moabb.datasets.Shin2017B

class moabb.datasets.Shin2017B(accept=False, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)

Bases: BaseShin2017

[source]

Dataset Snapshot

Shin2017B

Open access dataset for hybrid brain-computer interfaces using EEG and NIRS with motor imagery and mental arithmetic tasks

Motor Imagery, 2 classes (subtraction vs rest)

AuthorsJaeyoung Shin, Alexander von Lühmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han-Jeong Hwang, Klaus-Robert Müller

🇩🇪 Berlin Institute of Technology, DE·2017·h2j@kumoh.ac.kr

Motor Imagery Code: Shin2017B 29 subjects 3 sessions 30 ch 200 Hz 2 classes 10.0 s trials GPL 3.0

Class Labels: subtraction, rest

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Overview

Mental Arithmetic Dataset from Shin et al 2017.

Dataset from

Data Acquisition

EEG and NIRS data was collected in an ordinary bright room. EEG data was recorded by a multichannel BrainAmp EEG amplifier with thirty active electrodes (Brain Products GmbH, Gilching, Germany) with linked mastoids reference at 1000 Hz sampling rate. The EEG amplifier was also used to measure the electrooculogram (EOG), electrocardiogram (ECG) and respiration with a piezo based breathing belt. Thirty EEG electrodes were placed on a custom-made stretchy fabric cap (EASYCAP GmbH, Herrsching am Ammersee, Germany) and placed according to the international 10-5 system (AFp1, AFp2, AFF1h, AFF2h, AFF5h, AFF6h, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, T7, T8, Cz, CCP3h, CCP4h, CCP5h, CCP6h, Pz, P3, P4, P7, P8, PPO1h, PPO2h, POO1, POO2 and Fz for ground electrode).

NIRS data was collected by NIRScout (NIRx GmbH, Berlin, Germany) at 12.5 Hz sampling rate. Each adjacent source-detector pair creates one physiological NIRS channel. Fourteen sources and sixteen detectors resulting in thirty-six physiological channels were placed at frontal (nine channels around Fp1, Fp2, and Fpz), motor (twelve channels around C3 and C4, respectively) and visual areas (three channels around Oz). The inter-optode distance was 30 mm. NIRS optodes were fixed on the same cap as the EEG electrodes. Ambient lights were sufficiently blocked by a firm contact between NIRS optodes and scalp and use of an opaque cap.

EOG was recorded using two vertical (above and below left eye) and two horizontal (outer canthus of each eye) electrodes. ECG was recorded based on Einthoven triangle derivations I and II, and respiration was measured using a respiration belt on the lower chest. EOG, ECG and respiration were sampled at the same sampling rate of the EEG. ECG and respiration data were not analyzed in this study, but are provided along with the other signals.

Experimental Procedure

The subjects sat on a comfortable armchair in front of a 50-inch white screen. The distance between their heads and the screen was 1.6 m. They were asked not to move any part of the body during the data recording. The experiment consisted of three sessions of left and right hand MI (dataset A)and MA and baseline tasks (taking a rest without any thought) (dataset B) each. Each session comprised a 1 min pre-experiment resting period, 20 repetitions of the given task and a 1 min post-experiment resting period. The task started with 2 s of a visual introduction of the task, followed by 10 s of a task period and resting period which was given randomly from 15 to 17 s. At the beginning and end of the task period, a short beep (250 ms) was played. All instructions were displayed on the white screen by a video projector. MI and MA tasks were performed in separate sessions but in alternating order (i.e., sessions 1, 3 and 5 for MI (dataset A) and sessions 2, 4 and 6 for MA (dataset B)). Fig. 2 shows the schematic diagram of the experimental paradigm. Five sorts of motion artifacts induced by eye and head movements (dataset C) were measured. The motion artifacts were recorded after all MI and MA task recordings. The experiment did not include the pre- and post-experiment resting state periods.

