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Tài liệu slide tiếng Anh về NGN
Pre-processing for EEG and MEG Przemek Tomalski & Kathrin Cohen Kadosh Recording EEG QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. Two crucial steps Activity caused by your stimulus (ERP) is ‘hidden’ within continuous EEG stream ERP is your ‘signal’, all else in EEG is ‘noise’ Event-related activity should not be random, we assume all else is Epoching – cutting the data into chunks referenced to stimulus presentation Averaging – calculating the mean value for each time-point across all epochs Extracting ERP from EEG ERPs emerge from EEG as you average trials together Overview Pre-processing Converting the data Epoching/Segmentation Filtering Artifact Detection/Rejection Averaging Re-referencing Convert the data [...]... needed to see this picture Filtering in SPM Overview Pre-processing Converting the data Epoching/Segmentation Filtering Artifact Detection/Rejection Averaging Re-referencing Artifacts in EEG signal Blinks Eye-movements Muscle activity EKG Skin potentials Alpha waves Eye blinks QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture Eye movements QuickTime™ and... the square root of the number of trials As a general rule, it’s always better to try to decrease sources of noise than to increase the number of trials Averaging Averaging Assumes that only the EEG noise varies from trial to trial But – amplitude and latency will vary Variable latency is usually a bigger problem than variable amplitude Averaging: effects of variance Latency variation can be a . Pre-processing for EEG and MEG Przemek Tomalski & Kathrin Cohen Kadosh Recording EEG QuickTime™ and a TIFF (Uncompressed) decompressor are. by your stimulus (ERP) is ‘hidden’ within continuous EEG stream ERP is your ‘signal’, all else in EEG is ‘noise’ Event-related activity should not be