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Studying dynamic sound source localization in a virtual maze with EEG and psychophysics.

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] § Results › Turning decisions ↔ VirtualMaze_BEHAVanalysis.m, lines 1–57 · score 0.71 · alarm sound position, segments indicate, walking direction, incorrect turning decisions, regression, fell
  2. [2] § Results › MMN ↔ VirtualMaze_ERPanalysis.m, lines 85–223 · score 0.67 · Bar graph, FDR corrected, spatial deviation, rectangle, SEM, Grand
  3. [3] § Materials and methods › Data analysis › Statistical analysis ↔ VirtualMaze_ERPanalysis.m, lines 38–81 · score 0.63 · 104–154 ms, 104 ms, M1, M2, window, topographic

Paper

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The authors' code

MATLAB · 303 lines · 13 KB · no license · 2 matches

  1. %% VirtualMaze_ERPanalysis
  2. %
  3. % Script summarizes ERP analysis in:
  4. % Strauss, H., Grimm, S., Feder, S., Miksch, J., Bendixen, A. (accepted).
  5. % Studying dynamic sound source localization in a virtual maze with EEG and
  6. % psychophysics. Frontiers in Neuroscience
  7. %
  8. % Necessary set/fdt-files are located in the folder ERPdata/
  10. % Figure generation:
  11. % * Figure 3(A-C) Grand-average ERPs for standards, deviants and their difference
  12. % waveforms and MMN topographies for small, medium, and large spatial
  13. % deviations
  14. % * Figure 3(D) Comparison of the grand-average difference waveforms between the
  15. % three angle size conditions
  16. % * Figure 3(E) Bar graph depicting mean amplitudes in the MMN time window for
  17. % deviants and standards with small, medium, and large angles
  18. %
  19. % Statistical Analysis:
  20. % * repeated-measures ANOVA with the factors Stimulus Type (Deviant,
  21. % Standard) and Angle Size (Small, Medium, Large) on mean
  22. % amplitudes at electrode FCz in the MMN time window
  23. % * follow-up t-tests to compare mean amplitudes between deviants and
  24. % standards in the three angle size conditions
  25. % * follow-up t-tests to compare MMN mean amplitudes between small and
  26. % medium, small and large, and medium and large angle sizes
  27. % * repeated-measures ANOVA with the factors Stimulus Type (Deviant,
  28. % Standard) and Angle Size (Small, Medium, Large) on mean
  29. % amplitudes at the mastoid electrodes (M1, M2) in the MMN time window
  30. %
  31. %
  32. % Requirements:
  33. % - EEGLAB toolbox (https://eeglab.org)[Version used: eeglab v2025.0.0]
  34. % - Statistics and Machine Learning Toolbox (for functions ranova.m, fitrm.m)
  38. %% define and add eeglab path
  39. % eeglabPath = 'YOUR EEGLAB PATH';
  40. % addpath(eeglabPath)
  41. eeglab
  43. erppath = './ERPdata/';
  45. conditions = {'dev_small', 'dev_medium', 'dev_large', 'sta_small', 'sta_medium', 'sta_large'};
  46. angles = {'small', 'medium', 'large'};
  47. channel = 'FCz';
  48. channel_M = {'M1', 'M2'};
