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Advantages and artifacts of high-speed OLED monitors for vision, eye-tracking, and EEG research.

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18 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 18 matches
  1. [1] § Methods › Practical test 2: Intra-saccadic stimulation ↔ Fig10_saccadeTask/plotdata_intrasaccadic_Fig9.m, lines 21–38 · score 0.82 · parallel port trigger, EYE EEG toolbox, downsampled, synchronized, EEGLAB, error
  2. [2] § Methods › Temporal light modulation (TLM) during each cycle ↔ Fig03_TLMflicker/Figure3_quantifyTLM_osf.m, lines 1–25 · score 0.81 · temporal light modulation, full screen stimuli, 0–255, TLM, gamma, spike
  3. [3] § Methods › Temporal independence (paired-pulse paradigm) ↔ Fig04_PairedPulsesTest/code_analyze_PairedPulseParadigm_OSF.m, lines 1–17 · score 0.80 · paired pulse paradigm, Hallum Cloherty, recorded photodiode, temporal dependencies, biphasic, sum
  4. [4] § Methods › Display properties and settings ↔ Fig04_PairedPulsesTest/code_analyze_PairedPulseParadigm_OSF.m, lines 1–17 · score 0.75 · Single biphasic pulses, double pulse, Paired pulse, temporal dependencies, simulate, summed
  5. [5] § Methods › Display properties and settings ↔ Fig03_TLMflicker/Figure3_quantifyTLM_osf.m, lines 1–25 · score 0.74 · temporal light modulation, flicker spikes, 0–255, ordinal, TLM, amplitude
  6. [6] § Results › Temporal independence (paired-pulse paradigm) ↔ Fig04_PairedPulsesTest/code_analyze_PairedPulseParadigm_OSF.m, lines 357–400 · score 0.72 · artificially doubling, double pulse, single pulse, simulation, spikes, amplitude
  7. [7] § Results › Transition times ↔ Fig01_Fig06C_Fig08_photodiodeData/plotdata_photodiode_Fig1_Fig4_Fig5.m, lines 47–141 · score 0.71 · duty cycle, peak luminance, luminance response, stimulus duration, extremely, horizontal
  8. [8] § Methods › Display properties and settings ↔ Fig10_saccadeTask/plotdata_intrasaccadic_Fig9.m, lines 267–334 · score 0.69 · flip command, critical latencies, saccade onset, boundary, executes, contingent
  9. [9] § Methods › Practical test 2: Intra-saccadic stimulation ↔ Fig10_saccadeTask/plotdata_intrasaccadic_Fig9.m, lines 49–66 · score 0.64 · eye tracking, velocity threshold, saccade detection, offline, algorithm
  10. [10] § Methods › Spatial uniformity ↔ Fig05_uniformity_angles_operatingtime/plotdata_photometer_Fig2.m, lines 10–33 · score 0.61 · operating temperature, luminance uniformity, viewing angle, IPS, LCD, Figure 5
  11. [11] § Methods › Temporal independence (paired-pulse paradigm) ↔ Fig04_PairedPulsesTest/code_analyze_PairedPulseParadigm_OSF.m, lines 357–400 · score 0.59 · double pulse, single pulse, prediction, summing, error, quantify
  12. [12] § Methods › Stimulus presentation ↔ Fig10_saccadeTask/plotdata_intrasaccadic_Fig9.m, lines 21–38 · score 0.58 · parallel port, photodiode signal, trigger
  13. [13] § Methods › Practical test 2: Intra-saccadic stimulation ↔ matlab/detect_saccade_2d_C_ONSET.cpp, lines 1–44 · score 0.57 · gaze position, retrieved, SDs, millisecond, algorithm, median
  14. [14] § Methods › Practical test 2: Intra-saccadic stimulation ↔ R/detect_saccade_for_R.c, lines 1–34 · score 0.56 · gaze position, retrieved, SDs, millisecond, algorithm, median
  15. [15] § Methods › Viewing angle ↔ Fig05_uniformity_angles_operatingtime/plotdata_photometer_Fig2.m, lines 10–33 · score 0.56 · horizontal viewing angle, azimuth, photometer, LCD, monitor, OLED
  16. [16] § Methods › Practical test 2: Intra-saccadic stimulation ↔ matlab/detect_saccade_2d_C_ONSET.cpp, lines 1–44 · score 0.55 · velocity threshold, saccade detection, SDs, algorithm, median, min
  17. [17] § Results › Practical test 1: Fast flicker stimulation ↔ Fig01_Fig06C_Fig08_photodiodeData/plotdata_photodiode_Fig1_Fig4_Fig5.m, lines 328–361 · score 0.55 · RIFT signal, photodiode signal, arbitrary, flickering, ms, cycles
  18. [18] § Methods › Transition times ↔ Fig01_Fig06C_Fig08_photodiodeData/plotdata_photodiode_Fig1_Fig4_Fig5.m, lines 47–141 · score 0.52 · peak luminance, stimulus duration, interval, rise, photodiode, cycle

