moabb.datasets.Cho2017#

class moabb.datasets.Cho2017(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

Dataset Snapshot

Cho2017

EEG datasets for motor imagery brain-computer interface from 52 subjects with psychological and physiological questionnaire, EMG datasets, 3D EEG electrode locations, and non-task-related states

Motor Imagery, 2 classes (left_hand vs right_hand)

AuthorsHohyun Cho, Minkyu Ahn, Sangtae Ahn, Moonyoung Kwon, Sung Chan Jun

🇰🇷 Gwangju Institute of Science and Technology, KR·2017·scjun@gist.ac.kr

Motor Imagery Code: Cho2017 52 subjects 1 session 68 ch (64 EEG) 512 Hz 2 classes 3.0 s trials CC BY 4.0

Class Labels: left_hand, right_hand

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Overview

Motor Imagery dataset from Cho et al 2017.

Dataset from the paper

Dataset Description

We conducted a BCI experiment for motor imagery movement (MI movement) of the left and right hands with 52 subjects (19 females, mean age ± SD age = 24.8 ± 3.86 years); Each subject took part in the same experiment, and subject ID was denoted and indexed as s1, s2, …, s52. Subjects s20 and s33 were both-handed, and the other 50 subjects were right-handed.

EEG data were collected using 64 Ag/AgCl active electrodes. A 64-channel montage based on the international 10-10 system was used to record the EEG signals with 512 Hz sampling rates. The EEG device used in this experiment was the Biosemi ActiveTwo system. The BCI2000 system 3.0.2 was used to collect EEG data and present instructions (left hand or right hand MI). Furthermore, we recorded EMG as well as EEG simultaneously with the same system and sampling rate to check actual hand movements. Two EMG electrodes were attached to the flexor digitorum profundus and extensor digitorum on each arm.

Subjects were asked to imagine the hand movement depending on the instruction given. Five or six runs were performed during the MI experiment. After each run, we calculated the classification accuracy over one run and gave the subject feedback to increase motivation. Between each run, a maximum 4-minute break was given depending on the subject's demands.

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Benchmark Context

WithinSession

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

Sample frame: 52 subjects × 1 sessions

MI left vs right 19 pipelinesMax 76.23 · Median 71.38 · Mean 68.47 · Std 6.58

Citation & Impact

Paper DOI10.1093/gigascience/gix034
CitationsLoading…
Public APICrossref | OpenAlex
Data DOI10.5524/100295
MOABB tables1 (WithinSession)
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30d: 127 · all-time: 1,808
#3 of 151 · Top 2% most viewed
Updated: 2026-03-20 UTC

Stimulus Protocol

3s task window per trial · 2-class motor imagery paradigm · 1 runs/session across 1 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
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Left
         └─ Hand

Tree · right_hand

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

Channel Summary

Total channels68

EEG64 (active electrodes)

EMG4

Montage10-05

Sampling512 Hz

ReferenceCMS/DRL

Notch / line60 Hz

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

Overview

Motor Imagery dataset from Cho et al 2017.

Dataset from the paper [1].

Dataset Description

We conducted a BCI experiment for motor imagery movement (MI movement) of the left and right hands with 52 subjects (19 females, mean age ± SD age = 24.8 ± 3.86 years); Each subject took part in the same experiment, and subject ID was denoted and indexed as s1, s2, …, s52. Subjects s20 and s33 were both-handed, and the other 50 subjects were right-handed.

EEG data were collected using 64 Ag/AgCl active electrodes. A 64-channel montage based on the international 10-10 system was used to record the EEG signals with 512 Hz sampling rates. The EEG device used in this experiment was the Biosemi ActiveTwo system. The BCI2000 system 3.0.2 was used to collect EEG data and present instructions (left hand or right hand MI). Furthermore, we recorded EMG as well as EEG simultaneously with the same system and sampling rate to check actual hand movements. Two EMG electrodes were attached to the flexor digitorum profundus and extensor digitorum on each arm.

Subjects were asked to imagine the hand movement depending on the instruction given. Five or six runs were performed during the MI experiment. After each run, we calculated the classification accuracy over one run and gave the subject feedback to increase motivation. Between each run, a maximum 4-minute break was given depending on the subject’s demands.

References

[1]

Cho, H., Ahn, M., Ahn, S., Kwon, M. and Jun, S.C., 2017. EEG datasets for motor imagery brain computer interface. GigaScience. https://doi.org/10.1093/gigascience/gix034

Code Examples

from moabb.datasets import Cho2017
dataset = Cho2017()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	52
#Chan	64
#Classes	2
#Trials / class	100
Trials length	3 s
Freq	512 Hz
#Sessions	1
#Runs	1
Total_trials	9800

Participants

Population: healthy
Age: 24.8 years
Handedness: {‘right’: 50, ‘both’: 2}
BCI experience: collected via questionnaire (0 = no, number = how many times)

Equipment

Amplifier: Biosemi ActiveTwo
Electrodes: active electrodes
Montage: standard_1005
Reference: CMS/DRL

Preprocessing

Data state: raw
Notes: Bad trial indices provided separately in .mat files (bad_trial_indices); raw EEG data is unfiltered

Data Access

DOI: 10.5524/100295
Data URL: http://dx.doi.org/10.5524/100295
Repository: GigaDB

Experimental Protocol

Paradigm: imagery
Feedback: none
Stimulus: visual instruction

__init__(subjects=None, sessions=None, *, return_all_modalities=False)[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, generate_figures=False)[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.
generate_figures (bool) – If True, generate interactive neural signature HTML figures in {bids_root}/derivatives/neural_signatures/. Requires plotly (pip install moabb[interactive]). Default is False.

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])

Examples using `moabb.datasets.Cho2017`#

Dataset bubble plot

moabb.datasets.Cho2017#

Examples using moabb.datasets.Cho2017#

Examples using `moabb.datasets.Cho2017`#