  49. twin = [104 154];
  50. cols = [212 0 0; 0 0 255; 0, 0, 0; 117 112 179; 217 95 2; 27 158 119]/255;
  52. % load ERP datasets from all conditions (requirement: EEGLab) and collect
  53. % them in the array allERPs (shape: conditions x electrodes x sampling
  54. % points x participants)
  55. for iCond = 1:size(conditions,2)
  56. EEG = pop_loadset('filename', ['ERPs_' conditions{iCond} '.set'], 'filepath', erppath);
  57. allERPs(iCond,:,:,:) = EEG.data;
  58. end
  60. % determine channel number and sampling points of interest
  61. channelNr = find(strcmp(channel,{EEG.chanlocs.labels}));
  62. for c = 1:size(channel_M,2)
  63. channelNr_M(c) = find(strcmp(channel_M{c},{EEG.chanlocs.labels}));
  64. end
  65. twin_pts = (twin-EEG.times(1))*EEG.srate/1000+1;
  67. % extract grand-average traces
  68. devTrace = squeeze(mean(allERPs(1:3,channelNr,:,:),4));
  69. staTrace = squeeze(mean(allERPs(4:6,channelNr,:,:),4));
  70. diffTrace = devTrace - staTrace;
  72. % extract topographic maps in MMN time window
  73. devMap = squeeze(mean(mean(allERPs(1:3,:,twin_pts(1):twin_pts(2),:),3),4));
  74. staMap = squeeze(mean(mean(allERPs(4:6,:,twin_pts(1):twin_pts(2),:),3),4));
  75. diffMap = devMap - staMap;
  77. % extract MMN mean amplitudes at relevant electrodes
  78. MMNamp = squeeze(mean(allERPs(:,channelNr,twin_pts(1):twin_pts(2),:),3))';
  79. MMNamp_M = squeeze(mean(mean(allERPs(:,channelNr_M,twin_pts(1):twin_pts(2),:),2),3))';
  80. meanMMNamp = reshape( mean(MMNamp,1), [3,2]); % mean over participants
  81. semMMNamp = reshape( std(MMNamp,[],1)/sqrt(size(MMNamp,1)), [3 2]); % standard error of mean
  85. %% Figure plots
  86. % Figure 3A-C: Grand-average ERPs for standards, deviants and their difference
  87. % waveform and MMN topographies for small, medium, and large spatial
  88. % deviations
  89. f1 = figure(Name="Figure 3A-C",NumberTitle="off");
  90. for iAngle = 1:3
  92. % calculate FDR-corrected running t test
  93. start_t = 0;
  94. start_t_pts = (start_t-EEG.times(1))*EEG.srate/1000+1;
  95. devTrace_rtt = squeeze(allERPs(iAngle,channelNr,start_t_pts:end,:));
  96. staTrace_rtt = squeeze(allERPs(3+iAngle,channelNr,start_t_pts:end,:));
  97. n_rtt = length(devTrace_rtt);
  98. all_p = nan(1,n_rtt); all_t = nan(1,n_rtt);
  99. for t = 1:n_rtt
  100. [~,all_p(t),~,stats] = ttest(devTrace_rtt(t,:), staTrace_rtt(t,:), 'alpha', 0.05, 'tail', 'both');
  101. all_t(t) = stats.tstat;
  102. end
  103. [all_p_sorted, order] = sort(all_p);
  104. [~,orderI] = sort(order);
  105. q = [1:n_rtt]/n_rtt*0.05;
  106. FDRthresh_sorted = all_p_sorted < q;
  107. FDRthresh = FDRthresh_sorted(orderI);
  109. % plot ERP traces including running t test results
  110. subplot(2,3,iAngle)
  111. axis([-100 500, -4.3 4.3])
  112. hold on
  113. y = ylim; plot([0 0],[y(1) y(2)], 'k', 'LineWidth', 0.5, 'HandleVisibility','off');
  114. rectangle('Position',[twin(1) y(1) twin(2)-twin(1) y(2)-y(1)],'FaceColor',[0.8 0.8 0.8 0.2],'EdgeColor',[0.8 0.8 0.8 0.2]);
