Datasets:
epoch_id large_stringlengths 24 50 | dataset large_stringclasses 3
values | subject_id large_stringclasses 107
values | session_id null | run_id large_stringclasses 532
values | source_file large_stringclasses 532
values | label_code int64 0 2 | label_name large_stringclasses 3
values | n_channels int64 5 5 | n_samples int64 512 512 | channel_names large_stringclasses 1
value | sampling_rate_hz float64 128 128 | epoch_start_sec float64 0 1.77k | epoch_end_sec float64 4 1.78k | filter_version large_stringclasses 1
value | preprocessing_version large_stringclasses 1
value | ingested_at large_stringclasses 532
values | is_rest_synthetic bool 2
classes | features_bytes unknown |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
physionet|S001|S001R04|1|3200|7199 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 1 | right | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 3.2 | 7.199 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|0|11500|15499 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 0 | left | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 11.5 | 15.499 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|0|19800|23799 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 0 | left | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 19.8 | 23.799 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|1|28100|32099 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 1 | right | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 28.1 | 32.099 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|1|36400|40399 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 1 | right | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 36.4 | 40.399 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|0|44700|48699 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 0 | left | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 44.7 | 48.699 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|1|53000|56999 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 1 | right | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 53 | 56.999 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|0|61300|65299 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 0 | left | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 61.3 | 65.299 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|1|69600|73599 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 1 | right | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 69.6 | 73.599 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|0|77900|81899 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 0 | left | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 77.9 | 81.899 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|0|86200|90199 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 0 | left | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 86.2 | 90.199 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|1|94500|98499 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 1 | right | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 94.5 | 98.499 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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physionet|S001|S001R04|0|102800|106799 | physionet | S001 | null | S001R04 | /teamspace/studios/this_studio/projectcerebro/data_cleaned/physionet/S001/S001R04_cleaned_raw.fif | 0 | left | 5 | 512 | FZ,C3,CZ,C4,PZ | 128 | 102.8 | 106.799 | bp_8_30_v1 | v1.1.0 | 2026-05-10T00:27:59.526876+00:00 | false | [
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End of preview. Expand in Data Studio
YAML Metadata Warning:The task_categories "classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
ProjectCerebro — EEG Motor Imagery Dataset
Preprocessed EEG epochs for motor imagery classification (left hand, right hand, rest).
Sources
- PhysioNet EEGMMIDB — 104 subjects x 6 runs (downloaded via MNE)
- BCI Competition IV Dataset 2a — 9 subjects (via MOABB)
Preprocessing Pipeline
- Stage 1 - ICA Cleaning: Broadband filter (1-100 Hz) + average reference + infomax ICA with mne-icalabel (removes muscle, eye, heart artifacts)
- Stage 2 - Epoch Extraction: 8-30 Hz bandpass, 5-channel alignment (FZ, C3, CZ, C4, PZ), 4s epochs at 128 Hz, shape (5, 512)
File
epochs_mi_bp8_30.parquet - Snappy-compressed Parquet
| Column | Description |
|---|---|
| epoch_id | Unique ID |
| dataset | physionet or bci_iv_2a |
| subject_id | e.g. S001, A01 |
| label_code | 0=left, 1=right, 2=rest |
| label_name | left, right, rest |
| features_bytes | numpy .npy bytes, shape (5, 512) float32 |
| sampling_rate_hz | 128.0 Hz |
| filter_version | bp_8_30_v1 |
Usage
import pandas as pd
import numpy as np
import io
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="divyanshmaurya1/projectcerebro-eeg",
filename="epochs_mi_bp8_30.parquet", repo_type="dataset")
df = pd.read_parquet(path)
def load_features(row):
return np.load(io.BytesIO(row["features_bytes"])) # shape: (5, 512)
X = np.stack([load_features(r) for _, r in df.iterrows()])
y = df["label_code"].values
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