moabb.datasets.MAMEM1

class moabb.datasets.MAMEM1(subjects=None, sessions=None)

Bases: BaseMAMEM

[source]

Dataset Snapshot

MAMEM1

Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs

SSVEP, 5 classes (6.66 vs 7.50 vs 8.57 vs 10.00 vs 12.00)

AuthorsVangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos, Ioannis Kompatsiaris

🇬🇷 Centre for Research and Technology Hellas (CERTH), GR·2016

SSVEP Code: MAMEM1 11 subjects 1 session 256 ch 250 Hz 5 classes 5.0 s trials ODC-By 1.0

Class Labels: 6.66, 7.50, 8.57, 10.00, 12.00

Quickstart Read Paper Compare Similar Report Issue

Overview

SSVEP MAMEM 1 dataset.

Dataset from

EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation,and the EGI 300 Geodesic EEG System, using a stimulation, HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals.

Check the technical report for more detail. From, subjects were exposed to non-overlapping flickering lights from five magenta boxes with frequencies [6.66Hz, 7.5Hz, 8.57Hz 10Hz and 12Hz]. 256 channel EEG recordings were captured.

Each session of the experimental procedure consisted of the following:

1. 100 seconds of rest. 2. An adaptation period in which the subject is exposed to eight 5 second windows of flickering from a magenta box. Each flickering window is of a single isolated frequency, randomly chosen from the above set, specified in the FREQUENCIES1.txt file under 'adaptation'. The individual flickering windows are separated by 5 seconds of rest. 3. 30 seconds of rest. 4. For each of the frequencies from the above set in ascending order, also specified in FREQUENCIES1.txt under 'main trials':

1. Three 5 second windows of flickering at the chosen frequency, separated by 5 seconds of rest. 2. 30 seconds of rest.

This gives a total of 15 flickering windows, or 23 including the adaptation period.

The order of chosen frequencies is the same for each session, although there are small-moderate variations in the actual frequencies of each individual window. The .freq annotations list the different frequencies at a higher level of precision.

Note: Each 'session' in experiment 1 includes an adaptation period, unlike experiment 2 and 3 where each subject undergoes only one adaptation period before their first 'session'.

From:

Eligible signals: The EEG signal is sensitive to external factors that have to do with the environment or the configuration of the acquisition setup The research stuff was responsible for the elimination of trials that were considered faulty. As a result the following sessions were noted and excluded from further analysis: 1. S003, during session 4 the stimulation program crashed 2. S004, during session 2 the stimulation program crashed, and 3. S008, during session 4 the Stim Tracker was detuned. Furthermore, we must also note that subject S001 participated in 3 sessions and subjects S003 and S004 participated in 4 sessions, compared to all other subjects that participated in 5 sessions (NB: in fact, there is only 3 sessions for subjects 1, 3 and 8, and 4 sessions for subject 4 available to download). As a result, the utilized dataset consists of 1104 trials of 5 seconds each.

Flickering frequencies: Usually the refresh rate for an LCD Screen is 60 Hz creating a restriction to the number of frequencies that can be selected. Specifically, only the frequencies that when divided with the refresh rate of the screen result in an integer quotient could be selected. As a result, the frequendies that could be obtained were the following: 30.00. 20.00, 15.00, 1200, 10.00, 857. 7.50 and 6.66 Hz. In addition, it is also important to avoid using frequencies that are multiples of another frequency, for example making the choice to use 10.00Hz prohibits the use of 20.00 and 30.00 Mhz. With the previously described limitations in mind, the selected frequencies for the experiment were: 12.00, 10.00, 8.57, 7.50 and 6.66 Hz.

Stimuli Layout: In an effort to keep the experimental process as simple as possible, we used only one flickering box instead of more common choices, such as 4 or 5 boxes flickering simultaneously The fact that the subject could focus on one stimulus without having the distraction of other flickering sources allowed us to minimize the noise of our signals and verify the appropriateness of our acquisition setup Nevertheless, having concluded the optimal configuration for analyzing the EEG signals, the experiment will be repeated with more concurrent visual stimulus.

Trial duration: The duration of each trial was set to 5 seconds, as this time was considered adequate to allow the occipital part of the bran to mimic the stimulation frequency and still be small enough for making a selection in the context

Show more ▾Open in Overview tab →

Benchmark Context

WithinSession

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

Sample frame: 11 subjects × 1 sessions

• SSVEP all classes 6 pipelinesMax 53.71 · Median 25.77 · Mean 32.59 · Std 15.45

Citation & Impact

• Paper DOI10.48550/arXiv.1602.00904
• CitationsLoading…
• Public APICrossref | OpenAlex
• MOABB tables1 (WithinSession)
• Page Views
  30d: 44 · all-time: 302

  #31 of 151 · Top 21% most viewed

  Updated: 2026-03-20 UTC

Stimulus Protocol

5s task window per trial · 5-class ssvep paradigm · 1 runs/session across 1 sessions

HED Event Tags

HED tags5/5 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus

5

Label

5

Sensory-event

5

Visual-presentation

5

6.66

Sensory-eventExperimental-stimulusVisual-presentationLabel

7.50

Sensory-eventExperimental-stimulusVisual-presentationLabel

8.57

Sensory-eventExperimental-stimulusVisual-presentationLabel

10.00

Sensory-eventExperimental-stimulusVisual-presentationLabel

12.00

Sensory-eventExperimental-stimulusVisual-presentationLabel

HED tree view

Tree · 6.66

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

Tree · 7.50

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

Tree · 8.57

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

Tree · 10.00

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

Tree · 12.00

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

Channel Summary

Total channels256

EEG256

MontageGSN-HydroCel-256

Sampling250 Hz

Notch / line50 Hz

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

Overview

SSVEP MAMEM 1 dataset.

