<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Harold,<div><br></div><div>have you checked out ft_statfun_correlationT?</div><div>This sounds like the function you're looking for. Use cfg.statistic = 'ft_statfun_correlationT' in your call to ft_timelockstatistics.</div><div>Plus you can use all the correction methods available in FT.</div><div>See the FAQ for more info: <a href="http://www.fieldtriptoolbox.org/faq/how_can_i_test_for_correlations_between_neuronal_data_and_quantitative_stimulus_and_behavioural_variables">http://www.fieldtriptoolbox.org/faq/how_can_i_test_for_correlations_between_neuronal_data_and_quantitative_stimulus_and_behavioural_variables</a></div><div><br></div><div>Good luck,</div><div><br></div><div>Julian</div><div><br><div>
<span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; font-size: 12px; ">********************</div><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><b>Dr. Julian Keil</b></div><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><b><br></b></div>AG Multisensorische Integration<br>Psychiatrische Universitätsklinik<br>der Charité im St. Hedwig-Krankenhaus<br>Große Hamburger Straße 5-11, Raum A007<br>10115 Berlin</div><div><br>Telefon: +49-30-2311-1879<br>Fax: +49-30-2311-2209 </div><div><a href="http://psy-ccm.charite.de/forschung/bildgebung/ag_multisensorische_integration">http://psy-ccm.charite.de/forschung/bildgebung/ag_multisensorische_integration</a></div></span>
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<br><div><div>Am 26.01.2016 um 00:25 schrieb Harold Cavendish:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div dir="ltr">Dear FieldTrip users,<br><div class="gmail_quote"><div dir="ltr"><div><br></div><div>I'm looking for the best method to analyse the relationship between ERP and behavioural data but all I've been able to find so far are methods to compare ERPs (or more generally time-series).</div><div><br></div><div>Consider the following example: 20 participants performed a memory-related task in which they had to respond based on the difference between the target stimulus and the probe stimulus. EEG (50 electrodes, about 1000 samples at 200 Hz) and responses were recorded; ERPs were then created based on epochs in which the responses were correct. Similarly, response accuracy was averaged over all successful epochs per each subject.</div><div><br></div><div>The resulting data are therefore: 20 participants x 50 channels x 1000 samples (ERP) and 20 averages of accuracy (one value per participant).</div><div><br></div><div>My objective is to identify which portion of the ERP data best explains (or predicts) the changes in accuracy. In other words, in which time interval is the most significant difference between people who do well in the task and those who don't.</div><div><br></div><div>So far I've been doing correlations between ERP at each time point and overall accuracy, which is unreliable due to multiple comparisons (correction methods are reportedly too conservative). Nevertheless, certain channels show quite strong correlations in multiple time intervals.</div><div><br></div><div>How would you approach this problem, please?</div><div><br></div><div>Many thanks!</div><div><br></div><div>Best regards,</div><div>Harold</div></div>
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