Paper

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The authors' code

MATLAB · 521 lines · 20 KB · no license · 4 matches

  1. %% Test of 240 Hz OLED monitor: Analyzed eye-tracking & photodiode from experiment with intrasaccadic stimulation
  2. % This Matlab script generates Figure 6 of Dimigen & Stein, 2024
  3. % Code is provided solely to replicate our analysis, all rights reserved
  4. % The code is organized so that the analysis can be replicated without the need to download toolboxes
  5. %
  6. % [email hidden], 2024
  8. clear all, close all
  10. %% subfunctions needed
  11. addpath Violinplot-Matlab % from: https://github.com/bastibe/Violinplot-Matlab (DOI: 10.5281/zenodo.4559847)
  12. addpath eeglab_eyeeeg % EEGLAB (copy of version 2021.1) with the EYE-EEG extension (v1.0) installed
  13. eeglab; close % start EEGLAB % close the GUI window (not needed)
  15. % load custom colormap for plotting
  16. load colormap.mat cmap % cbrewer('seq','Blues', 256,[]);
  18. %% load logfile of the experiment (Matlab format)
  19. load('data_logfile/OLED_logfile_901.mat');
  21. %% Load integrated dataset (EEGLAB format) containing synchronized eye movement & photodiode data
  22. EEG = pop_loadset('filename','OLED_eeglab_synchronized_901.set','filepath','data_eeglab/');
  24. % Notes about this dataset
  25. % - integrated data was generated with the EYE-EEG toolbox (v.99)
  26. % - channel names should be self-explanatory
  27. % - photo didoe data was downsampled from the original 8000 Hz to now 2000 Hz
  28. % - eye-trackign data was upsampled from the original 1000 Hz to now 2000 Hz
  29. % - data is near-perfectly synchronized with error < 0.4 ms
  30. % Channels:
  31. % Channel 1: Photodiode signal (raw)
  32. % Channel 2: Left eye, horizontal gaze position (X)
  33. % Channel 3: Left eye, vertical gaze position (Y)
  34. % Channel 4: Received parallel port triggers
  35. % Channel 5: Eye velocity X (optional, only minimally smoothed)
  36. % Channel 6: Eye velocity Y (optional, only minimally smoothed)
  37. % Channel 7: eye velocity, 2D
  38. % Channel 8: Photodiode signal (normalized, range 0 to 1)
  40. %% Cut an initial set of epochs around the trigger marking that the saccade was detected online ("S99")
  41. % make the epoch long enough so it does not distort the median-based velocity
  42. % threshold computation by including predominantly saccade samples
  43. EEG = pop_epoch(EEG,{'s99'}, [-0.5 0.5], 'epochinfo', 'yes');
  44. EEG = pop_rmbase(EEG,[-200 -100],[],1); % note: remove baseline ONLY from photo channel not from eye-tracking data!
  46. %% Remove epochs with blinks (sub-zero gaze values)
  47. [~, ix_badepochs] = pop_eegthresh(EEG,1,[2 3],[1 1],[c.resx c.resy],EEG.xmin,EEG.xmax,0,0);
  49. %% Detect saccades with adaptive Engbert & Kliegl (2003) algorithm
  50. LAMBDA = 6; % velocity multiplier
  51. MINDUR = 20; % = 10 ms at 2000 Hz sampling rate
  52. SMOOTH = 1; % smooth velocity time series
  53. GLOABLTHRESH = 0; % compute velocity threshold individ. for each epoch
  54. MININTERVAL = 100; % has no effect here
  55. CLUSTERMODE = 1; % detect all saccades (can include post-sacc. oscillations)
  56. PLOTFIG = 0;
  57. ADDSACC = 1;
  58. ADDFIX = 1;
  59. degperpix = 0.0190; % degrees of visual angle per screen pixel
  61. % Note: since we have an upsampled 2 kHz sampling rate for the eye-tracking data,
  62. % even more velocity smoothing might be a good idea, the exact saccON to saccDETECT interval
  63. % will obviously always depend on the algorithm and parameters for offline
  64. % saccade detection, but this interval is not essential for the current
  65. % monitor test