  115. x = xlim; plot([x(1) x(2)],[0 0], 'k', 'LineWidth', 0.5, 'HandleVisibility','off');
  117. indTraces = squeeze(mean(allERPs(1:6,channelNr,:,:),2));
  118. for l = 1:3
  119. if l == 1
  120. dataTrace = indTraces(iAngle,:,:);
  121. elseif l == 2
  122. dataTrace = indTraces(iAngle+3,:,:);
  123. elseif l == 3
  124. dataTrace = indTraces(iAngle,:,:) - indTraces(iAngle+3,:,:);
  125. end
  127. sem = squeeze(std(dataTrace,[],3))/sqrt(size(dataTrace,3));
  128. sem = sem(:);
  129. a = ones(size(EEG.times))';
  130. regions(1) = patch('XData', [EEG.times'; EEG.times(end:-1:1)'], ...
  131. 'YData', [squeeze(mean(dataTrace,3))' + sem; squeeze(mean(dataTrace(:,end:-1:1,:),3))'], ...
  132. 'FaceColor', cols(l,:), ...
  133. 'FaceVertexAlphaData', [0.1*a; 0.3*a], ...
  134. 'FaceAlpha', 0.2, 'EdgeColor', 'none',...
  135. 'HandleVisibility','off');
  136. regions(2) = patch('XData', [EEG.times'; EEG.times(end:-1:1)'], ...
  137. 'YData', [squeeze(mean(dataTrace,3))' - sem; squeeze(mean(dataTrace(:,end:-1:1,:),3))'], ...
  138. 'FaceColor', cols(l,:), ...
  139. 'FaceVertexAlphaData', [0.1*a; 0.3*a], ...
  140. 'FaceAlpha', 0.2, 'EdgeColor', 'none',...
  141. 'HandleVisibility','off');
  142. end
  143. plot(EEG.times,devTrace(iAngle,:),'Color',cols(1,:),'LineWidth',1.2)
  144. plot(EEG.times,staTrace(iAngle,:),'Color',cols(2,:),'LineWidth',1.2)
  145. plot(EEG.times,diffTrace(iAngle,:),'k','LineWidth',1.7)
  148. % plot FDR-corrected running t tests
  149. rectangle('Position',[start_t 3.8 EEG.times(end)-start_t 0.3],'FaceColor',[0.8 0.8 0.8],'EdgeColor',[0.8 0.8 0.8]);
  150. for t = 1:n_rtt
  151. if FDRthresh(t)
  152. if all_t(t) > 0 %red for positive T values
  153. rectangle('Position',[ EEG.times(start_t_pts + t - 1)-1 3.8 2 0.3],'FaceColor',[1 0 0],'EdgeColor',[1 0 0]);
  154. else %blue for negative T values
  155. rectangle('Position',[ EEG.times(start_t_pts + t - 1)-1 3.8 2 0.3],'FaceColor',[0 0 1],'EdgeColor',[0 0 1]);
  156. end
  157. end
  158. end
  159. xlabel('Time (ms)')
  160. ylabel('Voltage (µV)')
  161. set(gca, 'YDir', 'Reverse', 'TickDir', 'out');
  163. % plot topographies
  164. subplot(2,3,3+iAngle)
  165. topoplot(diffMap(iAngle,:), EEG.chanlocs, 'maplimits',[-3 3]);
  166. end
  167. set(f1, 'Color','w','Position',[0 370 900 400])
  168. set(findall(gcf,'-property','FontSize'),'FontSize',12)
  169. % exportgraphics(gcf,"Figure3AC.eps",ContentType="vector")
  172. % Figure 3D: Comparison of grand-average difference waveforms for small,
  173. % medium, and large spatial deviations
  174. f2 = figure(Name="Figure 3D",NumberTitle="off");
  175. set(f2, 'Color','w','Position',[0 0 450 350])
  176. axis([-100 500, -4 4])
  177. hold on
  178. y = ylim; plot([0 0],[y(1) y(2)], 'k', 'LineWidth', 0.5, 'HandleVisibility','off');
  179. rectangle('Position',[twin(1) y(1) twin(2)-twin(1) y(2)-y(1)],'FaceColor',[0.8 0.8 0.8 0.2],'EdgeColor',[0.8 0.8 0.8 0.2]);
