Editing Ai Neuroscience (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''EEG motor imagery decoding for BCI:'''
<syntaxhighlight lang="python">
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset

class EEGNet(nn.Module):
    """Compact CNN for EEG classification — widely used in BCI research."""
    def __init__(self, n_channels=64, n_classes=4, n_samples=256):
        super().__init__()
        # Temporal convolution (learns frequency-specific filters)
        self.temporal = nn.Conv2d(1, 8, (1, 64), padding=(0, 32), bias=False)
        self.bn1 = nn.BatchNorm2d(8)
        # Depthwise spatial convolution (learns spatial filters per temporal filter)
        self.depthwise = nn.Conv2d(8, 16, (n_channels, 1), groups=8, bias=False)
        self.bn2 = nn.BatchNorm2d(16)
        self.avg1 = nn.AvgPool2d((1, 4))
        # Separable convolution
        self.separable = nn.Conv2d(16, 16, (1, 16), padding=(0, 8), bias=False)
        self.bn3 = nn.BatchNorm2d(16)
        self.avg2 = nn.AvgPool2d((1, 8))
        self.dropout = nn.Dropout(0.5)
        # Flatten and classify
        flat_dim = 16 * ((n_samples // 32) // 1)
        self.classifier = nn.Linear(flat_dim, n_classes)

    def forward(self, x):  # x: (B, 1, C, T)
        x = self.bn1(self.temporal(x))
        x = self.bn2(self.depthwise(x))
        x = self.avg1(torch.nn.functional.elu(x))
        x = self.dropout(x)
        x = self.bn3(self.separable(x))
        x = self.avg2(torch.nn.functional.elu(x))
        x = self.dropout(x)
        x = x.flatten(1)
        return self.classifier(x)

# Load BCI Competition IV dataset (motor imagery: left hand, right hand, feet, tongue)
# X: (n_trials, 1, n_channels, n_samples), y: class labels
model = EEGNet(n_channels=64, n_classes=4)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()
</syntaxhighlight>

; Neuroscience AI tools
: '''Spike sorting''' → Kilosort4 (GPU spike sorter), MountainSort, SpyKING CIRCUS
: '''Calcium imaging''' → Suite2p (fast segmentation + trace extraction), CaImAn, CellPose
: '''fMRI analysis''' → Nilearn, FSL, FreeSurfer; deep learning: fastMRI, BrainBERT
: '''EEG/BCI''' → MNE-Python, MOABB (benchmarks), EEGLAB; EEGNet, EEG-Conformer
: '''Connectomics''' → CAVE (Connectome Annotation Versioning Engine), CloudVolume
</div>

<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">