  66. EEG = pop_detecteyemovements(EEG,[2 3],[],LAMBDA,MINDUR,degperpix,SMOOTH,GLOABLTHRESH,MININTERVAL,CLUSTERMODE,PLOTFIG,ADDSACC,ADDFIX);
  68. %% Plot the epoched data in EEGLAB
  69. pop_eegplot( EEG, 1, 1, 1); % note: click "Disply" --> "Remove DC Offset" to the gaze position channels properly
  72. %% Loop trough the epochs
  73. % - detect when OLED reaches 90% luminance in trial
  74. % - store epoch latencies
  75. for e = 1:size(EEG.data,3)
  77. % get onset of stim (onset = >90% of avg. sustained luminance of stimulus)
  78. epdata = squeeze(EEG.data(1,:,e));
  79. % normalize data using maximal peak (can be outlier due to flicker or noise)
  80. [epmx, mxsmp] = max(epdata);
  81. dataN = epdata ./ epmx;
  82. % now get actual sustained stim. luminance while the stim. is on (takes into account flicker/noise)
  83. ixstim = find(dataN > .80); % we define peak duration as values above 80% of "epmx"
  84. mlum = mean(dataN(ixstim)); % take mean luminance
  86. % stim onset: look for first point exceeding 90% of sustained stim. luminance
  87. LUMTHRESH_ON = 0.90;
  88. LUMTHRESH_OFF = 0.10;
  89. oled_on = find(dataN > LUMTHRESH_ON.*mlum);
  90. if ~isempty(oled_on)
  91. oled_on_ms = EEG.times(oled_on(1));
  92. else
  93. error('No onset found, there might be a problem')
  94. end
  96. % stimulus offset: once stim. has dropped below 10% again
  97. oled_off90 = oled_on(end);
  98. oled_below10 = dataN < LUMTHRESH_OFF.*mlum; % samples below 10%
  99. helpvec = false(1,size(EEG.data,2));
  100. helpvec(oled_off90:end) = true;
  101. oled_off10 = find(oled_below10 & helpvec); % get samples below 10% happening after lumin. has dropped again
  103. % get latency of last stimulus sample in ms
  104. oled_off90_ms = EEG.times(oled_off90);
  106. % did we find a proper offset?
  107. if ~isempty(oled_off10)
  108. oled_off10 = oled_off10(1);
  109. oled_off10_ms = EEG.times(oled_off10);
  110. else
  111. oled_off10 = NaN;
  112. oled_off10_ms = NaN;
  113. % warning('No offset <10 perc. found, maybe stim. was shown for too long?');
  114. end
  116. %% Control figure for onset/offset detection
  117. % figure; hold on; title(sprintf('Epoch: %i',e));
  118. % plot(EEG.times,epdata);
  119. % xline(oled_on_ms,'g')
  120. % xline(oled_off90_ms,'r')
  121. % xline(oled_off10_ms,'k:')
  122. % xlim([-30 80])
  123. fprintf('\nApprox. stim. lumin. in epoch %i was %.2f',e,mlum);
  125. %% Get infos for this epoch from EEG.epoch
  126. E = EEG.epoch(e);
  127. % E.eventtype
  129. ix_sac = find(ismember(E.eventtype,'saccade'));
  130. ix_bnd = find(ismember(E.eventtype,'s99'));
  131. ix_Son = find(ismember(E.eventtype,{'s20','s21'}));
  132. ix_Soff = find(ismember(E.eventtype,{'s30','s31'}));
  133. ix_fix = find(ismember(E.eventtype,'fixation'));
  135. % catch rare exception: ix_sac is empty
  136. if ~isempty(ix_sac)
  138. if length(ix_sac)>1
  139. lats = [E.eventlatency{ix_sac}];
  140. [~,ixs] = min(abs(lats)); % find saccade temporally closest to boundary trigger
  141. else
  142. ixs = 1;
  143. end
  144. ms_sac = E.eventlatency{ix_sac(ixs)};
  145. sacamp = E.eventsac_amplitude{ix_sac(ixs)};
  146. x_saccOn = E.eventsac_startpos_x{ix_sac(ixs)};
  147. y_saccOn = E.eventsac_startpos_y{ix_sac(ixs)};
  148. x_saccOff = E.eventsac_endpos_x{ix_sac(ixs)};
  149. y_saccOff = E.eventsac_endpos_y{ix_sac(ixs)};
  151. else % ix_sac empty?
  152. ms_sac = NaN;
  153. sacamp = NaN;
  154. x_saccOn = NaN;
  155. y_saccOn = NaN;
  156. x_saccOff = NaN;
  157. y_saccOff = NaN;
  158. end
  160. ms_bnd = E.eventlatency{ix_bnd(1)};
  161. ms_Son = E.eventlatency{ix_Son(1)};
  162. if ~isempty(ix_Soff)
  163. ms_Soff = E.eventlatency{ix_Soff(1)};
  164. else
  165. ms_Soff = NaN;
  166. end