  180. x = xlim; plot([x(1) x(2)],[0 0], 'k', 'LineWidth', 0.5, 'HandleVisibility','off');
  181. indDiffTraces = squeeze(mean(allERPs(1:3,channelNr,:,:),2)) - squeeze(mean(allERPs(4:6,channelNr,:,:),2));
  183. for l = 1:3
  184. sem = squeeze(std(indDiffTraces(l,:,:),[],3))/sqrt(size(indDiffTraces,3));
  185. sem = sem(:);
  186. a = ones(size(EEG.times))';
  187. regions(1) = patch('XData', [EEG.times'; EEG.times(end:-1:1)'], ...
  188. 'YData', [diffTrace(l,:)' + sem; diffTrace(l,end:-1:1)'], ...
  189. 'FaceColor', cols(l+3,:), ...
  190. 'FaceVertexAlphaData', [0.1*a; 0.3*a], ...
  191. 'FaceAlpha', 0.2, 'EdgeColor', 'none',...
  192. 'HandleVisibility','off');
  193. regions(2) = patch('XData', [EEG.times'; EEG.times(end:-1:1)'], ...
  194. 'YData', [diffTrace(l,:)' - sem; diffTrace(l,end:-1:1)'], ...
  195. 'FaceColor', cols(l+3,:), ...
  196. 'FaceVertexAlphaData', [0.1*a; 0.3*a], ...
  197. 'FaceAlpha', 0.2, 'EdgeColor', 'none',...
  198. 'HandleVisibility','off');
  199. plot(EEG.times,diffTrace(l,:),'Color',cols(l+3,:),'LineWidth',2)
  201. end
  202. xlabel('Time (ms)')
  203. ylabel('Voltage (µV)')
  204. legend('Small', 'Medium', 'Large')
  205. legend boxoff
  206. set(gca, 'YDir', 'Reverse', 'TickDir', 'out');
  207. set(findall(gcf,'-property','FontSize'),'FontSize',14)
  208. % exportgraphics(gcf,"Figure3D.eps",ContentType="vector")
  210. % Figure 3E: Bar graph of mean amplitudes in the MMN time window for deviants
  211. % and standards with small, medium, and large angles
  212. f3 = figure(Name="Figure 3E",NumberTitle="off");
  213. set(f3, 'Color','w','Position',[450 0 450 350])
  214. hold on
  215. b = bar(meanMMNamp);
  216. b(1).FaceColor = cols(1,:); b(1).EdgeColor = cols(1,:);
  217. b(2).FaceColor = cols(2,:); b(2).EdgeColor = cols(2,:);
  218. ylabel('Voltage (µV)')
  219. set(gca, 'YDir', 'Reverse', 'TickDir', 'out', 'XTick', 1:3, 'XTickLabel', {'Small', 'Medium', 'Large'});
  220. errorbar([1:3]-0.14, meanMMNamp(:,1)', semMMNamp(:,1)','k.')
  221. errorbar([1:3]+0.14, meanMMNamp(:,2)', semMMNamp(:,2)','k.')
  222. set(findall(gcf,'-property','FontSize'),'FontSize',14)
  223. % exportgraphics(gcf,"Figure3E.eps",ContentType="vector")
  225. %% Statistical tests
  226. % Repeated-measures ANOVA on frontocentral MMN mean amplitudes
  227. disp('Effects of Angle Size and Stimulus Type on MMN at electrode FCz')
  228. angle = ["small"; "medium"; "large"; "small"; "medium"; "large"];
  229. stimulus = ["deviant"; "deviant"; "deviant"; "standard"; "standard"; "standard"];
  230. within = table(angle,stimulus,VariableNames=["AngleSize", "StimulusType"]);
  232. data = table(MMNamp(:,1), MMNamp(:,2), MMNamp(:,3), MMNamp(:,4), MMNamp(:,5), MMNamp(:,6), 'VariableNames',{'y1' 'y2' 'y3' 'y4' 'y5' 'y6'});
  233. rm = fitrm(data,'y1-y6 ~ 1','WithinDesign',within);
  234. ranovatbl = ranova(rm,'WithinModel',"AngleSize*StimulusType");
  235. ranovatbl.partialEta2 = nan(8,1);
