EEGdash›NeMAR›NM000118

Iss. 118 · 9 subjects · 9 recordings · See source

Dataset Brief · Nakanishi2015 – SSVEP Nakanishi 2015 dataset

NM000118: eeg dataset, 9 subjects#

Nakanishi2015 – SSVEP Nakanishi 2015 dataset

Access recordings and metadata through EEGDash.

Citation: Masaki Nakanishi, Yijun Wang, Yu-Te Wang, Tzyy-Ping Jung (2019). Nakanishi2015 – SSVEP Nakanishi 2015 dataset. 10.82901/nemar.nm000118

Modality: eeg Subjects: 9 Recordings: 9 License: — Source: nemar

Metadata: Complete (90%)

9-participant EEG dataset — Nakanishi2015 – SSVEP Nakanishi 2015 dataset.

Data & curation Masaki Nakanishi · Yijun Wang · Yu-Te Wang · Tzyy-Ping Jung
Year 2019 · Distributed via NeMAR
Funding Swartz Foundation gift fund · U.S. Office of Naval Research (N00014-08-1215) · + 6 more

EEG · 8 ch256 HzBIDS 1.9.0Task · ssvepHealthyVisualPerception

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000118

dataset = NM000118(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000118(cache_dir="./data", subject="01")

Advanced query

dataset = NM000118(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000118,
  title = {Nakanishi2015 – SSVEP Nakanishi 2015 dataset},
  author = {Masaki Nakanishi and Yijun Wang and Yu-Te Wang and Tzyy-Ping Jung},
  doi = {10.82901/nemar.nm000118},
  url = {https://doi.org/10.82901/nemar.nm000118},
}

§ 02Study · The README

About This Dataset#

SSVEP Nakanishi 2015 dataset.

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

DOI

SSVEP Nakanishi 2015 dataset

9.25

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000118-blue)](https://doi.org/10.82901/nemar.nm000118) # SSVEP Nakanishi 2015 dataset SSVEP Nakanishi 2015 dataset. ## Dataset Overview - Code: Nakanishi2015 - Paradigm: ssvep - DOI: 10.1371/journal.pone.0140703 - Subjects: 9 - Sessions per subject: 1 - Events: 9.25=1, 11.25=2, 13.25=3, 9.75=4, 11.75=5, 13.75=6, 10.25=7, 12.25=8, 14.25=9, 10.75=10, 12.75=11, 14.75=12 - Trial interval: [0.15, 4.3] s - File format: mat - Data preprocessed: True ## Acquisition - Sampling rate: 256.0 Hz - Number of channels: 8 - Channel types: eeg=8 - Channel names: PO7, PO3, POz, PO4, PO8, O1, Oz, O2 - Montage: standard_1020 - Hardware: Biosemi ActiveTwo - Reference: CMS/DRL - Sensor type: EEG - Line frequency: 60.0 Hz ## Participants - Number of subjects: 9 - Health status: healthy - Age: mean=28.0 - Gender distribution: male=9, female=1 - BCI experience: not specified ## Experimental Protocol - Paradigm: ssvep - Number of classes: 12 - Class labels: 9.25, 11.25, 13.25, 9.75, 11.75, 13.75, 10.25, 12.25, 14.25, 10.75, 12.75, 14.75 - Trial duration: 4.0 s - Study design: 12-class SSVEP target identification task with joint frequency and phase coding - Feedback type: none - Stimulus type: flickering - Stimulus modalities: visual - Primary modality: visual - Synchronicity: synchronous - Mode: offline - Training/test split: False - Instructions: Subjects were asked to gaze at one of the visual stimuli indicated by the stimulus program in a random order for 4s. At the beginning of each trial, a red square appeared for 1s at the position of the target stimulus. Subjects were asked to shift their gaze to the target within the same 1s duration. After that, all stimuli started to flicker simultaneously for 4s. - Stimulus presentation: SoftwareName=MATLAB with Psychophysics Toolbox, monitor=ASUS VG278 27-inch LCD, refresh_rate=60Hz, resolution=1280x800 pixels, stimulus_size=6x6 cm each, viewing_distance=60cm, arrangement=4x3 matrix virtual keypad ## HED Event Annotations Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser ```