Mental Arithmetic (Dataset B)

For the visual instruction of the MA task, an initial subtraction such as 'three-digit number minus one-digit number' (e.g., 384-8) appeared at the center of the screen for 2 s. The subjects were instructed to memorize the numbers while the initial subtraction was displayed on the screen. The initial subtraction disappeared with a short beep sound and a black fixation cross was displayed during the task period in which the subjects were asked to repeatedly perform to subtract the one-digit number from the result of the previous subtraction. For the baseline task, no specific sign but the black fixation cross was displayed on the screen, and the subjects were instructed to take a rest. Note that there were other rest periods between the MA and baseline task periods, as same with the MI paradigm. Both task periods were finished with a short beep sound and a 'STOP' displayed for 1 s on the screen. The fixation cross was displayed again during the rest period. MA and baseline trials were randomized in the same way as MI.

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Citation & Impact

• Paper DOI10.1109/TNSRE.2016.2628057
• CitationsLoading…
• Public APICrossref | OpenAlex
• Page Views
  30d: 23 · all-time: 316

  #28 of 97 · Top 29% most viewed

  Updated: 2026-03-12 UTC

Stimulus Protocol

10s task window per trial · 2-class motor imagery paradigm · 1 runs/session across 3 sessions

HED Event Tags

HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event

2

Agent-action

1

Experimental-stimulus

1

Rest

1

Visual-presentation

1

subtraction

Sensory-eventAgent-action

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

HED tree view

Tree · subtraction

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Think
      └─ Label

Tree · rest

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Channel Summary

Total channels30

EEG30 (active electrodes)

EOG2

Montage10-5

Sampling200 Hz

Referencelinked mastoids

Notch / line50 Hz

This diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.

Overview

Mental Arithmetic Dataset from Shin et al 2017.

Dataset from [1].

Caution

You should accept the licence term [2] to download this dataset, using: Shin2017B(accept=True)

Data Acquisition

EEG and NIRS data was collected in an ordinary bright room. EEG data was recorded by a multichannel BrainAmp EEG amplifier with thirty active electrodes (Brain Products GmbH, Gilching, Germany) with linked mastoids reference at 1000 Hz sampling rate. The EEG amplifier was also used to measure the electrooculogram (EOG), electrocardiogram (ECG) and respiration with a piezo based breathing belt. Thirty EEG electrodes were placed on a custom-made stretchy fabric cap (EASYCAP GmbH, Herrsching am Ammersee, Germany) and placed according to the international 10-5 system (AFp1, AFp2, AFF1h, AFF2h, AFF5h, AFF6h, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, T7, T8, Cz, CCP3h, CCP4h, CCP5h, CCP6h, Pz, P3, P4, P7, P8, PPO1h, PPO2h, POO1, POO2 and Fz for ground electrode).

NIRS data was collected by NIRScout (NIRx GmbH, Berlin, Germany) at 12.5 Hz sampling rate. Each adjacent source-detector pair creates one physiological NIRS channel. Fourteen sources and sixteen detectors resulting in thirty-six physiological channels were placed at frontal (nine channels around Fp1, Fp2, and Fpz), motor (twelve channels around C3 and C4, respectively) and visual areas (three channels around Oz). The inter-optode distance was 30 mm. NIRS optodes were fixed on the same cap as the EEG electrodes. Ambient lights were sufficiently blocked by a firm contact between NIRS optodes and scalp and use of an opaque cap.

EOG was recorded using two vertical (above and below left eye) and two horizontal (outer canthus of each eye) electrodes. ECG was recorded based on Einthoven triangle derivations I and II, and respiration was measured using a respiration belt on the lower chest. EOG, ECG and respiration were sampled at the same sampling rate of the EEG. ECG and respiration data were not analyzed in this study, but are provided along with the other signals.

