Search is not available for this dataset
packed_spikes unknown | shape listlengths 4 4 | label class label 890
classes |
|---|---|---|
"VtwODFCQQAHFISYDAYPgFEiAucEzACAYEYCSAEIkgEMZn6KiigBxvCARgORCCGIBZyoJeoQooIESAADgeAGkFEiOwqRElCA0hgF(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"SgiarroFBDIYpEWUShGEGAIPgARBPkoh8d5AADKAkMiDkiQ2s4GwHfEipCFAKAGJBMgGgY1RUj7+UeIGIPGCEAEAAIkbJI+/2pQ(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"4dRd0mAACFZptKiEBQIAMKG7suCCQIEscYAAJMBwBhBgwDcTEkAAJP+CAoAAAIgQsi2UgfgAEHDWuBAgAEkAJO4ABTPEAIA9Nsg(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"Mu2SCZALgJAickgp5AEZBAI75BIJMAAVUMr8IPgAOgggGAqDMgzCLaABKAI0AjIA2DSsKChgGS3ggJyUbVEgEkgqppShyTEodhA(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"AkUzIDoBAgEwBAAAQAACFsEAIIxAgAAERgICCIgAQgFBBAnM0AahgyIISgAAAgAgAAxiyCMEAyAoIAAMAAkQQQASAKCyQIyUQKY(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"AAaAAAAAAAgQEMAAJSwABAAGgAAAAERAIAAEgEAIBAAABAIAJBAgEAAQAAAQEgAAAAgAAAAICBEAAABGSCAEAAgAQAQAAAAAAAg(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"QAAAAAAQABoAAAAIAAAAgAgAABEAQEAAAAAAYAAAABAAAgAAYIAAiAAQACAAgCIAQAQAYCBwCAAAgQAABAAAABIEQCIgCAAAAAA(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"H9IEDCfrYzsTqgJbjFyIz1cx/bZm7/oDwWwZOAKvmizokAHXEXFdMUWTjzhg+d3WNeriklBsSb4cA14rgUEna7FcBUH1JODs+fg(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"JXz8Jet3n+t/QLPn+f3r79dYM3/+rwE2vlNd+1/du9be6X///8YGAUeVP+ftuv6vd8n9m/HwQGK/0Tc1Wv79v36l/6dzZVp1JJV(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
"Ax6l4y0AEKT/3C46VvIBKmCSq8cf4ACpf9lkN0jgACE4QlMI79ZGhf+NaR3N0ABNiEAKEDbDWM9/u3i4ZoxtnwHDkWwe2GADD3c(...TRUNCATED) | [
100,
3,
128,
128
] | 000001_aardvark |
End of preview. Expand in Data Studio
This dataset is spike encoded data of the THINGS EEG. This data is rate coded (Rate coding uses input features to determine spiking frequency). The num of steps is 100. The image resolution was reduced from 224 to 128 and features standardized.
To reconstruct the images run the script below.
import matplotlib.pyplot as plt
REPO_ID = "corquaerit/things-eeg-spikes-RC"
spike_ds = load_dataset(REPO_ID, split="train", streaming=True)
label_feature = spike_ds.features["label"]
def unpack_row(row):
n_bits = int(np.prod(row["shape"]))
bits = np.unpackbits(np.frombuffer(row["packed_spikes"], dtype=np.uint8))[:n_bits]
return bits.reshape(row["shape"]).astype(np.float32)
iterator = iter(spike_ds)
n_examples = 4
fig, axes = plt.subplots(1, n_examples, figsize=(4 * n_examples, 4))
for i in range(n_examples):
row = next(iterator)
spikes = unpack_row(row)
reconstructed = spikes.mean(axis=0)
reconstructed = np.transpose(reconstructed, (1, 2, 0))
label_name = label_feature.int2str(row["label"])
axes[i].imshow(reconstructed)
axes[i].set_title(label_name, fontsize=9)
axes[i].axis("off")
plt.tight_layout()
plt.show()
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