File size: 2,681 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 75 76 77 78 | """EEG Annotation Tool adapter for the experimental SenuaLab EEGPT linear probe."""
from __future__ import annotations
from typing import Any
import mne
try:
from braindecode.models import EEGPT
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
EEGPT_CHANNELS = [
"Fp1", "Fp2", "F3", "F4", "C3", "C4", "P3", "P4", "O1", "O2",
"F7", "F8", "T7", "T8", "P7", "P8", "Fz", "Cz", "Pz",
]
MODEL_CHANNELS = INTERNATIONAL_10_20_CHANNELS
MODEL_CHANNEL_ALIASES = {"T3": "T7", "T4": "T8", "T5": "P7", "T6": "P8"}
MODEL_INPUT_SAMPLES = 1000
MODEL_SAMPLING_RATE_HZ = 250.0
MODEL_WINDOW_SECONDS = 4.0
MODEL_ENTRY_CLASS = "SenuaEEGPTLinearProbe"
MODEL_NUM_CLASSES = 2
MODEL_CLASS_LABELS = ["Non-IED", "IED"]
MODEL_NON_IED_CLASS_INDEX = 0
MODEL_IED_CLASS_INDICES = [1]
MODEL_DESCRIPTION = "Experimental SenuaLab EEGPT frozen-encoder linear probe"
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 = 0.40234375
MODEL_DEFAULT_STEP_MS = 500.0
MODEL_DEFAULT_PAD_POLICY = "skip"
MODEL_DEFAULT_BATCH_SIZE = 16
MODEL_DEFAULT_BATCH_MEMORY_MB = 256.0
MODEL_VALIDATION_NOTE = "Experimental ablation; not recommended for deployment and not externally validated."
def preprocess_batch(batch, source_sfreq=None, channel_names=None):
return _preprocess(
batch,
source_sfreq=source_sfreq,
target_sfreq=250,
target_samples=1000,
channel_names=channel_names,
)
def _chs_info() -> list[dict[str, Any]]:
info = mne.create_info(EEGPT_CHANNELS, sfreq=250.0, ch_types="eeg")
info.set_montage("standard_1020")
return info["chs"]
class SenuaEEGPTLinearProbe(EEGPT):
def __init__(self, num_channels: int = 19, num_classes: int = 2, input_length: int = 1000):
if num_channels != 19 or input_length != 1000:
raise ValueError("SenuaEEGPTLinearProbe requires 19 channels and 1,000 samples")
super().__init__(
n_outputs=num_classes,
n_chans=19,
chs_info=_chs_info(),
n_times=1000,
sfreq=250.0,
chan_proj_type="none",
return_encoder_output=False,
)
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