DOI: 10.5061/dryad.mgqnk9947
title: Mobile Brain-Body Imaging (MoBI) dual-tasking datasets (response inhibition while walking): Young adults

description: Combining walking with a demanding cognitive task is traditionally
 expected to elicit decrements in gait and/or cognitive task performance.
 However, it was recently shown that, in a cohort of young adults, most
 participants improved performance when walking was added to performance of
 a Go/NoGo response inhibition task. The present study aims to extend these
 previous findings to an older adult cohort, to investigate whether this
 improvement when dual-tasking is observed in healthy older adults. Mobile
 Brain/Body Imaging (MoBI) was used to record electroencephalographic (EEG)
 activity, three-dimensional (3D) gait kinematics and behavioral responses
 in the Go/NoGo task, during sitting or walking on a treadmill, in 34 young
 adults and 37 older adults. Increased response accuracy during walking,
 independent of age, was found to correlate with slower responses to
 stimuli (r = 0.44) and with walking-related EEG amplitude modulations over
 frontocentral regions (r = 0.47) during the sensory gating (N1) and
 conflict monitoring (N2) stages of inhibition, and over left-lateralized
 prefrontal regions (r = 0.47) during the stage of inhibitory control
 implementation (P3). These neural activity changes are related to the
 cognitive component of inhibition, and they were interpreted as signatures
 of behavioral improvement during walking. On the other hand, aging,
 independent of response accuracy during walking, was found to correlate
 with slower treadmill walking speeds (r = -0.68) and attenuation in
 walking-related EEG amplitude modulations over left-dominant frontal
 (r = -0.44) and parietooccipital regions (r = 0.48) during the N2 stage,
 and over centroparietal regions (r = 0.48) during the P3 stage. These
 neural activity changes are related to the motor component of inhibition,
 and they were interpreted as signatures of aging. Older adults whose
 response accuracy ‘paradoxically’ improved during walking manifested
 neural signatures of both behavioral improvement and aging, suggesting
 that their flexibility in reallocating neural resources while walking
 might be maintained for the cognitive but not for the motor inhibitory
 component. These distinct neural signatures of aging and behavior can
 potentially be used to identify ‘super-agers’, or individuals at risk for
 cognitive decline due to aging or neurodegenerative disease.
description: This dataset was collected using the Mobile Brain-Body Imaging
 modality, involving synchronous recordings of 3 data streams:
 1) EEG (BioSemi Inc., Amsterdam, The Netherlands)
 2) Behavioral responses to the designed Go/NoGo task
 (Presentation, Neurobehavioral Systems Inc., Berkeley, CA, USA)
 3) Full-body kinematics (OptiTrack, NaturalPoint, Inc.,
 Corvallis, OR, USA). To record these 3 data streams in
 a time-synchronized manner, the Lab Streaming Layer (LSL: https://labstreaminglayer.org/#/) was used.  The data included are raw, except for the behavior-related logfiles, for which both raw and processed versions are provided (see README file for details).
description: The .mat files in the "LSLData" subfolders require
 MATLAB (MathWorks Inc., Natick, MA, USA) to open. An open-source
 alternative to open them is using Python (see https://www.askpython.com/python/examples/mat-files-in-python)  The .set and .fdt files in the "EEGstruct_Raw" subfolders require EEGLAB, which is an open-source MATLAB toolbox for electrophysiological signal processing and analysis (https://sccn.ucsd.edu/eeglab/index.php). Alternatively, they can be opened using MNE, which is an open-source MATLAB toolbox for electrophysiological signal processing and analysis (https://mne.tools/dev/generated/mne.io.read_raw_eeglab.html). The .txt and .log files in the "Logiles_Raw" and "Logfiles_Processed" subfolders can be opened using any text editor.  The metadata.xlsx file can be opened using Microsoft Excel. Alternatively, LibreOffice (https://www.libreoffice.org/), which is free and open-source, can be used. 
