API Reference¶

Core¶

`Data`	Create dataloaders for brain-modeling experiments.
`Experiment`	Brain-modeling experiment with support for loading pretrained weights.
`BenchmarkAggregator`	Orchestrate multiple `Experiment` runs and visualise results.

Pytorch-lightning module for M/EEG model training.

CLI¶

`run_benchmark`	Run one or more NeuralBench experiments from Python.
`run_benchmark_cli`	CLI entry point for `neuralbench`.

Events Transforms¶

`TextPreprocessor`	Clean and filter text-related events.
`SklearnSplit`	Perform train/val/test split using sklearn's `train_test_split`.
`SimilaritySplit`	Perform train/val/test split based on similarity of sentence events.
`PredefinedSplit`	Assign train/test labels based on a predefined split, and optionally split train into validation as well.
`CropSleepRecordings`	Keep up to max_wake_duration_min mins of wake (W) time before and after the first and last sleep events.
`CropTimelines`	Crop neuro timelines.
`AddDefaultEvents`	Add default events to a timeline to fill out its duration.
`OffsetEvents`	Offset selected events by specified amounts.

Callbacks¶

`TestFullRetrievalMetrics`	Accumulate predictions on entire test set before evaluating metrics.
`RecordingLevelEval`	Callback to evaluate average prediction over each recording (timeline).
`PlotConfusionMatrix`
`PlotRegressionVectors`	Visualize predictions vs ground truth for multi-dimensional regression tasks.
`plot_confusion_matrix`	Plot confusion matrix/matrices with a shared colorbar.

Utilities¶

`TrainerConfig`	Joint configuration for Trainer and some callbacks.
`load_checkpoint`	Load checkpoint through state_dicts.
`compute_class_weights_from_dataset`	Compute class weights from training dataset for handling class imbalance.
`make_weighted_sampler`	Create a weighted random sampler for the given dataset to handle class imbalance.

Modules¶

`DownstreamWrapper`	Configuration for wrapping a (pretrained) model for downstream fine-tuning or linear probing.
`DownstreamWrapperModel`	Wrapper for downstream evaluation of pretrained models.

Configuration¶

`setup_config`	Set up neuralbench configuration.
`load_config`	Load neuralbench configuration.
`get_config`	Get the current configuration, loading it if necessary.