  168. if ~isempty(ix_fix)
  169. ms_fix = E.eventlatency{ix_fix(2)};
  170. fix_x = E.eventfix_avgpos_x{ix_fix(2)};
  171. fix_y = E.eventfix_avgpos_y{ix_fix(2)};
  172. else
  173. ms_fix = NaN;
  174. fix_x = NaN;
  175. fix_y = NaN;
  176. end
  178. %% check for blinks in this epoch/trial
  179. xydata = EEG.data([2 3],:,e);
  180. isblink = any(xydata(:) < 0); % any blinks in this trial?
  182. %%
  183. trialinfo(e,1) = e;
  184. trialinfo(e,2) = ms_sac;
  185. trialinfo(e,3) = ms_bnd;
  186. trialinfo(e,4) = ms_Son; % on trigger
  187. trialinfo(e,5) = oled_on_ms; % actual on
  188. trialinfo(e,6) = ms_Soff; % off trigger
  189. trialinfo(e,7) = oled_off10_ms; % actual off
  190. trialinfo(e,8) = ms_fix;
  191. trialinfo(e,9) = fix_x;
  192. trialinfo(e,10) = fix_y;
  193. trialinfo(e,11) = sacamp;
  194. trialinfo(e,12) = fliplog(e,2); % trialnumber (just to check)
  195. trialinfo(e,13) = fliplog(e,12); % gaze X at saccade detection / bound. crossing
  196. trialinfo(e,14) = fliplog(e,13); % gaze Y at saccade detection / bound. crossing
  197. trialinfo(e,15) = fliplog(e,14); %
  198. trialinfo(e,16) = fliplog(e,15); %
  199. trialinfo(e,17) = isblink;
  200. trialinfo(e,18) = x_saccOn;
  201. trialinfo(e,19) = y_saccOn;
  202. trialinfo(e,20) = x_saccOff;
  203. trialinfo(e,21) = y_saccOff;
  204. if ~ismember(e,ix_badepochs)
  205. trialinfo(e,22) = 0; % good, gaze on screen
  206. else
  207. trialinfo(e,22) = 1; % bad, gaze temporarily lost or outside screen
  208. end
  210. % write normalized photo diode data into empty channel 8
  211. EEG.data(8,:,e) = dataN;
  213. end % go tru epochs
  215. % note: latencies for the stim. triggers have a temporal "pseudo-resolution" now, due
  216. % to the downsampling from 8K to 2K; round them again to nearest sample
  217. accu = 0.5;
  218. trialinfo(:,4) = round(trialinfo(:,4)./accu)*accu;
  220. % Add actual photo onsets and offsets as new EEG.event events
  221. % Example use of addevents:
  222. % EEG = addevents(EEG,[100 1 456 1; 200 1 789 2;],{'latency','duration','xxxx','epoch'},'ARRRRRRGGG');
  224. %% Get a subset of trials without problems (sanity checks)
  225. ixgood = trialinfo(:,11) > 16 ...
  226. & trialinfo(:,11) < 24 ... % sacc. amplitude not off by more than 4°
  227. & trialinfo(:,20) > c.boundary2xpix ... % sacc. of proper/sufficient amplitude to cross right boundary (at
  228. ...
  229. & trialinfo(:,2) < trialinfo(:,3) ... % saccOn before saccDetect (!)
  230. & trialinfo(:,7) > trialinfo(:,5) ... % stimOff after stimOn (!)
  231. & trialinfo(:,8) > trialinfo(:,2) ... % fixOn after saccOn (!)
  232. & ~isnan(trialinfo(:,2)) ... % at least 1 saccade event was found
  233. & ~isnan(trialinfo(:,8)) ... % at least 1 fixation event was found
  234. ...
  235. & ~isnan(trialinfo(:,6)) ... % a stim offset trigger was found
  236. & trialinfo(:,7) <= 198 ... % OLED offset (signal below 10% again) was detected within 200 ms of boundary crossing
  237. ...
  238. & trialinfo(:,2) > -30 ... % detected a saccade within -30 ms...
  239. & trialinfo(:,2) < 0 ... % ...to 0 ms relative to boundary trigger
  240. & trialinfo(:,22) == 0; % gaze data in epoch contains no out-of-monitor-area values
  242. %% Visualize the gaze and photo diode signal in "bad" trials
  243. % ixbad = find(~ismember(1:200, find(ixgood)))
  244. % for j = ixbad
  245. % EEG.epoch(j)
  246. % figure;
  247. % subplot(2,1,1); hold on, plot(EEG.data(2,:,j),'r-'); plot(EEG.data(3,:,j),'b-'); title(j) % gaze
  248. % subplot(2,1,2); hold on, plot(EEG.data(1,:,j),'k-'); title('Photodiode')
  249. % pause
  250. % close
  251. % end
  253. %% Make figure of single-trial latencies as dots
  254. figure;