Dataset from [1].

EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation,and the EGI 300 Geodesic EEG System, using a stimulation, HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals.

Check the technical report [2] for more detail. From [1], subjects were exposed to non-overlapping flickering lights from five magenta boxes with frequencies [6.66Hz, 7.5Hz, 8.57Hz 10Hz and 12Hz]. 256 channel EEG recordings were captured.

Each session of the experimental procedure consisted of the following:

1. 100 seconds of rest.

2. An adaptation period in which the subject is exposed to eight 5 second windows of flickering from a magenta box. Each flickering window is of a single isolated frequency, randomly chosen from the above set, specified in the FREQUENCIES1.txt file under ‘adaptation’. The individual flickering windows are separated by 5 seconds of rest.

3. 30 seconds of rest.

4. For each of the frequencies from the above set in ascending order, also specified in FREQUENCIES1.txt under ‘main trials’:

   1. Three 5 second windows of flickering at the chosen frequency,

      separated by 5 seconds of rest.

   2. 30 seconds of rest.

This gives a total of 15 flickering windows, or 23 including the adaptation period.

The order of chosen frequencies is the same for each session, although there are small-moderate variations in the actual frequencies of each individual window. The .freq annotations list the different frequencies at a higher level of precision.

Note: Each ‘session’ in experiment 1 includes an adaptation period, unlike experiment 2 and 3 where each subject undergoes only one adaptation period before their first ‘session’.

From [3]:

Eligible signals: The EEG signal is sensitive to external factors that have to do with the environment or the configuration of the acquisition setup The research stuff was responsible for the elimination of trials that were considered faulty. As a result the following sessions were noted and excluded from further analysis: 1. S003, during session 4 the stimulation program crashed 2. S004, during session 2 the stimulation program crashed, and 3. S008, during session 4 the Stim Tracker was detuned. Furthermore, we must also note that subject S001 participated in 3 sessions and subjects S003 and S004 participated in 4 sessions, compared to all other subjects that participated in 5 sessions (NB: in fact, there is only 3 sessions for subjects 1, 3 and 8, and 4 sessions for subject 4 available to download). As a result, the utilized dataset consists of 1104 trials of 5 seconds each.

Flickering frequencies: Usually the refresh rate for an LCD Screen is 60 Hz creating a restriction to the number of frequencies that can be selected. Specifically, only the frequencies that when divided with the refresh rate of the screen result in an integer quotient could be selected. As a result, the frequendies that could be obtained were the following: 30.00. 20.00, 15.00, 1200, 10.00, 857. 7.50 and 6.66 Hz. In addition, it is also important to avoid using frequencies that are multiples of another frequency, for example making the choice to use 10.00Hz prohibits the use of 20.00 and 30.00 Mhz. With the previously described limitations in mind, the selected frequencies for the experiment were: 12.00, 10.00, 8.57, 7.50 and 6.66 Hz.

Stimuli Layout: In an effort to keep the experimental process as simple as possible, we used only one flickering box instead of more common choices, such as 4 or 5 boxes flickering simultaneously The fact that the subject could focus on one stimulus without having the distraction of other flickering sources allowed us to minimize the noise of our signals and verify the appropriateness of our acquisition setup Nevertheless, having concluded the optimal configuration for analyzing the EEG signals, the experiment will be repeated with more concurrent visual stimulus.

Trial duration: The duration of each trial was set to 5 seconds, as this time was considered adequate to allow the occipital part of the bran to mimic the stimulation frequency and still be small enough for making a selection in the context

References

[1] (1,2)

Oikonomou, V. P., Liaros, G., Georgiadis, K., Chatzilari, E., Adam, K., Nikolopoulos, S., & Kompatsiaris, I. (2016). Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. arXiv preprint arXiv:1602.00904.

[2]

MAMEM Steady State Visually Evoked Potential EEG Database https://archive.physionet.org/physiobank/database/mssvepdb/

[3]

S. Nikolopoulos, 2016, DataAcquisitionDetails.pdf https://figshare.com/articles/dataset/MAMEM_EEG_SSVEP_Dataset_I_256_channels_11_subjects_5_frequencies_/2068677?file=3793738

Code Examples

from moabb.datasets import MAMEM1
dataset = MAMEM1()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	10
#Chan	256
#Classes	5
#Trials / class	12-15
Trials length	3 s
Freq	250 Hz
#Sessions	1

Participants

• Population: healthy

• Clinical population: able-bodied subjects without any known neuro-muscular or mental disorders

• Handedness: {‘right’: 10, ‘left’: 1}

Equipment

• Amplifier: EGI 300 Geodesic EEG System (GES 300)

• Montage: GSN-HydroCel-256

Preprocessing

• Data state: raw

Data Access

• DOI: 10.6084/m9.figshare.2068677.v1

• Data URL: https://dx.doi.org/10.6084/m9.figshare.2068677.v1

• Repository: Figshare

Experimental Protocol

• Paradigm: ssvep

• Feedback: none

• Stimulus: flickering box

__init__(subjects=None, sessions=None)

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)

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])

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)

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