LibriBrain

LibriBrain#

The LibriBrain 2025 datasets provide MEG-based tasks with convenient download and caching from Hugging Face.

Common Arguments#

  • data_path: local root where files are stored / downloaded

  • preprocessing_str: expected preprocessing string in filenames

  • tmin, tmax: window relative to event (seconds)

  • standardize: z-score channels using per-run stats

  • include_run_keys: list of run keys to include (see constants.RUN_KEYS)

  • include_info: include an info dict in each sample

  • download: if True (default), fetch missing files via Hugging Face

Speech (binary time series)#

from pnpl.datasets import LibriBrainSpeech
from pnpl.datasets.libribrain2025 import constants

ds = LibriBrainSpeech(
    data_path="./data/LibriBrain",
    preprocessing_str="bads+headpos+sss+notch+bp+ds",
    include_run_keys=[constants.RUN_KEYS[0]],
    tmin=0.0,
    tmax=0.2,
    include_info=True,
)

print(len(ds))

Each item returns (data: float32[channels,time], labels: int[time], info: dict).

Phoneme (classification)#

from pnpl.datasets import LibriBrainPhoneme
from pnpl.datasets.libribrain2025 import constants

ds = LibriBrainPhoneme(
    data_path="./data/LibriBrain",
    preprocessing_str="bads+headpos+sss+notch+bp+ds",
    include_run_keys=[constants.RUN_KEYS[0]],
    tmin=-0.2,
    tmax=0.6,
)
print(len(ds))

Each item returns (data: float32[channels,time], label_id: int64).

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