  255. hold on; title('Critical latencies')
  256. plot(trialinfo(ixgood,2),'mo')
  257. plot(trialinfo(ixgood,3),'b.-')
  258. plot(trialinfo(ixgood,4),'cx')
  259. plot(trialinfo(ixgood,5),'ko')
  260. plot(trialinfo(ixgood,6),'m.')
  261. plot(trialinfo(ixgood,7),'b.')
  262. plot(trialinfo(ixgood,8),'r.')
  263. ylim([-20 60])
  264. yline(0,'k:')
  266. % #########################################################################
  267. %% Compute the most interesting latencies
  268. % #########################################################################
  270. fprintf('\n\nCritical latencies in the saccade-contingent experiment:\ns')
  272. % Interval (1): Saccade onset to saccade detected
  273. mean(trialinfo(ixgood,3)-trialinfo(ixgood,2)) % 444: 6.35 ms vs. 5.64 (445) vs. 4.714 ms (901)
  274. std(trialinfo(ixgood,3)-trialinfo(ixgood,2))
  275. % MS: 5.28 ms
  276. % SD: 1.84 ms
  278. % Interval (2): Saccade detected to flip command executed
  279. mean(trialinfo(ixgood,4)-trialinfo(ixgood,3)) % 444: 4.56 ms vs. 4.51 vs. 4.683
  280. std(trialinfo(ixgood,4)-trialinfo(ixgood,3))
  281. % MS: 4.69 ms
  282. % SD: 1.19 ms
  284. % Interval (3): Flip command executed to OLED reaches 90% luminance
  285. mean(trialinfo(ixgood,5)-trialinfo(ixgood,4)) % 444: 2.90 ms vs. 2.92 (of which (1000/240)/2) = 2.08 ms should be due to central position vs. 2.99 ms
  286. std(trialinfo(ixgood,5)-trialinfo(ixgood,4))
  287. % MS: 2.92 ms
  288. % SD: 0.29 ms
  290. % Interval (4): Saccade-contingent display change latency: Boundary to StimOn
  291. mean(trialinfo(ixgood,5))
  292. std(trialinfo(ixgood,5))
  293. % MS: 7.61 ms
  294. % SD: 1.18 ms
  295. median(trialinfo(ixgood,5))
  297. % median(trialinfo(ixgood,5))
  298. % min(trialinfo(ixgood,5))
  299. % max(trialinfo(ixgood,5))
  301. % Interval (5): Total time: Saccade onset until OLED reaches 90% luminance
  302. mean(trialinfo(ixgood,5)-trialinfo(ixgood,2)) % 444: 13.82 ms, 445: 13.07 ms, 901: 12.39 ms
  303. std(trialinfo(ixgood,5)-trialinfo(ixgood,2))
  304. % MS: 12.89 ms
  305. % SD: 2.25 ms
  307. median(trialinfo(ixgood,5)-trialinfo(ixgood,2)) % 901: 12 ms
  308. % min(trialinfo(ixgood,5)-trialinfo(ixgood,2)) % 444: 5 ms, 445: 5.5 ms, 901: 5.5
  309. % max(trialinfo(ixgood,5)-trialinfo(ixgood,2)) % 444: 20.5 ms, 445: 254 ms, 901: 21 ms
  311. % Extra-Time: 4: Fixation onset relative to OLED offset
  312. mean(trialinfo(ixgood,8)-trialinfo(ixgood,7)) % 444: 2.90 ms vs. 2.92 (of which (1000/240)/2) = 2.08 ms should be due to central position, 7.50 ms for 901!
  313. std(trialinfo(ixgood,8)-trialinfo(ixgood,7))
  314. % MS: 8.13 ms
  315. % SD: 3.58 ms
  317. % min(trialinfo(ixgood,8)-trialinfo(ixgood,7)) % -8
  318. % max(trialinfo(ixgood,8)-trialinfo(ixgood,7)) % +9
  320. % how many offsets were well-timed and not too late?
  321. sum(trialinfo(ixgood,8)-trialinfo(ixgood,7) > 0) % 72 well-timed; 901: 155!
  322. sum(trialinfo(ixgood,8)-trialinfo(ixgood,7) <= 0) % 99 slightly too late, 901: 6!
  324. fprintf('\n\nPercentage of trials remaining: %.3f', 100 * (sum(ixgood) / 200))
  326. % percentage of false alarms = stimOn happened before saccOn
  327. nFalseAlarm = sum(trialinfo(:,5)-trialinfo(:,2) <= 0)
  328. fprintf('\n\nTrials with a false alarm (premature change): %.3f', 100 * (nFalseAlarm / 200))
  330. saccdur = mean(trialinfo(ixgood,8)-trialinfo(ixgood,2)); % 60.36 ms
  331. stimdur = mean(trialinfo(ixgood,7)-trialinfo(ixgood,5)); % 39.33 ms
  332. saccperc = (stimdur/saccdur).*100;
  333. fprintf('\n\nMean Saccade Duration and Stimulus Duration: %.3f and %.3f ms', saccdur, stimdur)
  334. fprintf('\n\nStimulus was shown for %.3f percent of the entire saccade duration (as detected offline)', saccperc)
