Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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Mobile Brain-Body Imaging (MoBI) technology was deployed at the Museo de Arte Contemporáneo (MARCO) in Monterrey, México, in an effort to collect Electroencefalographic (EEG) data from large numbers (N = ~1200) of participants and allow the study of the brain’s response to artistic stimuli, as part of the studies developed by University of Houston (TX, USA) and Tecnológico de Monterrey (MTY, México).

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Previous neuroimaging research has been traditionally confined to strict laboratory environments due to the limits of technology. Only recently have more studies emerged exploring the use of mobile brain imaging outside the laboratory. This study uses electroencephalography (EEG) and signal processing techniques to provide new opportunities for studying mobile subjects moving outside of the laboratory and in real world settings. The purpose of this study was to document the current viability of using high density EEG for mobile brain imaging both indoors and outdoors.

Instructions: 

Study Summary:

The purpose of this study is to test the reliability of brain and body dynamics recordings in a real world environment, compare electrocortical dynamics and behaviors in outdoor vs. indoor settings, determine behavioral, biomechanical, and EEG correlates to visual search task performance, and search for EEG and behavioral parameters related to increased mental stress. Each subject walked outdoors on a heavily wooded trail and indoors on a treadmill, performing a visual search object recognition task. After a baseline condition without targets, the subject was tasked to identify light green, target flags vs. dark green, nontarget flags. During two conditions, non-stress and stress, the subject received $0.25 for each correct flag identification. During the stress condition, the subject also received a punishment (loss of $1.00) for each incorrect flag identification, plus an automatic punishment (loss of $1.00) approximately every 2 minutes. Each of the 3 conditions lasted approximately 20 minutes. Saliva samples were collected at the start and end of each condition. The order of non-stress and stress conditions was randomized for each subject. Please note there are some events, where the subject was assumed to have perceived a stimulus, that are lacking the Participant/Effect HED tag. This tag allows for automated processing of events. These particular events (e.g. occasional experimenter instructions to walk down a certain part of the outdoor trail) are of low importance for the purposes of data analysis.

 

Data Summary

In accordance with the Terms of Service, this dataset is made available under the terms of the "Creative Commons" Attribution (CC-BY) license. (https://ieee-dataport.org/faq/who-owns-datasets-ieee-dataport).

 

Number of Sessions: 98

Number of Subjects: 49

Subject Groups: normal

Primary source of event information: Tags

Number of EEG Channels: 264 (105 recordings)

Recorded Modalities: EEG (105 recordings), Eye_tracker (88 recordings), Force_plate (47 recordings), IMU (99 recordings), Pulse_from_EEG (52 recordings), Pulse_sensor (86 recordings)

EEG Channel Location Type(s): Custom (105 recordings),

 

Data organization

This study is an EEG Study Schema (ESS) Standard Data Level 1 container. This means that it contains raw, unprocessed EEG data arranged in a standard manner. Data is in a container folder and ready to be used with MATLAB to automate access and processing. All other data measures other than EEG are in .mat (MATLAB) format. For more information please visit eegstudy.org.

 

There is one folder for every subject that includes the following files when available:

(1) Indoor EEG session (<ID number_Indoor.set>)

EEG files have been imported into EEGLAB and are stored as unprocessed raw .set format in standard EEGLAB Data Structures.

(https://sccn.ucsd.edu/wiki/A05:_Data_Structures)

 

(2) Outdoor EEG session (<ID number_Outdoor.set>)

Same as Indoor EEG session (above)

 

(3) Indoor IMU session (<ID number_Indoor_imu.mat>)

The IMU .mat file contains a structure with 6 fields (variable name: IMU)

 

IMU.dataLabel: string including ID number, environment, and sensor type

IMU.dataArray: 10xNx6 matrix. Third dimension refers to each of 6 IMU sensors (left foot, right foot, left ankle, right ankle, chest, and waist). Columns are frame numbers. Rows are: 

• x, y, and z direction of accelerations, in m/s^2 

• x, y, and z direction of gyroscopes, in rad/s 

• x, y, and z direction of magnetometers, in microteslas

• Temperature, in degrees Celsius

IMU.axisLabel: String headings for ‘dataType’ and ‘frame’ and ‘sensorNumber’

IMU.axisValue: 1x10 cell array of string headings for each row of data type, and 1x6 cell array of string headings for each IMU sensor

IMU.samplingRate: Sampling rate

IMU.dateTime: String of date and time information of recording

 

(4) Outdoor IMU session (<ID number_Outdoor_imu.mat>)

Same as Indoor IMU session (above).

 

(5) Indoor eye tracking session (<ID number_Indoor_eye_tracker.mat>)

The eye tracker .mat file contains a structure with 6 fields (variable name: Eye_tracker)

 

Eye_tracker.dataLabel: string including ID number, environment, and sensor type

Eye_tracker.dataArray: 7xN matrix. Columns are frame numbers. Rows are: 

• x and y coordinates of the master spot, in eye image pixels

• x and y coordinates of the pupil center, in eye image pixels

• Pupil radius, in eye image pixels

• Eye direction with respect to the scene image, in scene image pixels

The eye and scene images are displayed and recorded with resolution of 640 x 480 pixels. The origin is the top left of the image with the X-axis positive to the right and the Y-axis positive downwards. Unavailable data is shown by the number –2000.