Experimental Procedure

The subjects sat on a comfortable armchair in front of a 50-inch white screen. The distance between their heads and the screen was 1.6 m. They were asked not to move any part of the body during the data recording. The experiment consisted of three sessions of left and right hand MI (dataset A)and MA and baseline tasks (taking a rest without any thought) (dataset B) each. Each session comprised a 1 min pre-experiment resting period, 20 repetitions of the given task and a 1 min post-experiment resting period. The task started with 2 s of a visual introduction of the task, followed by 10 s of a task period and resting period which was given randomly from 15 to 17 s. At the beginning and end of the task period, a short beep (250 ms) was played. All instructions were displayed on the white screen by a video projector. MI and MA tasks were performed in separate sessions but in alternating order (i.e., sessions 1, 3 and 5 for MI (dataset A) and sessions 2, 4 and 6 for MA (dataset B)). Fig. 2 shows the schematic diagram of the experimental paradigm. Five sorts of motion artifacts induced by eye and head movements (dataset C) were measured. The motion artifacts were recorded after all MI and MA task recordings. The experiment did not include the pre- and post-experiment resting state periods.

Mental Arithmetic (Dataset B)

For the visual instruction of the MA task, an initial subtraction such as ‘three-digit number minus one-digit number’ (e.g., 384-8) appeared at the center of the screen for 2 s. The subjects were instructed to memorize the numbers while the initial subtraction was displayed on the screen. The initial subtraction disappeared with a short beep sound and a black fixation cross was displayed during the task period in which the subjects were asked to repeatedly perform to subtract the one-digit number from the result of the previous subtraction. For the baseline task, no specific sign but the black fixation cross was displayed on the screen, and the subjects were instructed to take a rest. Note that there were other rest periods between the MA and baseline task periods, as same with the MI paradigm. Both task periods were finished with a short beep sound and a ‘STOP’ displayed for 1 s on the screen. The fixation cross was displayed again during the rest period. MA and baseline trials were randomized in the same way as MI.

References

[1]

Shin, J., von Lühmann, A., Blankertz, B., Kim, D.W., Jeong, J., Hwang, H.J. and Müller, K.R., 2017. Open access dataset for EEG+NIRS single-trial classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), pp.1735-1745.

[2]

GNU General Public License, Version 3 https://www.gnu.org/licenses/gpl-3.0.txt

Code Examples

from moabb.datasets import Shin2017B
dataset = Shin2017B()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	29
#Chan	30
#Classes	2
#Trials / class	30
Trials length	10 s
Freq	200 Hz
#Sessions	3
#Runs	1
Total_trials	5220

Participants

• Population: healthy

• Age: 28.5 years

• Handedness: {‘right’: 29, ‘left’: 1}

• BCI experience: naive to MI experiment

Equipment

• Amplifier: BrainAmp

• Electrodes: active electrodes

• Montage: 10-5

• Reference: linked mastoids

Preprocessing

• Data state: preprocessed

• Bandpass filter: 0.5-50 Hz

• Steps: common average reference, bandpass filtering (0.5-50 Hz), ICA-based EOG rejection, downsampling to 200 Hz

• Re-reference: car

Data Access

• DOI: 10.1109/TNSRE.2016.2628057

• Data URL: http://doc.ml.tu-berlin.de/hBCI

• Repository: GitHub

Experimental Protocol

• Paradigm: imagery

• Feedback: none

• Stimulus: visual instruction (subtraction problem and fixation cross)

__init__(accept=False, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)

Initialize function for the BaseDataset.

property all_subjects

Full list of subjects available in this dataset (unfiltered).

convert_to_bids(path=None, subjects=None, overwrite=False, format='EDF', verbose=None)

Convert the dataset to BIDS format.

Saves the raw EEG data in a BIDS-compliant directory structure. Unlike the caching mechanism (see CacheConfig), the files produced here do not contain a processing-pipeline hash (desc-<hash>) in their names, making the output a clean, shareable BIDS dataset.

Parameters:

• path (str | Path | None) – Directory under which the BIDS dataset will be written. If None the default MNE data directory is used (same default as the rest of MOABB).

• subjects (list of int | None) – Subject numbers to convert. If None, all subjects in subject_list are converted.