description: # Title of Dataset Mobile Brain-Body Imaging (MoBI) dual-tasking datasets
 (response inhibition while walking): Young adults ## Description of the
 data and file structure This Drayd dataset contains multimodal MoBI data,
 collected from young adults while performing the 1-back Go/NoGo response
 inhibition task and concurrently walking on a treadmill. The data is
 organized as follows: ``` |-- 010705001 | |-- LSLData | | |--
 010705001.mat | |-- Logfiles_Raw | | |-- GoNoGo_010705001.txt | | |--
 mainExperScript_010705001.log | | |-- motion_state_010705001.txt | | |--
 Training_GoNoGo_010705001.txt | |-- Logfiles_Processed | | |--
 GoNoGo_010705001_processed.txt | | |--
 mainExperScript_010705001_processed.txt | |-- EEGstruct_Raw | | |--
 010705001.set | | |-- 010705001.fdt |-- 010705002 | |-- LSLData | | |--
 010705002.mat | |-- Logfiles_Raw | | |-- GoNoGo_010705002.txt | | |--
 mainExperScript_010705002.log | | |-- motion_state_010705002.txt | | |--
 Training_GoNoGo_010705002.txt | |-- Logfiles_Processed | | |--
 GoNoGo_010705002_processed.txt | | |--
 mainExperScript_010705002_processed.txt | |-- EEGstruct_Raw | | |--
 010705002.set | | |-- 010705002.fdt |-- ... |-- ... |-- ... |--
 metadata.xlsx ``` ### Notes: #### About the LSLData folder: The `.mat`
 file in this folder contains a cell array with the 3 synchronized
 datastreams (EEG, motion capture, behavioral responses) along with
 metadata. Each cell of the array contains a different datastream. In case
 the Presentation scenario had to be terminated before its completion (e.g.
 the participant wanted to take a restroom break), then a new scenario was
 launched after the break to complete the required number of task blocks.
 In those cases, two separate `.mat` files occurred: one containing the
 recording before the break (e.g. `010705013_1.mat`) and another containing
 the recording after the break (eg. `010705013_2.mat`). #### About the
 Logfiles\_Raw folder: * The `GoNoGo_{participantID}.txt` is a manually
 created logfile containing the following information about the images
 presented during the Go/NoGo task (each row corresponds to one image): *
 Column **Block**: contains the block number during which the image was
 presented * Column **Trial**: contains the trial number during which the
 imagee was presented * Column **Image**: contains the IAPS code of the
 presented image * Column **RespTime**: contains the response time to the
 presented image * Column **MotState**: contains `sitting` if the block was
 a sitting block, and `walking` if the block was a walking block * Column
 **Button**: contains `1` if a valid button press was recorded in response
 to the image, and `0` if no valid button press was recorded. * The
 `Training_GoNoGo_{participantID}.txt` is a manually created logfile
 containing the same information as the `GoNoGo_{participantID}.txt`, but
 only for the training block. Note that this data was not analyzed the in
 the paper--it only serves to assess how well the participant understands
 the task, before they start with the actual experiment. * The
 `motion_state_{participantID}.txt` is a manually created logfile
 containing the order in which sitting and walking blocks were performed.
 IMPORTANT: this is the final/correct sequence of walking/sitting -- if the
 walking/sitting sequence in the `GoNoGo_{participantID}.txt` is different,
 then it must change to align with this one. The reason why those two
 sequences are different for some participants between the two logfiles is
 because the walking/sitting sequence that had initially been planned for
 them (`GoNoGo_{participantID}.txt`) had to change on the fly, for example
 because they were tired and requested to do more sitting and leave walking
 for later. * The `mainExperScript_{participantID}.log` is an logfile
 automatically generated by Presentation after the completion of each
 experimental scenario run. The information is organized in the following
 columns: * Column **Trial**: incremental trial number * Column **Event
 Type**: it can take one the following values * `Picture`: this is the most
 common event. The `Picture` event is on throughout runtime of the
 experimental scenario (even when a the black screen with the white
 centered cross is dispayed on the projection screen--that is a picture
 too) * `Response`: button press from the Nintendo switch * `Text Input`:
 it occurs at the end of some experimental blocks. The experimenter has
 coded the scenario to pause and wait until text input from is provided *
 `Pause`: it occurs at the end of some experimental blocks. It indicates
 that the scenario has been paused manually. * `Resume`: it almost always
 occurs after pause events, at the end of some experimental blocks. It
 indicates that the scenario has been resumed manually. * `Quit`: the