  236. for eff = [3 5 7]
  238. SS = table2array(ranovatbl(eff,1));
  239. ErrSS = table2array(ranovatbl(eff+1,1));
  240. DFeff = table2array(ranovatbl(eff,2));
  241. DFerr = table2array(ranovatbl(eff+1,2));
  242. n2p = SS/(SS+ErrSS);
  243. ranovatbl.partialEta2(eff) = n2p;
  244. end
  246. fprintf('\n\nMMN: rmANOVA\n')
  247. disp(ranovatbl)
  248. disp('')
  250. % Follow-up t-tests: comparing deviant vs. standard ERPs for small, medium
  251. % and large angles
  252. fprintf('\n\nMMN: Pairwise t-test\n')
  253. for iAngle = 1:3
  254. [h,p,ci,stats] = ttest(MMNamp(:,iAngle), MMNamp(:,3+iAngle));
  255. cohen_d = mean(MMNamp(:,iAngle) - MMNamp(:,3+iAngle))/std(MMNamp(:,iAngle) - MMNamp(:,3+iAngle));
  256. if p >= 0.001
  257. fprintf('dev vs. sta %s: t(%i) = %0.3f, p = %0.3f, d = %0.3f\n', angles{iAngle}, stats.df, stats.tstat, p, cohen_d)
  258. else
  259. fprintf('dev vs. sta %s: t(%i) = %0.3f, p < 0.001, d = %0.3f\n', angles{iAngle}, stats.df, stats.tstat, cohen_d)
  260. end
  261. end
  263. % Follow-up t-tests: comparing MMN in small, medium and large angle size
  264. % conditions
  265. fprintf('\n\nMMN difference between angles: Pairwise t-test\n')
  266. pairs = nchoosek(1:3, 2);
  267. for iPair = 1:size(pairs,1)
  268. diff1 = MMNamp(:,pairs(iPair,1)) - MMNamp(:,3+pairs(iPair,1));
  269. diff2 = MMNamp(:,pairs(iPair,2)) - MMNamp(:,3+pairs(iPair,2));
  270. [h,p,ci,stats] = ttest(diff1, diff2);
  271. cohen_d = mean(diff1 - diff2)/std(diff1 - diff2);
  272. if p >= 0.001
  273. fprintf('%s vs. %s: t(%i) = %0.3f, p = %0.3f, d = %0.3f\n', angles{pairs(iPair,1)}, angles{pairs(iPair,2)}, stats.df, stats.tstat, p, cohen_d)
  274. else
  275. fprintf('%s vs. %s: t(%i) = %0.3f, p < 0.001, d = %0.3f\n', angles{pairs(iPair,1)}, angles{pairs(iPair,2)}, stats.df, stats.tstat, cohen_d)
  276. end
  277. end
  280. % Repeated-measures ANOVA on mastoidal MMN mean amplitudes
  281. disp('Effects of Angle Size and Stimulus Type on mastoidal MMN')
  282. angle = ["small"; "medium"; "large"; "small"; "medium"; "large"];
  283. stimulus = ["deviant"; "deviant"; "deviant"; "standard"; "standard"; "standard"];
  284. within = table(angle,stimulus,VariableNames=["AngleSize", "StimulusType"]);
  286. data_M = table(MMNamp_M(:,1), MMNamp_M(:,2), MMNamp_M(:,3), MMNamp_M(:,4), MMNamp_M(:,5), MMNamp_M(:,6), 'VariableNames',{'y1' 'y2' 'y3' 'y4' 'y5' 'y6'});
  287. rm_M = fitrm(data_M,'y1-y6 ~ 1','WithinDesign',within);
  288. ranovatbl_M = ranova(rm_M,'WithinModel',"AngleSize*StimulusType");
  289. ranovatbl_M.partialEta2 = nan(8,1);
  290. for eff = [3 5 7]
  292. SS = table2array(ranovatbl_M(eff,1));
  293. ErrSS = table2array(ranovatbl_M(eff+1,1));
  294. DFeff = table2array(ranovatbl_M(eff,2));
  295. DFerr = table2array(ranovatbl_M(eff+1,2));
  296. n2p = SS/(SS+ErrSS);
  297. ranovatbl_M.partialEta2(eff) = n2p;
  298. end
  300. fprintf('\n\nMMN at Mastoids: rmANOVA\n')
  301. disp(ranovatbl_M)
  302. disp('')

