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<div>Hi,</div>
<div><br /></div>
<div>I have been trying to load my EEG data (~8 hours data from an overnight sleep study) into BCILAB for offline analyses but when I inspect it before training, the EEG plot isn't displayed correctly and chunks of my data are missing. I collected my EEG data using BrainVision Recorder and added channel locations and event labels using EEGLAB. I loaded this dataset (.set) to BCILAB. Interestingly, as you can see in the images (https://ibb.co/nvYQCb; https://ibb.co/gSrCsb; https://ibb.co/jtgCsb), when I inspect the data I see 1-10 seconds data in the first window, 299.9-309.9 data in the second window, 599.8-609.8 in the third window (a consistent loss of 289.9 seconds data across windows) and so on. Moreover, the axes are wrong and it's not 'channel activities EEG plot' that I see with EEGLAB and what's shown in the BCILAB tutorial (https://www.youtube.com/watch?v=Sup2SaJyFQE&t=33). Does anyone have any idea why the data doesn't load properly? I tried different paradigms but I cannot train the model because of the lack of event markers in the data training because of data loss. Does anyone have any suggestions as I am very new to BCILAB? I will really appreciate any recommendations. Thanks!</div>
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<div>Software versions used:</div>
<div>MATLAB version 2015a (Mac OS Sierra 10.12.4 but I also observed this on Windows)</div>
<div>BCILAB DEVEL (with EEGLAB 13_4_4b in dependencies) - With BCILAB master, I get supergui problems</div>
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<div>Error obtained when I try to train my data (my event markers are present in the data when I check with EEGLAB but because of data loss, most of the events are absent in the data loaded by BCILAB)</div>
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<div>WARNING: the data set did not contain matching target markers.</div>
<div>WARNING: set_gettarget did not find any target information in this data set. See help of set_gettarget and set_targetmarkers for how data sets can be annotated with target information.</div>
<div><br /></div>
<div>The loaded EEGLAB set is lacking an online expression; assuming it contains unfiltered data.</div>
<div>If it contains filtered data, however, BCI models derived from it will likely not be online-capable.</div>
<div>fold.</div>
<div>WARNING: This data set contains no trials for one of your target classes: please check whether your target marker naming is correct, and ensure that your data has enough target events in each of its required subsets (e.g., in a (nested) cross-validation).</div>
<div>WARNING: After set_selepos your signal is empty.</div>
<div>WARNING: This training set contains only one class - the subsequent learning phase will likely fail.</div>
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<div>Computation failed; error trace:</div>
<div> Your training data set has no trials for one of your classes; you need at least two classes to train a classifier.</div>
<div> The most likely reasons are that one of your target markers does not occur in the data, or that all your trials of a particular class are concentrated in a single short segment of your data (10 or 20 percent). The latter would be a problem with the experiment design.</div>
<div> occurred in:</div>
<div> ml_trainlda: 103</div>
<div> ml_train: 170</div>
<div> ParadigmDataflowSimplified.calibrate_prediction_function: 481</div>
<div> ParadigmDataflowSimplified.calibrate_simple: 602</div>
<div> ParadigmBaseSimplified.calibrate: 184</div>
<div> instantiate_paradigm/@(varargin)instance.calibrate(varargin{:}): 0</div>
<div> @(trainset,varargin)utl_complete_model(args.calibrate_func('collection',{trainset},varargin{:}),args.predict_func): 903</div>
<div> evaluate_internal: 62</div>
<div> cached_evaluate: 42</div>
<div> utl_evaluate_fold: 34</div>
<div> par_beginschedule: 166</div>
<div> par_schedule: 71</div>
<div> utl_crossval: 311</div>
<div> utl_nested_crossval: 141</div>
<div> run_computation: 938</div>
<div> make_func/@(f,a,frame__f1)feval(f,a{:}): 0</div>
<div> hlp_scope: 53</div>
<div> bci_train: 856</div>
<div> pushbutton1_Callback: 156</div>
<div> gui_mainfcn: 95</div>
<div> gui_calibratemodel: 30</div>
<div> @(hObject,eventdata)gui_calibratemodel('pushbutton1_Callback',hObject,eventdata,guidata(hObject)): 0</div>
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<div>Thank you very much!</div>
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