  336. %% Remove bad epochs from the EEG.data (i.e., those not found in "ixgood")
  337. ixkill = find(~ixgood);
  338. EEG = pop_selectevent(EEG,'omitepoch',ixkill,'deleteevents','off','deleteepochs','on','invertepochs','off');
  340. %% Re-code critical saccade as "saccadeCrit" in EEG.event
  341. % (to distinguish them from microsaccades/refixations/overshoot saccades)
  342. for e = 1:size(EEG.data,3)
  344. EP = EEG.epoch(e);
  345. nsacc = sum(ismember(EP.eventtype,'saccade'));
  346. ix = find(ismember(EP.eventtype,'saccade'));
  347. if nsacc > 0
  348. % recode name of first saccade, which should always be the critical one
  349. eventindex = EP.event(ix(1));
  350. EEG.event(eventindex).type = 'saccadeCrit';
  351. latency_ms = EP.eventlatency{ix(1)};
  352. if latency_ms < -30 || latency_ms > 1
  353. error('Something wrong here, implausible latency for critical saccade')
  354. end
  355. else
  356. error('epoch without saccade event!: %i',e)
  357. end
  358. end
  359. EEG = eeg_checkset(EEG,'eventconsistency'); % rebuild/update EEG.epoch
  362. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  363. %% MAKE BIG FIGURE (Figure 6 of manuscript
  364. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  365. SMOOTH = 1; % = do not do vertical smoothing across trials
  366. CHAN = 8;
  367. xLimits = [-20 65];
  368. LW = 1.5; % linewidth
  369. LW2 = 1.1;
  371. % EEG3: Cut new epochs around the (online) saccade detection event
  372. EEG3 = pop_epoch(EEG,{'s99'},[-0.030 0.070],'epochinfo','yes');
  374. % get ERPimage
  375. [outdata,outvar,outtrials] = pop_erpimage(EEG3,1,CHAN,[],'pDiode',SMOOTH,1,{'s20','s21'},[],'latency' ,'yerplabel','\muV','erp','on','cbar','on','topo', {[1] EEG.chanlocs EEG.chaninfo}); close;
  376. % outdata: nsamples * epochs
  377. % outvar: sorting variable
  379. % ERP averages
  380. ERP_s99_hgaze_median = median(EEG3.data(2,:,:),3);
  381. ERP_s99_velo_median = median(EEG3.data(7,:,:),3);
  382. ERP_s99_horVelo_median = median(EEG3.data(5,:,:),3);
  384. %% Data for VIOLIN plot
  385. % trialinfo(e,2) = ms_sac;
  386. % trialinfo(e,3) = ms_bnd;
  387. % trialinfo(e,4) = ms_Son; % on trigger
  388. % trialinfo(e,5) = oled_on_ms; % actual on
  389. % trialinfo(e,6) = ms_Soff; % off trigger
  390. % trialinfo(e,7) = oled_off10_ms; % actual off
  391. % trialinfo(e,8) = ms_fix;
  393. % data for violin plots (from good trials)
  394. viodata = [trialinfo(ixgood,2),trialinfo(ixgood,4),trialinfo(ixgood,5), trialinfo(ixgood,7), trialinfo(ixgood,8)];
  396. % get median values
  397. medi(1) = nanmedian(viodata(:,1));
  398. medi(2) = nanmedian(viodata(:,2));
  399. medi(3) = nanmedian(viodata(:,3));
  400. medi(4) = nanmedian(viodata(:,4));
  401. medi(5) = nanmedian(viodata(:,5));
  403. xmean(1) = nanmean(viodata(:,1));
  404. xmean(2) = nanmean(viodata(:,2));
  405. xmean(3) = nanmean(viodata(:,3));
  406. xmean(4) = nanmean(viodata(:,4));
  407. xmean(5) = nanmean(viodata(:,5));
  409. xstd(1) = nanstd(viodata(:,1));
  410. xstd(2) = nanstd(viodata(:,2));
  411. xstd(3) = nanstd(viodata(:,3));
  412. xstd(4) = nanstd(viodata(:,4));
  413. xstd(5) = nanstd(viodata(:,5));
  415. % get colors
  416. viocolors = [cmap(50,:); cmap(101,:); cmap(152,:); cmap(203,:); cmap(255,:)];
  418. %% CREATE FIGURE 6 OF MANUSSRIPT
  419. figure('Name','OLED: Big Figure')
  421. %% 1. Violin plot (using violinplot.m from B. Bechtold)
  422. subplot(3,1,1); hold on;
  423. vs = violinplot(viodata,[],...
  424. 'Orientation', 'horizontal',...
  425. 'ViolinColor',viocolors,...
  426. 'DataStyle', 'scatter',... % histogram, none...
  427. 'ShowMedian', true,...
  428. 'ShowMean', true,...
  429. 'MedianMarkerSize',60,...
