File size: 2,712 Bytes
dc9d5f6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | """EEG Annotation Tool adapter for SenuaLab EEG-DINO Medium."""
from __future__ import annotations
try:
from braindecode.models import EEGDINO
except ImportError as exc:
raise ImportError("This model requires braindecode[hub]==1.6.1") from exc
from .preprocessing import INTERNATIONAL_10_20_CHANNELS, preprocess_batch as _preprocess
MODEL_CHANNELS = INTERNATIONAL_10_20_CHANNELS
MODEL_INPUT_SAMPLES = 800
MODEL_SAMPLING_RATE_HZ = 200.0
MODEL_WINDOW_SECONDS = 4.0
MODEL_ENTRY_CLASS = "SenuaEEGDINOMedium"
MODEL_NUM_CLASSES = 2
MODEL_CLASS_LABELS = ["Non-IED", "IED"]
MODEL_NON_IED_CLASS_INDEX = 0
MODEL_IED_CLASS_INDICES = [1]
MODEL_DESCRIPTION = "SenuaLab partially fine-tuned EEG-DINO Medium binary IED detector"
MODEL_BATCH_PREPROCESSOR = "preprocess_batch"
MODEL_REQUIRED_REFERENCE = "common average (applied by model adapter)"
MODEL_REQUIRED_FILTERS = ["1-45 Hz zero-phase Butterworth (applied by model adapter)"]
MODEL_REQUIRED_NORMALIZATION = "global four-second window z-score, clipped to [-8,8]"
MODEL_INPUT_UNIT = "scale-invariant after window z-score"
MODEL_SOURCE_SIGNAL_POLICY = "raw"
MODEL_REQUIRES_FULL_WINDOW = True
MODEL_REQUIRES_ALL_CHANNELS = True
MODEL_DEFAULT_THRESHOLD = 0.619140625
MODEL_DEFAULT_STEP_MS = 500.0
MODEL_DEFAULT_PAD_POLICY = "skip"
MODEL_DEFAULT_BATCH_SIZE = 8
MODEL_DEFAULT_BATCH_MEMORY_MB = 384.0
MODEL_VALIDATION_NOTE = "Threshold selected on vEpiSet validation subjects; requires Braindecode 1.6.1 and is not externally validated."
MODEL_PREPROCESSING_NOTE = "The adapter applies the released 1-45 Hz, common-average, 200 Hz resampling, and global-window z-score pipeline."
def preprocess_batch(batch, source_sfreq=None, channel_names=None):
return _preprocess(
batch,
source_sfreq=source_sfreq,
target_sfreq=200,
target_samples=800,
channel_names=channel_names,
)
class SenuaEEGDINOMedium(EEGDINO):
def __init__(self, num_channels: int = 19, num_classes: int = 2, input_length: int = 800):
if num_channels != 19 or input_length != 800:
raise ValueError("SenuaEEGDINOMedium requires 19 channels and 800 samples")
super().__init__(
n_outputs=num_classes,
n_chans=19,
n_times=800,
sfreq=200,
patch_size=200,
n_layer=16,
nhead=8,
dim_feedforward=1024,
channels_kernel_stride_padding_norm=(
(64, 49, 25, 24, (8, 64)),
(128, 3, 1, 1, (8, 128)),
(64, 3, 1, 1, (8, 64)),
),
n_channel_embeddings=19,
n_global_tokens=1,
global_token_layer=1,
drop_prob=0.1,
)
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