 scenario has been ternimated manually before its completion * Column
 **Code**: * For the **Picture** event type, it can take one of the
 following values: * `countdown_3`: Part of the countdown at the beginning
 of each experimental block. Displays a white '3' with a black
 background on the projection screen in front of the participant. *
 `countdown_2`: Part of the countdown at the beginning of each experimental
 block. Displays a white '2' with a black background on the
 projection screen in front of the participant. * `countdown_1`: Part of
 the countdown at the beginning of each experimental block. Displays a
 white '1' with a black background on the projection screen in
 front of the participant. * `countdown_go`: Part of the countdown at the
 beginning of each experimental block. Displays a white 'Go' with
 a black background on the projection screen in front of the participant. *
 `pic_display`: Displays an IAPS image on the projection screen in front of
 the participant. * `fixation_cross_no_resp`: Displays a white
 '+' with a black background on the projection screen in front of
 the participant. No button presses are accepted during this event code,
 since they are considered as delayed responses to the previous trial. *
 `fixation_cross_resp`: Displays a white '+' with a black
 background on the projection screen in front of the participant. Button
 presses are accepted during this event code. * For the **Response** event
 type, it can take either of the following values: * `1`: For button
 presses provided by the participant during task performance. * `2`: For
 keyboard presses provided by the experimenter at the end of each block, to
 enable continuing to the next block. * For all the rest of the event types
 (**Text Input**, **Pause**, **Resume**, **Quit**), the event code value is
 empty. * Column **Time**: time of occurrence of each event relative to the
 start of the scenario. * Column **TTime**: time of occurrence of each
 event relative to the start of the trial the event is in. * Column
 **Uncertainty** (Time): temporal uncertainty for each event. For details,
 see
 [here](https://www.neurobs.com/pres_docs/html/03_presentation/09_timing/01_uncertainties.htm) * Column **Duration**: For picture stimuli, this is the duration of the picture presentation. For pause events, this is the duration of the pause. Presentation does not monitor the durations of other events. * Column **Uncertainty** (Duration): uncertainty in the duration of a picture stimulus. For details, see [here](https://www.neurobs.com/pres_docs/html/03_presentation/09_timing/01_uncertainties.htm) * Column **ReqTime**: Requested time of presentation given in the scenario file. Note that actual presentation times for picture stimuli are constrained by the monitor refresh and therefore should differ from requested times. * Column **ReqDur**: For picture stimuli, this is the requested duration of presentation given in the scenario file. Note that picture stimuli durations are constrained by the monitor refresh. * Column **Stim Type**: Its value is `other`, except for pictures with code `fixation_cross_resp` during which button presses are accepted. For these picture events, the value is either `hit` (button press was detected) or miss (no button press was detected). All times written in the logfile are in tenths of milliseconds (0.1 milliseconds resolution). The uncertainties provide the upper limit so that an uncertainty of 0.2 milliseconds means the uncertainty is between 0.1 and 0.2 milliseconds. To view the logfile data properly aligned with respect to the columns defined above, it is suggested to use the following command in MATLAB: ```matlab S = importdata({full_path_to_logfile},'\t') ``` where S is a structure, and the field S.textdata is a cell array containing the aligned data. For more details about its structure, check the [Presentation documentation](https://www.neurobs.com/pres_docs/html/03_presentation/07_data_reporting/01_logfiles/03_event_table.htm). The event code of every image is the same, i.e. `pic_display`, which functions as a placeholder. To obtain behaviorally meaningful information, i.e. whether a specific trial was a correct or incorrect Go or NoGo, we need to know which exact IAPS image code each `pic_display` event corresponds to. To this end, information from the `mainExperScript_{participantID}.log` has to be fused with information from the `GoNoGo_{participantID}.txt` (after ensuring that the walking/sitting sequence of the latter is corrected according to the `motion_state_{participantID}.txt`). In case the Presentation scenario had to be terminated before its completion (e.g. the participant wanted to take a restroom break), then a new scenario was launched after the break to complete the required number of task blocks. As such, two separate sets of logfiles occurred, for all logfiles described