VirtualMaze_ERPanalysis.m, no license · at the source

Overview

Authors: Hanna Strauss1, Sabine Grimm2, Sascha Feder1, Jochen Miksch2, Alexandra Bendixen1
  1. Cognitive Systems Lab, Faculty of Natural Sciences, Institute of Physics, Chemnitz University of Technology, Chemnitz, Germany
  2. Physics of Cognition Group, Faculty of Natural Sciences, Institute of Physics, Chemnitz University of Technology, Chemnitz, Germany
Institutions: Chemnitz University of Technology (Germany)
Journal: Frontiers in neuroscience, volume 20, article 1907950
Dates: received 12 June 2026; accepted 17 August 2026; published online 3 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnins.2026.1907950 · PMID 42756237 · PMCID PMC13582665 · OpenAlex W7207753100
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: head movement, location deviant, oddball paradigm, predictive coding, self-generation, spatial auditory scene analysis, variable standard
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Technische Universität Chemnitz
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

Auditory scene analysis benefits from spatial separation of the sound sources to be disentangled. Studies characterizing the ability to localize sound sources have revealed that spatial ambiguities can be resolved by slightly moving the head. This implies that the auditory system must take into account how head movements displace the sensors (ears) relative to the sound source, in order to separate the consequences of own movement from actual movement of the sound source. Outside of the typical lab, not only the head can be turned, but also the whole body might move towards or away from the sound source. With modern virtual-reality (VR) technology including near-real-time auditory feedback, it has now become feasible to study the relations between body movement, head movement, and sound localization in a controlled manner. Here we present a VR study in which participants move through a virtual maze to locate a repetitive alarm signal. Participants’ turning decisions provide a behavioral measure of their ability to localize the sound source. In addition, we introduce occasional task-unrelated location deviations in the alarm signal to record an unobtrusive neural measure of localization ability: the Mismatch Negativity (MMN) component extracted from participants’ continuous electroencephalogram (EEG) informs us about participants’ deviance detection at the sensory level. We examine these neural and behavioral correlates of sound localization during simulated locomotion and real head movements. Our results show high behavioral accuracy and the elicitation of a robust MMN component. This indicates faithful extraction of source location although participants move relative to the sound source. Our findings are compatible with a predictive-coding framework, where the effects caused by own movement are taken into account for the interpretation of sensory signals. Yet because the range of physical variation by own movement was narrower than intended, we cannot exclude the possibility that deviance detection rested on simpler sensory mechanisms. We discuss how this constraint can be overcome in future studies to fully exploit our new paradigm for naturalistic, yet controlled studies on sound localization and auditory scene analysis under dynamic listening requirements.

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.

OSF ymgw3

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: MATLAB (2)
Size: 18 files, 2 scripts
Software Heritage: not checked
Found in: “Data availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), EEGLAB (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/ymgw3/

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

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;
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Data

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

Data availability statement

The pre-processed EEG data (single-subject averages) and behavioral data supporting the conclusions of this article are available alongside with the analysis code in a publicly accessible repository (https://osf.io/ymgw3/). The raw EEG data will be made available by the authors upon request, without undue reservation.

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 3, 28 September 2026

  • Funding: added Technische Universität Chemnitz

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 7 keywords, 70 references.

Cite

This paper

Strauss, H., Grimm, S., Feder, S., Miksch, J., & Bendixen, A. (2026). Studying dynamic sound source localization in a virtual maze with EEG and psychophysics. Frontiers in neuroscience, 20, 1907950. https://doi.org/10.3389/fnins.2026.1907950

BibTeX

@article{strauss2026studying,
author = {Strauss, Hanna and Grimm, Sabine and Feder, Sascha and Miksch, Jochen and Bendixen, Alexandra},
title = {{Studying dynamic sound source localization in a virtual maze with EEG and psychophysics}},
journal = {Frontiers in neuroscience},
year = {2026},
month = sep,
volume = {20},
pages = {1907950},
publisher = {Frontiers Media SA},
issn = {1662-4548},
doi = {10.3389/fnins.2026.1907950},
url = {https://doi.org/10.3389/fnins.2026.1907950},
pmid = {42756237},
pmcid = {PMC13582665}
}

RIS

TY - JOUR
AU - Strauss, Hanna
AU - Grimm, Sabine
AU - Feder, Sascha
AU - Miksch, Jochen
AU - Bendixen, Alexandra
TI - Studying dynamic sound source localization in a virtual maze with EEG and psychophysics
T2 - Frontiers in neuroscience
J2 - Front Neurosci
PY - 2026
DA - 2026/09/03
VL - 20
SP - 1907950
SN - 1662-4548
PB - Frontiers Media SA
DO - 10.3389/fnins.2026.1907950
UR - https://doi.org/10.3389/fnins.2026.1907950
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnins.2026.1907950",
"type": "article-journal",
"title": "Studying dynamic sound source localization in a virtual maze with EEG and psychophysics",
"container-title": "Frontiers in neuroscience",
"author": [
{
"family": "Strauss",
"given": "Hanna"
},
{
"family": "Grimm",
"given": "Sabine"
},
{
"family": "Feder",
"given": "Sascha"
},
{
"family": "Miksch",
"given": "Jochen"
},
{
"family": "Bendixen",
"given": "Alexandra"
}
],
"container-title-short": "Front Neurosci",
"volume": "20",
"page": "1907950",
"DOI": "10.3389/fnins.2026.1907950",
"PMID": "42756237",
"PMCID": "PMC13582665",
"ISSN": "1662-4548",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnins.2026.1907950",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
3
]
]
}
}

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