  430. 'MarkerSize',6,... % data points, default is 24
  431. 'BoxColor',[.1 .1 .1],... % color for box-and-whiskers
  432. 'ShowNotches', false,...
  433. 'QuartileStyle','boxplot',... % , none
  434. 'HalfViolin','full',... % left, full
  435. 'QuartileStyle','boxplot'); % shadow. boxplot, none
  437. ylim([0.5 5.5])
  438. yticks(1:5)
  439. yticklabels({'Sacc. onset','Flip','Stim. ON (>90%)','Stim. OFF (<5%)','Fixation'})
  440. xlabel('Time relative to sacc. detection [ms]');
  441. xline(0,'k-','linewidth',LW) % detection time = 0 ms
  443. xline(medi(1),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % sacc detection
  444. xline(medi(2),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % flip
  445. xline(medi(3),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % stim at 90%
  446. xline(medi(4),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % stim at 95%
  447. xline(medi(5),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % fixation onset
  448. grid on;
  449. xlim(xLimits);
  450. set(gca,'XTick',xLimits(1):5:xLimits(2));
  451. box off
  453. FIGSIZE = [650 10 1090 920];
  454. set(findall(gcf,'-property','FontName'),'FontName','Arial');
  455. set(findall(gcf,'-property','FontSize'),'FontSize',12);
  456. set(gcf,'color','white')
  457. set(gcf,'Position',FIGSIZE)
  459. nanmedian(viodata(:,3))
  460. nanmean(viodata(:,3))
  462. %% 2. Average saccadic gaze position trajectory
  463. subplot(3,1,2); hold on;
  465. % yyaxis left
  466. plot(EEG3.times,ERP_s99_hgaze_median,'Color',cmap(206,:),'LineWidth',LW)
  467. xlim(xLimits)
  468. xline(0,'k-','linewidth',LW)
  469. yline(30,'k:','LineWidth',LW/2) % 30°/sec line
  470. grid on
  471. % ylabel('Eye velocity (°/s)')
  472. ylabel('Horiz. gaze position [px]')
  473. xlabel('Time relative to saccade detection (ms)')
  474. set(gca,'XTick',[xLimits(1):5:xLimits(2)]);
  475. xline(medi(1),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % sacc detection
  476. xline(medi(2),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % flip
  477. xline(medi(3),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % stim at 90%
  478. xline(medi(4),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % stim off (< 5%)
  479. xline(medi(5),'Color',[.2 .2 .2],'linestyle',':','linewidth',LW2) % fixation onset
  480. % limits for gaze:
  481. ylim([c.fixposLx-100 c.fixposRx+100])
  483. fill([medi(3) medi(4) medi(4) medi(3)],[c.fixposLx-100 c.fixposLx-100 c.fixposRx+100 c.fixposRx+100],[.4 .2 .1],'edgecolor','none','facealpha',0.2)
  485. % yyaxis right
  486. % plot(EEG3.times,ERP_s99_horVelo_median,'Color',cmap(56,:),'LineWidth',LW)
  487. % ylim([0 500])
  488. % ylabel('Eye velocity [deg/s]')
  490. %% 3. ERPIMAGE of single-trial photo diode responses
  491. h1 = subplot(3,1,3); hold on;
  493. img = outdata';
  494. imagesc(EEG3.times,1:size(EEG3.data,3),img);
  496. % plot sorting line
  497. plot(outvar,1:size(EEG3.data,3),'k-','LineWidth',LW)
  499. xline(0,'k-','linewidth',LW)
  500. ylabel('Trials')
  501. xlabel('Time relative to saccade detection (ms)')
  502. orgSize = get(gca,'Position'); % add colorbar without resizing
  503. colorbar
  504. set(gca,'Position',orgSize);
  505. colormap(cmap)
  506. set(gca,'XTick',[xLimits(1):5:xLimits(2)]);
  507. axis tight
  508. xlim(xLimits)
  509. grid on
  511. % resize figure
  512. set(gcf,'Position',[10 10 975 980]);
  514. %% export
  515. % EXPORTFIG = false;
  516. % if EXPORTFIG
  517. % addpath M:/Dropbox/_subfunc_master/export_fig_2018
  518. % export_fig(gcf,sprintf('G:/Meine Ablage/2024_MonitorTest/_Results_Figures/ISS_simple/ISS_Figure6.eps'),'-painters','-transparent','-pdf')
  519. % end
  521. fprintf('\nDone.')

