RepTrace

Examples

Run the synthetic time-resolved decoding example with:

python examples/basic/time_resolved_decoding.py

The example creates a small MNE EpochsArray, attaches metadata, and runs the same decoder used for public benchmark data.

Plot the output with:

python -m reptrace.plot_time_decode \
  results/synthetic_decoding.csv \
  --chance 0.5 \
  --out results/synthetic_decoding.png

Public datasets

• NOD covers the first natural-image semantic decoding benchmarks.
• OpenNeuro ds000117 stages a small face-vs-scrambled MEG benchmark for cleaner onset-detection sanity checks.

Stream stimulus detection

After exporting probability observations with prob_class_* columns, detect zero, one, or many stimulus events in a long stream with:

python -m reptrace.stimulus_detection \
  results/sub-01_stream_observations.csv \
  --stream-column sequence_id \
  --score-mode class_probability \
  --threshold-window -0.35 -0.05 \
  --threshold-method max_run \
  --threshold-quantile 0.95 \
  --min-consecutive 2 \
  --merge-gap 0.05 \
  --refractory 0.20 \
  --out-events results/stimulus_events.csv \
  --out-summary results/stimulus_event_summary.csv

When annotated stimulus onsets are available, pass them for event-level matching and latency summaries:

python -m reptrace.stimulus_detection \
  results/sub-01_stream_observations.csv \
  --annotations results/sub-01_stimulus_annotations.csv \
  --stream-column stream_id \
  --score-mode class_probability \
  --threshold-window -0.35 -0.05 \
  --threshold-method max_run \
  --threshold-quantile 0.95 \
  --detection-window 0.0 inf \
  --min-consecutive 2 \
  --merge-gap 0.05 \
  --refractory 0.20 \
  --out-events results/sub-01_stimulus_events.csv \
  --out-summary results/sub-01_stimulus_event_summary.csv \
  --out-thresholds results/sub-01_stimulus_thresholds.csv

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