moabb.datasets.Shin2017A#

class moabb.datasets.Shin2017A(accept=False, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)[source]#

Bases: BaseShin2017

[source]

Dataset Snapshot

Shin2017A

Open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). Dataset includes two BCI experiments: left versus right hand motor imagery, and mental arithmetic versus resting state.

Motor Imagery, 2 classes (left_hand vs right_hand)

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: Shin2017A 29 subjects 3 sessions 30 ch 200 Hz 2 classes 10.0 s trials GPL 3.0

Class Labels: left_hand, right_hand

Read Paper Compare Similar Report Issue

Overview

Motor Imagey 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.

Motor Imagery (Dataset A)

For motor imagery, subjects were instructed to perform haptic motor imagery (i.e. to imagine the feeling of opening and closing their hands as they were grabbing a ball) to ensure that actual motor imagery, not visual imagery, was performed. All subjects were naive to the MI experiment. For the visual instruction, a black arrow pointing to either the left or right side appeared at the center of the screen for 2 s. The arrow disappeared with a short beep sound and then a black fixation cross was displayed during the task period. The subjects were asked to imagine hand gripping (opening and closing their hands) in a 1 Hz pace. This pace was shown to and repeated by the subjects by performing real hand gripping before the experiment. Motor imagery was performed continuously over the task period. The task period was finished with a short beep sound and a 'STOP' displayed for 1s on the screen. The fixation cross was displayed again during the rest period and the subjects were asked to gaze at it to minimize their eye movements. This process was repeated twenty times in a single session (ten trials per condition in a single session; thirty trials in the whole sessions). In a single session, motor imagery tasks were performed on the basis of ten subsequent blocks randomly consisting of one of two conditions: Either first left and then right hand motor imagery or vice versa.

Open in Overview tab →

Benchmark Context

WithinSession

Included in 1 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 29 subjects × 3 sessions

MI left vs right 19 pipelinesMax 72.30 · Median 65.63 · Mean 63.44 · Std 7.24

Citation & Impact

Paper DOI10.1109/TNSRE.2016.2628057
CitationsLoading…
Public APICrossref | OpenAlex
MOABB tables1 (WithinSession)
Page Views
30d: 45 · all-time: 548
#16 of 97 · Top 17% 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.

Agent-action

2

Sensory-event

2

left_hand

Sensory-eventAgent-action

right_hand

Sensory-eventAgent-action

HED tree view

Tree · left_hand

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Leftward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Left
         └─ Hand

Tree · right_hand

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Rightward
│     └─ Arrow
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand

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

Motor Imagey Dataset from Shin et al 2017.

Dataset from [1].

Caution

You should accept the licence term [2] to download this dataset, using: Shin2017A(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.

Motor Imagery (Dataset A)

For motor imagery, subjects were instructed to perform haptic motor imagery (i.e. to imagine the feeling of opening and closing their hands as they were grabbing a ball) to ensure that actual motor imagery, not visual imagery, was performed. All subjects were naive to the MI experiment. For the visual instruction, a black arrow pointing to either the left or right side appeared at the center of the screen for 2 s. The arrow disappeared with a short beep sound and then a black fixation cross was displayed during the task period. The subjects were asked to imagine hand gripping (opening and closing their hands) in a 1 Hz pace. This pace was shown to and repeated by the subjects by performing real hand gripping before the experiment. Motor imagery was performed continuously over the task period. The task period was finished with a short beep sound and a ‘STOP’ displayed for 1s on the screen. The fixation cross was displayed again during the rest period and the subjects were asked to gaze at it to minimize their eye movements. This process was repeated twenty times in a single session (ten trials per condition in a single session; thirty trials in the whole sessions). In a single session, motor imagery tasks were performed on the basis of ten subsequent blocks randomly consisting of one of two conditions: Either first left and then right hand motor imagery or vice versa.

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 Shin2017A
dataset = Shin2017A()
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 arrow and fixation cross

__init__(accept=False, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)[source]#: 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)[source]#

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])