plotdata_intrasaccadic_Fig9.m, no license · at the source

Overview

Authors: Olaf Dimigen1,2, Arne Stein1,2
ORCID iDs: Olaf Dimigen
  1. Department of Experimental Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands
  2. Research School of Behavioural and Cognitive Neurosciences, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
Institutions: University of Groningen (Netherlands)
Journal: Behavior research methods, volume 58, issue 6, article 161
Dates: received 5 September 2024; accepted 14 April 2026; published online 11 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3758/s13428-026-03034-9 · PMID 42115564 · PMCID PMC13161341 · OpenAlex W7160844750
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), methods / tools (subfield)
Methods: Connectivity, Statistics, Physiology & signal measures
Keywords: Laboratory monitor, Organic light-emitting diode (OLED), Visual psychophysics, Vision science, Gaze-contingent display updates, OLED temperature artifacts
MeSH: Artifacts*, Electroencephalography*, Eye-Tracking Technology*, Vision, Ocular*, Humans, Photic Stimulation (* major topic)
Topic: Organic Light-Emitting Diodes Research (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 65 references in the paper

Abstract

The recent introduction of organic light-emitting diode (OLED) monitors with refresh rates of 240 Hz or more opens new possibilities for their use as precise stimulation devices in vision research, experimental psychology, and electrophysiology. These affordable high-speed monitors, targeted at video gamers, promise several advantages over cathode ray tube (CRT) and liquid crystal display (LCD) monitors. Unlike LCDs, OLEDs have self-emitting pixels that can show true black, resulting in superior contrast, a broad color spectrum, and wide viewing angles. More importantly, the latest OLEDs offer excellent timing properties with minimal input lag and rapid transition times. However, OLED technology also has potential drawbacks, such as auto-brightness limiting (ABL), where luminance changes with the number of illuminated pixels. This study characterized a 240 Hz OLED monitor (ASUS PG27AQDM) in terms of its timing, temporal independence, spatial uniformity, viewing angles, warm-up time, and ABL behavior, and compared it with CRTs and LCDs. Results confirm excellent temporal performance, with CRT-like transition times, wide viewing angles, and good spatial uniformity. We show that ABL can be prevented with appropriate settings. However, we also report a novel type of luminance artifact on OLEDs, where high-contrast stimuli, shown for long durations, can create image persistence via localized warming or cooling of the panel. Finally, we demonstrate the monitor’s benefits in two time-critical paradigms: rapid invisible flicker tagging (RIFT) and saccade-contingent display changes. The latest consumer OLEDs provide precise and cost-effective stimulation devices for time-critical experiments, although some caution is warranted in experiments involving long exposures to high-contrast stimuli.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 18 matches between paragraphs and lines of code.

richardschweitzer/OnlineSaccadeDetection

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: bad52ff7ad164abf3795fbf6476f162d42535e25, 29 October 2024
Languages: R (3), C (2), C/C++ (2), MATLAB (2), C++ (1), Python (1)
Size: 22 files, 11 scripts
Software Heritage: not archived
Found in: the text, “Practical test 2: Intra-saccadic stimulation”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), Psychtoolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/richardschweitzer/OnlineSaccadeDetection

OSF 8h4fg

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (6)
Size: 13 files, 6 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (5 files), Statistics and Machine Learning Toolbox (2 files), Curve Fitting Toolbox (1 file), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
6 files
At the source: osf.io/8h4fg/

Code availability

Code is available; see statement regarding availability of data and materials.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 17 scripts, each with its path and the digest of its content;
  • 18 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 Statement

Data and MATLAB code to reproduce all results and figures are available at https://osf.io/8h4fg/

Code is available; see statement regarding availability of data and materials.

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 2, 28 September 2026

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

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 6 MeSH terms, 51 references.

Cite

This paper

Dimigen, O., & Stein, A. (2026). Advantages and artifacts of high-speed OLED monitors for vision, eye-tracking, and EEG research. Behavior research methods, 58(6), 161. https://doi.org/10.3758/s13428-026-03034-9

BibTeX

@article{dimigen2026advantages,
author = {Dimigen, Olaf and Stein, Arne},
title = {{Advantages and artifacts of high-speed OLED monitors for vision, eye-tracking, and EEG research}},
journal = {Behavior research methods},
year = {2026},
month = may,
volume = {58},
number = {6},
pages = {161},
publisher = {Springer Science+Business Media},
issn = {1554-351X},
doi = {10.3758/s13428-026-03034-9},
url = {https://doi.org/10.3758/s13428-026-03034-9},
pmid = {42115564},
pmcid = {PMC13161341}
}

RIS

TY - JOUR
AU - Dimigen, Olaf
AU - Stein, Arne
TI - Advantages and artifacts of high-speed OLED monitors for vision, eye-tracking, and EEG research
T2 - Behavior research methods
J2 - Behav Res Methods
PY - 2026
DA - 2026/05/11
VL - 58
IS - 6
SP - 161
SN - 1554-351X
PB - Springer Science+Business Media
DO - 10.3758/s13428-026-03034-9
UR - https://doi.org/10.3758/s13428-026-03034-9
LA - en
ER -

CSL-JSON

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"id": "10.3758/s13428-026-03034-9",
"type": "article-journal",
"title": "Advantages and artifacts of high-speed OLED monitors for vision, eye-tracking, and EEG research",
"container-title": "Behavior research methods",
"author": [
{
"family": "Dimigen",
"given": "Olaf"
},
{
"family": "Stein",
"given": "Arne"
}
],
"container-title-short": "Behav Res Methods",
"volume": "58",
"issue": "6",
"page": "161",
"DOI": "10.3758/s13428-026-03034-9",
"PMID": "42115564",
"PMCID": "PMC13161341",
"ISSN": "1554-351X",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.3758/s13428-026-03034-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
11
]
]
}
}

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