Middle Aged, Neural Pathways, Speech Intelligibility (* major topic)
Topic: Tactile and Sensory Interactions (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (337573)
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

While eye movements have been shown to track the speech envelope, it is unknown whether this reflects a hard-wired mechanism or one shaped by (lifetime) audiovisual experience. Further, questions remain about whether ocular tracking is modulated by speech intelligibility and which brain regions drive these synchronized eye movements. Here, we investigate ocular speech tracking in 47 (20 male), blindfolded early blind, late blind, and sighted individuals using magnetoencephalography and source-reconstructed oculomotor signals while participants listened to narrative speech of varying intelligibility. We find that oculomotor activity tracks acoustic speech features; however, while neural speech tracking is modulated by intelligibility, ocular tracking patterns remain ambiguous. Interestingly, we find effects reflected in two frequency-specific components: a low-frequency (∼1 Hz) effect present across all groups, indicating that visual experience is not required, and a high-frequency (∼6 Hz) effect reduced in early and late blind individuals. Moreover, this finding is not driven by cerebro-ocular connectivity, as late blind individuals exhibit stronger connectivity between the eyes and the left temporal cortices without a corresponding increase in ocular tracking. In conclusion, ocular speech tracking seems to respond selectively to acoustic features of speech, and does not require visual experience to develop. It may thus represent a hard-wired oculomotor mechanism within the oculo-cerebral network involved in speech processing.

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 8 matches between paragraphs and lines of code.

KajaRosa/kb_blindaudio

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 546ae0131f9fdd866d6e6656c4db1313a82f7c60, 6 April 2026
Languages: Python (12), Shell (2)
Size: 26 files, 14 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: environment (eog_pipeline/pixi.lock, eog_pipeline/pixi.toml)
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (9 files), Matplotlib (8 files), MNE-Python (8 files), pandas (8 files), Pingouin (5 files), seaborn (5 files), SciPy (4 files), Plotly (3 files), MNE-Connectivity (2 files), scikit-learn (2 files), NiBabel (1 file), Pillow (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
14 files
At the source: gitlab.com/KajaRosa/kb_blindaudio

Code Availability

GitLab: gitlab.com/KajaRosa/kb_blindaudio.

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

Tracing map

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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;
  • 8 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 authors acknowledge the Austrian NeuroCloud (https://anc.plus.ac.at/), hosted by the University of Salzburg and funded by the Federal Ministry of Education, Science and Research (BMBWF), for providing a FAIR-compliant research data repository. Find the data on request on the ANC: bids-datasets.data-pages.anc.plus.ac.at/auditory/ma_blindaudio/.

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

Versions

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Version 2, 28 September 2026

  • Authors: added Kaja Rosa Benz (0000-0002-8503-1273); Larissa Reitinger (0000-0002-6650-9948); Anne Hauswald (0000-0002-3754-0807); removed Kaja Rosa Benz; Larissa Reitinger; Anne Hauswald

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 4 keywords, 12 MeSH terms, 1 funder, 53 references.

Cite

This paper

Rosa Benz, K., Reitinger, L., Schmidt, F., Bottari, D., Hauswald, A., Collignon, O., & Weisz, N. (2026). Ocular Speech Tracking Persists in Blindness, but Its Dynamics and Oculo-Cerebral Connectivity Depend on Visual Status. eNeuro, 13(7), ENEURO.0041-26.2026. https://doi.org/10.1523/eneuro.0041-26.2026

BibTeX

@article{rosabenz2026ocular,
author = {Rosa Benz, Kaja and Reitinger, Larissa and Schmidt, Fabian and Bottari, Davide and Hauswald, Anne and Collignon, Olivier and Weisz, Nathan},
title = {{Ocular Speech Tracking Persists in Blindness, but Its Dynamics and Oculo-Cerebral Connectivity Depend on Visual Status}},
journal = {eNeuro},
year = {2026},
month = jul,
volume = {13},
number = {7},
pages = {ENEURO.0041--26.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0041-26.2026},
url = {https://doi.org/10.1523/eneuro.0041-26.2026},
pmid = {42379881},
pmcid = {PMC13349464}
}

RIS

TY - JOUR
AU - Rosa Benz, Kaja
AU - Reitinger, Larissa
AU - Schmidt, Fabian
AU - Bottari, Davide
AU - Hauswald, Anne
AU - Collignon, Olivier
AU - Weisz, Nathan
TI - Ocular Speech Tracking Persists in Blindness, but Its Dynamics and Oculo-Cerebral Connectivity Depend on Visual Status
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/07/09
VL - 13
IS - 7
SP - ENEURO.0041
EP - 26.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0041-26.2026
UR - https://doi.org/10.1523/eneuro.0041-26.2026
LA - en
ER -

CSL-JSON

{
"id": "10.1523/eneuro.0041-26.2026",
"type": "article-journal",
"title": "Ocular Speech Tracking Persists in Blindness, but Its Dynamics and Oculo-Cerebral Connectivity Depend on Visual Status",
"container-title": "eNeuro",
"author": [
{
"family": "Rosa Benz",
"given": "Kaja"
},
{
"family": "Reitinger",
"given": "Larissa"
},
{
"family": "Schmidt",
"given": "Fabian"
},
{
"family": "Bottari",
"given": "Davide"
},
{
"family": "Hauswald",
"given": "Anne"
},
{
"family": "Collignon",
"given": "Olivier"
},
{
"family": "Weisz",
"given": "Nathan"
}
],
"container-title-short": "eNeuro",
"volume": "13",
"issue": "7",
"page": "ENEURO.0041-26.2026",
"DOI": "10.1523/eneuro.0041-26.2026",
"PMID": "42379881",
"PMCID": "PMC13349464",
"ISSN": "2373-2822",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/eneuro.0041-26.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
9
]
]
}
}

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