• overwrite (bool) – If True, existing BIDS files for a subject are removed before saving. Default is False.

• format (str) – The file format for the raw EEG data. Supported values are "EDF" (default), "BrainVision", and "EEGLAB".

• verbose (str | None) – Verbosity level forwarded to MNE/MNE-BIDS.

Returns:

bids_root – Path to the root of the written BIDS dataset.

Return type:

pathlib.Path

Examples

>>> from moabb.datasets import AlexMI
>>> dataset = AlexMI()
>>> bids_root = dataset.convert_to_bids(path='/tmp/bids', subjects=[1])

See also

CacheConfig

Cache configuration for get_data().

moabb.datasets.bids_interface.get_bids_root

Return the BIDS root path.

Notes

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None, accept=False)

Get path to local copy of a subject data.

Parameters:

• subject (int) – Number of subject to use

• path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

• force_update (bool) – Force update of the dataset even if a local copy exists.

• update_path (bool | None Deprecated) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

• verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

Returns:

path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.

Return type:

list of str

download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)

Download all data from the dataset.

This function is only useful to download all the dataset at once.

Parameters:

• subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.

• path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

• force_update (bool) – Force update of the dataset even if a local copy exists.

• update_path (bool | None) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

• accept (bool) – Accept licence term to download the data, if any. Default: False

• verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

get_additional_metadata(subject: str, session: str, run: str) → None | DataFrame

Load additional metadata for a specific subject, session, and run.

This method is intended to be overridden by subclasses to provide additional metadata specific to the dataset. The metadata is typically loaded from an events.tsv file or similar data source.

Parameters:

• subject (str) – The identifier for the subject.

• session (str) – The identifier for the session.

• run (str) – The identifier for the run.

Returns:

A DataFrame containing the additional metadata if available, otherwise None.

Return type:

None | pd.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)

Select data for all provided subjects, blocks and repetitions.

subject -> session -> run -> block -> repetition

See also

BaseDataset.get_data

Parameters:

• subjects (List of int) – List of subject number

• block_list (List of int) – List of block number

• repetition_list (List of int) – List of repetition number inside a block

Returns:

data – dict containing the raw data

Return type:

Dict

get_data(subjects=None, cache_config=None, process_pipeline=None)

Return the data corresponding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {'subject_id' :
            {'session_id':
                {'run_id': run}
            }
        }

subjects are on top, then we have sessions, then runs. A sessions is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contiguous recording. Some dataset break session in multiple runs.

Processing steps can optionally be applied to the data using the *_pipeline arguments. These pipelines are applied in the following order: raw_pipeline -> epochs_pipeline -> array_pipeline. If a *_pipeline argument is None, the step will be skipped. Therefore, the array_pipeline may either receive a mne.io.Raw or a mne.Epochs object as input depending on whether epochs_pipeline is None or not.

Parameters:

• subjects (List of int) – List of subject number

• cache_config (dict | CacheConfig) – Configuration for caching of datasets. See CacheConfig for details.

• process_pipeline (Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.utils.make_process_pipelines(). This pipeline will receive mne.io.BaseRaw objects. The steps names of this pipeline should be elements of StepType. According to their name, the steps should either return a mne.io.BaseRaw, a mne.Epochs, or a numpy.ndarray(). This pipeline must be “fixed” because it will not be trained, i.e. no call to fit will be made.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata: DatasetMetadata | None

Return structured metadata for this dataset.

Returns the DatasetMetadata object from the centralized catalog, or None if metadata is not available for this dataset.

Returns:

The metadata object containing acquisition parameters, participant demographics, experiment details, and documentation. Returns None if no metadata is registered for this dataset.

Return type:

DatasetMetadata | None

Examples

>>> from moabb.datasets import BNCI2014_001
>>> dataset = BNCI2014_001()
>>> dataset.metadata.participants.n_subjects
9
>>> dataset.metadata.acquisition.sampling_rate
250.0