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Methods EEG Next The brain is made up of a vast array of neural networks that can be measured with electroencephalography (EEG). From resting-state activity, to the detection of emotional processing and cognitive function, EEG literally provides a window into the brain. The Brain Hygiene Lab uses a high-density EEG system designed and manufactured by Electrical Geodesic, Inc. HRV Next An active brain communicates with the body via the somatic and autonomic nervous systems. The somatic system manages voluntary muscle movement, while the autonomic system regulates internal organs during stress. It comprises the sympathetic and parasympathetic branches, and heart-rate variability measurement indicates their influence on the heart. Questionnaires Next 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. fNIRS Next Brain cells require oxygen and fuel for metabolism, which is supplied by the blood. Near-infrared light passing through the scalp can measure oxygenation levels in the blood and its return to the scalp, offering spatially precise metrics for assessing brain activity and function. Eye Tracking Next The eyes are the window to the mind. In many circumstances, we utilize our vision to focus our attention on an object; therefore, we can learn a lot about what people attend to by tracking their eyes. The Brain Hygiene Lab is equipped with a Tobii Pro Spectrum 150 Eye-tracking system, with Tobii Pro Lab software and Eprime integration. Peripheral Psychophysiology Next Our lab is also equipped with a Physio 16 Package from EGI that integrates with our high-density EEG system from EGI. With this system we can seamlessly collect respiration, ECG, Sp02 and other types of peripheral psychophysiological data.

Methods EEG Next The brain is made up of a vast array of neural networks that can be measured with electroencephalography (EEG). From resting-state activity, to the detection of emotional processing and cognitive function, EEG literally provides a window into the brain. The Brain Hygiene Lab uses a high-density EEG system designed and manufactured by Electrical Geodesic, Inc. HRV Next An active brain communicates with the body via the somatic and autonomic nervous systems. The somatic system manages voluntary muscle movement, while the autonomic system regulates internal organs during stress. It comprises the sympathetic and parasympathetic branches, and heart-rate variability measurement indicates their influence on the heart. Questionnaires Next 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. fNIRS Next Brain cells require oxygen and fuel for metabolism, which is supplied by the blood. Near-infrared light passing through the scalp can measure oxygenation levels in the blood and its return to the scalp, offering spatially precise metrics for assessing brain activity and function. Eye Tracking Next The eyes are the window to the mind. In many circumstances, we utilize our vision to focus our attention on an object; therefore, we can learn a lot about what people attend to by tracking their eyes. The Brain Hygiene Lab is equipped with a Tobii Pro Spectrum 150 Eye-tracking system, with Tobii Pro Lab software and Eprime integration. Peripheral Psychophysiology Next Our lab is also equipped with a Physio 16 Package from EGI that integrates with our high-density EEG system from EGI. With this system we can seamlessly collect respiration, ECG, Sp02 and other types of peripheral psychophysiological data.

< Back fNIRS Brain cells require oxygen and fuel for metabolism, which is supplied by the blood. Near-infrared light passing through the scalp can measure oxygenation levels in the blood and its return to the scalp, offering spatially precise metrics for assessing brain activity and function. 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

< 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

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

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

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

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 Peripheral Psychophysiology Our lab is also equipped with a Physio 16 Package from EGI that integrates with our high-density EEG system from EGI. With this system we can seamlessly collect respiration, ECG, Sp02 and other types of peripheral psychophysiological data. 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