 

Eye_tracker.axisLabel: String headings for ‘dataType’ and ‘frame’

Eye_tracker.axisValue: 1x7 cell array of string headings for each row of data type

Eye_tracker.samplingRate: Sampling rate

Eye_tracker.dateTime: String of date and time information of recording

 

(6) Outdoor eye tracking session (<ID number_Outdoor_eye_tracker.mat>)

Same as Indoor eye tracking session (above).

 

(7) Indoor heart rate from pulse sensor session (<ID number_Indoor_pulse_sensor.mat>)

The pulse sensor .mat file contains a structure with 6 fields (variable name: Pulse_sensor)

 

Pulse_sensor.dataLabel: string including ID number, environment, and sensor type

Pulse_sensor.dataArray: 3xN matrix. Columns are frame numbers. Rows are: 

• pulse (normalized wave), in volts  

• Inter-beat Interval (IBI), in milliseconds

• heart rate, in beats per minute (BPM)

Pulse_sensor.axisLabel: String headings for ‘dataType’ and ‘frame’

Pulse_sensor.axisValue: 1x3 cell array of string headings for each row of data type

Pulse_sensor.samplingRate: Sampling rate

Pulse_sensor.dateTime: String of date and time information of recording

 

(8) Outdoor heart rate from pulse sensor session 

(<ID number_Outdoor_pulse_sensor.mat>)

Same as Indoor pulse sensor session (above).

 

(9) Indoor heart rate from EEG session (<ID number_Indoor_pulse_from_eeg.mat>)

If pulse rate was recovered from EEG ECG a corresponding file is available. The pulse from EEG .mat file contains a structure with 6 fields (variable name: Pulse_from_EEG)

 

Pulse_from_EEG.dataLabel: string including ID number, environment, and sensor type

Pulse_from_EEG.dataArray: 3xN matrix. Columns are frame numbers. Rows are: 

• pulse (normalized wave), in volts  

• Inter-beat Interval (IBI), in milliseconds

• heart rate, in beats per minute (BPM)

Pulse_from_EEG.axisLabel: String headings for ‘dataType’ and ‘frame’

Pulse_from_EEG.axisValue: 1x3 cell array of string headings for each row of data type

Pulse_from_EEG.samplingRate: Sampling rate

Pulse_from_EEG.dateTime: String of date and time information of recording

 

(10) Outdoor heart rate from EEG session (<ID number_Outdoor_pulse_from_eeg.mat>)

Same as Indoor pulse from EEG session (above).

 

(11) Indoor treadmill force plate session (<ID number_Indoor_force_plate.mat>)

The force plate .mat file contains a structure with 6 fields (variable name: Force_plate)

 

Force_plate.dataLabel: string including ID number, environment, and sensor type

Force_plate.dataArray: 3xNx2 matrix. Third dimension is for left and right force plates, respectively. Columns are frame numbers. Rows are: 

• x, y, and z direction of force, in newtons

Force_plate.axisLabel: String headings for ‘dataType’ and ‘frame’ and ‘sensorNumber’

Force_plate.axisValue: 1x3 cell array of string headings for each row of data type

Force_plate.samplingRate: Sampling rate

Force_plate.dateTime: String of date and time information of recording

 

(12) EEG digitized head map (<ID number.sfp>)

Besa coordinates of all electrode positions.

 

 (13) Indoor eye tracking video (<ID number_Indoor_eye_tracker.avi>)

The eye tracker .avi file is a video from the subject’s perspective (640x480 resolution, 30 frames/sec)

 

(14) Outoor eye tracking video (<ID number_Outdoor_eye_tracker.avi>)

The eye tracker .avi file is a video from the subject’s perspective (640x480 resolution, 30 frames/sec)

 

(15) Indoor video camera (<ID number_Indoor_video_camera(#).avi>)

The camcorder .avi file is a video from the experimenter’s perspective (704x384 resolution, 30 frames/sec). If there are multiple parts the (#) appended indicates the order.

 

(16) Outdoor video camera (<ID number_Outdoor_video_camera(#).avi>)

The camcorder .avi file is a video from the experimenter’s perspective (704x384 resolution, 30 frames/sec). If there are multiple parts the (#) appended indicates the order.

 

Cortisol (Cortisol_all_subjects.xlsx)

Salivary cortisol data is provided as a single spreadsheet ‘Cortisol_all_subjects.xlsx’. It contains the following variables:

 

  • subid: ID number

  • sex: 1 = male, 2 = female

  • age: in years

  • height: in inches

  • weight: in pounds

  • environment: 1 = outdoors, 2 = indoors

  • ordererenvironment: 1 = outdoor first, 2 = indoor first

  • orderstress: 1 = stress first, 2 = non-stress first

  • condition: 1 = Initial sample taken before walking started, 2 = Baseline sample after baseline walking, 3 = Non-stress sample taken after non-stress condition, 4 = Stress sample taken after stress condition

  • concentration: cortisol levels in µg/L

  • cond_ordered = order of conditions by environment

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