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In order to analyze our data as accurately as possible, it is important that we filter out the extreme high and low frequency waves in our data, as these recordings are most likely due to the environment and other artifacts. Typically, frequencies less than 0.01 Hz - 0.1 Hz and greater than 40 Hz - 50 Hz are filtered out. BESA automatically turns on low and high cutoff filters that can be edited if desired. Keep in mind that filters distort data, and filtering too much can alter waveforms and wave amplitudes significantly. Filtering tutorial day 1 of the BESA workshop: 52:10 - 1:06:17

Affected data segments may be rejected (deleted) or replaced (interpolated) with estimations based on nearby non-artifact contaminated data. Interpolation should be less than 5% of the total channels (so interpolate 3-4 channels max if you have 64 channels total) This is due to the data warping interpolation does to nearby data. Deleting Channels We may want to delete bad channels that contain too many artifacts (noise, movement, blinking, etc.). Right click on the desired channel and click “Define this channel as bad”. The channel will automatically delete. This process can be reversed by clicking “Define this channel as bad” again. Interpolating Channels Interpolating creates a new, computer generated version of a bad channel using the data from the channels directly above and below it. This is not actual data and is merely an approximation. The channel label will turn pink when this is done.

BESA is capable of automatically recognizing the electrode coordinates and their labels for certain EEG caps. Channels that have been successfully identified by BESA have 10-10 names (E.g. Fp1, C1, P3) and are called Scalp Channels. Channels that have not been successfully identified by BESA have different names (E.g. EXG1, EA7, EB28) and are called Polygraphic Channels. In order to match up polygraphic channels with their locations, additional files containing EEG cap information must be imported into BESA. Dr. Thom has several of these files stored in the computer. If none of the channels are polygraphic, you do not need to manually set channel locations. Click on “Load Channel Configuration…” under the file tab This warning will pop up. Click “Yes” All the files on the computer will pop up. Go to… Windows (C:)→Users→Public→Public Documents→BESA→Research_7_0→Montages→Channels In the channels folder, there are folders of four different companies and the type of EEG caps they use. If you don’t know the type of cap used in the experiment, try counting the number of channels and finding a file in the StandardElectrodes that has around the same number of channels. In the example above, I was able to find a file (81 electrodes) within the StandardElectordes folder that allowed BESA to recognize and locate each of the electrodes. Here is a before and after setting the channel locations. Notice how the polygraphic channels are all black, with little readable data. Also, if you need to upload files into BESA in order to add additional information about channel configuration (*.ela), digitized head surface points (*.sfp, *.eps), labels (*.sfn), etc., watch the tutorial in day 1 of the BESA workshop: 22:02 - 23:00

EEG Pipeline BESA - Updated 2023 1 Collect Data There are hundreds of sites with free, downloadable EEG data that can be imported into BESA. Next 2 Import Data After choosing a dataset, isolate the raw EEG data from the metadata and other contextual information about the experiment. Next 3 Re-reference Data You need to convert the data to an average reference. Next 4 Set Channel Locations Select the files that specifies the channel locations. Next 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. Next 6 Filtering We use filters to take out signals that are definitley not brain data. You need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz), as well as the 60 Hz line noise from the ambient electrical wires. Next 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. Next 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. Next 9 Baseline Removal The signal recorded just before the stimulus is considered the baseline. This is comprised of neural activity that is not in response to the stimulus as well as noise. This baseline is subtracted from the entire epoch, leaving the neural activity that occured in response to the stimulus. Next 10 Component Rejection Once you've run ICA on a dataset, it is possible to reject non-neural signal sources. This section explains how to do that and explains the Sasica plug in. Next 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. Next 12 Averaging Trials Separating the data for each stimulus type into its own file. Next 13 Check the Data Head Plots Plot the head maps for the time ranges of interest ​ Download Waveforms Plot the waveforms of the averaged data ​ Download Grand Averaging Averaging all of the data of one type together (such as all face data / scene data) Download 14 Analyze the Data Average Amplitudes Wavelet Analysis Fourier Transforms Phase Coherence Download Resources Filename Key Download Automation Scripts Download EEGLAB Scripts Download Other Download

EEG Pipeline BESA - Updated 2023 1 Collect Data There are hundreds of sites with free, downloadable EEG data that can be imported into BESA. Next 2 Import Data After choosing a dataset, isolate the raw EEG data from the metadata and other contextual information about the experiment. Next 3 Re-reference Data You need to convert the data to an average reference. Next 4 Set Channel Locations Select the files that specifies the channel locations. Next 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. Next 6 Filtering We use filters to take out signals that are definitley not brain data. You need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz), as well as the 60 Hz line noise from the ambient electrical wires. Next 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. Next 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. Next 9 Baseline Removal The signal recorded just before the stimulus is considered the baseline. This is comprised of neural activity that is not in response to the stimulus as well as noise. This baseline is subtracted from the entire epoch, leaving the neural activity that occured in response to the stimulus. Next 10 Component Rejection Once you've run ICA on a dataset, it is possible to reject non-neural signal sources. This section explains how to do that and explains the Sasica plug in. Next 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. Next 12 Averaging Trials Separating the data for each stimulus type into its own file. Next 13 Check the Data Head Plots Plot the head maps for the time ranges of interest ​ Download Waveforms Plot the waveforms of the averaged data ​ Download Grand Averaging Averaging all of the data of one type together (such as all face data / scene data) Download 14 Analyze the Data Average Amplitudes Wavelet Analysis Fourier Transforms Phase Coherence Download Resources Filename Key Download Automation Scripts Download EEGLAB Scripts Download Other Download

< Back Questionnaires Surveys and questionnaires collect qualitative information that provides a detailed summary of your experiences. Researchers use these surveys to get to know you, to interpret your EEG and HRV data, and to develop a comprehensive, personalized Behavioral Health Report. The Life Events Checklist (LEC-5) is a standardized questionnaire that gathers information about traumatic life experiences. The LEC-5 provides an insight into one's life experiences and gives context to HRV and EEG data. LEC-5 The Pittsburgh Sleep Quality Index (PSQI) is used to determine the quality of your sleep in the last month. It is no secret that poor sleep is related to stress. The PSQI provides valuable information to our research team. PSQI The Patient Health Questionnaire-9 (PHQ-9) is used to determine the Major Depression Disorder (MDD) symptoms. Depression symptoms can help explain interoceptive data. PHQ-9 The Profile of Mood States (POMS) questionnaire takes record of the participant's mood over the past 7 days. It looks at mood states like anger, fear, depression, etc. POMS The Mini Screen is a survey that screens for psychiatric disorders. This includes eating disorders, depression, obsessive compulsive disorder, PTSD, and other. Mini Screen The Mini Screen Manual explains the results of the Mini Screen. Mini Screen The Generalized Anxiety Disorder-7 (GAD-7) questionnaire is used to determine the Anxiety symptoms. Anxiety is linked to other psychological disorders like PTSD. Anxiety symptoms can help explain interoceptive data. GAD-7 The Connor-Davidson Resilience Scale (CD-RISC) measures resilience. This short survey attempts to gauge how well one responds to stress and adversity. CD-RISC