Visualization

import neuroencoder as ne
ne.explore(embeddings)

import mne, torch, neuroencoder as ne
from neuroencoder import MRL
from mne.datasets import eegbci
mne.set_log_level("ERROR")

model = MRL.from_pretrained()

all_emb, all_files = [], []
for s in [1, 2, 3, 4, 5]:
    for r in [1, 2, 4, 6, 8, 10]:
        f = eegbci.load_data(subjects=s, runs=r, verbose=False)[0]
        raw = mne.io.read_raw_edf(f, preload=True, verbose=False)
        eegbci.standardize(raw)
        # Sliding window: 30s epochs, 1s hop
        images = ne.preprocess(
            raw.get_data(),
            sfreq=raw.info["sfreq"],
            channel_names=raw.ch_names,
            stride_seconds=1.0,
        )
        emb = model.predict(images, dim=192)
        all_emb.append(emb)
        all_files.extend([f.split("/")[-1]] * len(emb))

ne.explore(torch.cat(all_emb), filename=all_files, epoch_seconds=1.0)

ne.serve(embeddings, filename=filenames, port=5055)

ne.explore(embeddings, filename=["recording_A.edf"] * 50 + ["recording_B.edf"] * 50)

# Default - non-overlapping 30s epochs
ne.preprocess(data, sfreq=256, channel_names=ch_names)

# Sliding 30s windows hopping every 1 second
ne.preprocess(data, sfreq=256, channel_names=ch_names, stride_seconds=1.0)

ne.explore(embeddings, method="umap")    # default
ne.explore(embeddings, method="tsne")
ne.explore(embeddings, method="pca")

embedding-atlas embeddings.parquet --x x --y y --neighbors neighbors

ne.plot(embeddings)                  # by time (default, viridis)
ne.plot(embeddings, color="cluster") # DBSCAN auto-clustering
ne.plot(embeddings, color=labels)    # custom values