9.25
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/9_25

11.25
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/11_25

13.25
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/13_25

9.75
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/9_75

11.75
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/11_75

13.75
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/13_75

10.25
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/10_25

12.25
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/12_25

14.25
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/14_25

10.75
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/10_75

12.75
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/12_75

14.75
├─ Sensory-event ├─ Experimental-stimulus ├─ Visual-presentation └─ Label/14_75

``` ## Paradigm-Specific Parameters - Detected paradigm: ssvep - Stimulus frequencies: [9.25, 9.75, 10.25, 10.75, 11.25, 11.75, 12.25, 12.75, 13.25, 13.75, 14.25, 14.75] Hz - Frequency resolution: 0.5 Hz - Code type: joint frequency and phase coding - Number of targets: 12 ## Data Structure - Trials: 180 - Blocks per session: 15 - Trials context: 15 blocks x 12 trials per block = 180 trials total per subject ## Preprocessing - Preprocessing applied: True - Steps: downsampling, bandpass filtering - Bandpass filter: {‘low_cutoff_hz’: 6.0, ‘high_cutoff_hz’: 80.0} - Filter type: IIR - Downsampled to: 256.0 Hz - Epoch window: [0.135, 4.135] - Notes: Zero-phase forward and reverse IIR filtering was implemented using the filtfilt() function in MATLAB. Data epochs were extracted with a 135-ms latency delay considering the visual system delay. ## Signal Processing - Classifiers: CCA, IT-CCA, MwayCCA, L1-MCCA, MsetCCA, CACC, Combination Method - Feature extraction: CCA, canonical correlation - Spatial filters: CCA ## Cross-Validation - Method: leave-one-block-out - Folds: 15 - Evaluation type: cross_validation ## Performance (Original Study) - Accuracy: 92.78% - Itr: 91.68 bits/min - R Square: 0.87 - Combination Method Accuracy 1S: 92.78 - Combination Method Itr 1S: 91.68 - Standard Cca Accuracy 1S: 55.0 - Standard Cca Itr 2S: 50.4 ## BCI Application - Applications: communication - Environment: laboratory - Online feedback: False ## Tags - Pathology: Healthy - Modality: Visual - Type: Research ## Documentation - Description: A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. This study performed a comparison of existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. - DOI: 10.1371/journal.pone.0140703 - License: Unknown - Investigators: Masaki Nakanishi, Yijun Wang, Yu-Te Wang, Tzyy-Ping Jung - Contact: wangyj@semi.ac.cn - Institution: University of California San Diego - Department: Swartz Center for Computational Neuroscience, Institute for Neural Computation; Center for Advanced Neurological Engineering, Institute of Engineering in Medicine - Country: US - Repository: Github - Data URL: mnakanishi/12JFPM_SSVEP - Publication year: 2015 - Funding: Swartz Foundation gift fund; U.S. Office of Naval Research (N00014-08-1215); Army Research Office (W911NF-09-1-0510); Army Research Laboratory (W911NF-10-2-0022); DARPA (USDI D11PC20183); UC Proof of Concept Grant Award (269228); NIH Grant (1R21EY025056-01); Recruitment Program for Young Professionals - Ethics approval: Human Research Protections Program of the University of California San Diego - Keywords: SSVEP, BCI, CCA, canonical correlation analysis, brain-computer interface, steady-state visual evoked potentials ## Abstract Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance. ## Methodology A simulated online BCI experiment was conducted with 10 subjects. Each subject completed 15 blocks, with each block containing 12 trials (one for each of the 12 targets). Visual stimuli were presented as a 4x3 matrix on a 27-inch LCD monitor at 60Hz refresh rate. The 12 targets used joint frequency and phase coding (frequencies: 9.25-14.75Hz with 0.5Hz intervals; phases: 0 to 5.5π with 0.5π intervals). Each trial began with a 1s cue (red square) followed by 4s of flickering stimulation. EEG was recorded from 8 occipital electrodes at 2048Hz and downsampled to 256Hz for analysis. Seven CCA-based methods were compared using leave-one-block-out cross-validation (14 blocks for training, 1 for testing). Performance was evaluated using classification accuracy and ITR. ## References Masaki Nakanishi, Yijun Wang, Yu-Te Wang and Tzyy-Ping Jung, “A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials,” PLoS One, vol.10, no.10, e140703, 2015. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140703 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 — Generated by MOABB 1.4.3 (Mother of All BCI Benchmarks) NeuroTechX/moabb

License: Unknown

Authors:

Masaki Nakanishi
Yijun Wang
Yu-Te Wang
Tzyy-Ping Jung

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000118

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=9, range 28–28 yr, mean 28.0 yr)

25

Other · 9

Channel counts: 8 ch (n=9 recordings)

Sampling frequencies: 256.0 Hz (n=9 recordings)

Total recording duration: 2 h 8 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 8 ch · EEG · 256 Hz · 9 subjects, 9 recordings

Live trace viewer — sub-6 · ses-0 · task-ssvep · run-0

Showing one representative recording out of 9 subjects and 9 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 8 sensors — 8 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000118(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Nakanishi2015

Canonical—

Importable asNM000118 · Nakanishi2015

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Nakanishi2015 – SSVEP Nakanishi 2015 dataset

Study:: nm000118 (NeMAR)
Author (year):: Nakanishi2015
Canonical:: —

Also importable as: NM000118, Nakanishi2015.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 9; recordings: 9; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000118 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000118 DOI: https://doi.org/10.82901/nemar.nm000118

Examples

>>> from eegdash.dataset import NM000118
>>> dataset = NM000118(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/nm000118 · pull with datasets.load_dataset("EEGDash/nm000118").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000118.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000118 to reproduce the tutorial on this dataset.

Citation

Masaki Nakanishi, Yijun Wang, Yu-Te Wang, Tzyy-Ping Jung (2019). Nakanishi2015 – SSVEP Nakanishi 2015 dataset. 10.82901/nemar.nm000118

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000118.

Related & sibling datasets

NM000123EEG · 12 subj NM000176EEG · 5 subj NM000121EEG · 11 subj NM000122EEG · 12 subj NM000126EEG · 34 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

See source · 10.82901/nemar.nm000118

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace · Paper

Dataset ID	`NM000118`
Title	Nakanishi2015 – SSVEP Nakanishi 2015 dataset
Author (year)	`Nakanishi2015`
Canonical	—
Importable as	`NM000118`, `Nakanishi2015`
Year	2019
Authors	Masaki Nakanishi, Yijun Wang, Yu-Te Wang, Tzyy-Ping Jung
License	—
Citation / DOI	10.82901/nemar.nm000118
Source links	OpenNeuro \| NeMAR

NM000118: eeg dataset, 9 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#