above. Any logfile recorded as part of the first session, before the break, is denoted by an additional `_1` at the end of the logfile name, for example: `mainExperScript_010705013_1.log`, `GoNoGo_010705013_1.txt`, `motion_state_010705013_1.txt` and `Training_GoNoGo_010705013_1.txt`. Any logfile recorded as part of the second session, after the break, is denoted by an additional `_2` at the end of the file name, for example: `mainExperScript_010705013_2.log`, `GoNoGo_010705013_2.txt`, `motion_state_010705013_2.txt` and `Training_GoNoGo_010705013_2.txt`. In some cases where running the training block was not necessary (e.g. for the second recording session after the break, or the participant had already completed the training block shortly before the start of the experiment), the logfile name contains an additional `_noTraining` string, and no manually created training logfile was generated in this case, for example: `mainExperScript_noTraining_010705024.log` and `GoNoGo_noTraining_010705024.txt`. #### About the Logfiles\_Processed folder: * The `GoNoGo_{participantID}_processed.txt` is the same as the `GoNoGo_{participantID}.txt`, with the difference that it has 2 additional columns: * Column **EmoState**: contains the emotional valence (`positive/neutral/negative`) of each presented image. The classification into the 3 categories was conducted based on [Grühn & Scheibe, 2008](https://rdcu.be/dymZf). * Column **ZeroClusters**: contains `1` for all trials except those which belong to a cluster of 6 consecutive non-responses; those latter trials are assigned the value `0` in this column * The `mainExperScript_{participantID}_processed.txt` is the same as the `mainExperScript_{participantID}.log`, with the difference that every placeholder `pic_display` event code has been replaced with an appropriate string of the following structure: `StimOnset_MotState_EmoState_DistPrevNoGo_{distanceNum}_ButtonResp_{Button}_ZeroCluster_{ZeroClusters}_RT_{RespTime}_BlockNum_{Block}`. The `DistPrevNoGo` is followed by a number that indicates how many trials before the current one the last NoGo trial happened (**distanceNum**). #### About the EEGstruct\_Raw folder: Contains `.set` and `.fdt` files, which are formats used by EEGLAB. [EEGLAB](https://sccn.ucsd.edu/eeglab/index.php) is an open-source MATLAB toolbox for electrophysiological signal processing and analysis. Here is an example of loading an EEG dataset, using the pop_loadset function provided by EEGLAB: ```matlab EEGstruct = pop_loadset('010705001.set') ``` `.set` files contain the metadata and `.fdt` files contain the raw data. Alternatively, if the user prefers to work with `.mat` files only, they can load each EEG structure only once using pop_loadset, and then save it as a `.mat` file as follows: ```matlab save('010705001.mat','EEGstruct','-v7.3') ``` Each of these folders essentially contains a structure, the fields of which have been populated with EEG and behavioral data. Specifically, the field **data** contains a (channels)x(time points) matrix with the raw EEG data; the field **event** contains a structure where field **type** contains the event names (e.g. `sitting_hit_negative`, `walking_corrRej_positive`) and field **latency** contains the EEG time point at which the event occured. #### About the metadata.xlsx: This Excel file contains metadata about the whole dataset, organized into 2 sheets, the `Young Adults` sheet and the `Older adults` sheet. Each sheet contains metadata for the respective age group indicated by the sheet name. The first 5 columns are common across the 2 sheets: * Column **ID**: The 9-digit participant ID which is also the name of the individual participant data folders, e.g. `010705001`. The last 3 digits represent an incrementally-assigned number from 1-102. * Column **Age**: Participant's age at the time of the recording * Column **Speed**: treadmill speed, in miles per hour * Column **Sex**: `F` for female, `M` for male * Column **Dominant hand** (coincides with the hand used to provide button-press responses): `R` for right hand, `L` for left hand The `Older adults` sheet includes one additional, 6th column **MoCA score** containing the MoCA scores for each of the older participants. ## Sharing/Access information Data for this project will only be shared via Dryad. Data was not derived from any other sources. ## Code/Software The code will be provided [here](https://github.com/CNL-R). ## Associated Datasets In the context of this study, data were also collected from older adults while performing the 1-back Go/NoGo response inhibition task and concurrently walking on a treadmill. These older adult data can be found in the Dryad dataset titled **[Mobile Brain-Body Imaging (MoBI) dual-tasking datasets (response inhibition while walking): Older adults](https://doi.org/10.5061/dryad.xsj3tx9nb)**.