MNE & Scikit-Learn Common Spatial Pattern (CSP) EEG Classifier
Production ML pipeline for decoding motor imagery Brain-Computer Interface signals (Left Hand vs Right Hand movement intention). Extracts spatial covariance features using CSP and trains an LDA / SVM classifier with cross-validation.
Appended directly to the LLM system prompt for tailored code output.
Generated Code
⚡ Instant Boilerplate| 1 | import numpy as np |
| 2 | from mne.decoding import CSP |
| 3 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis |
| 4 | from sklearn.pipeline import make_pipeline |
| 5 | |
| 6 | X = np.random.randn(100, 16, 500) |
| 7 | y = np.random.choice([0, 1], size=100) |
| 8 | |
| 9 | pipeline = make_pipeline(CSP(n_components=4), LinearDiscriminantAnalysis()) |
| 10 | pipeline.fit(X, y) |
| 11 | print("Pipeline trained successfully!") |
| 12 |
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