目录

理论

工具

方法实现

参考文献

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理论

工具

python开发环境

方法实现

自定义CSP函数和LDA癫痫脑电二分类

# Import necessary libraries
import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA# Load EEG data for epileptic and non-epileptic patients
eeg_epileptic = np.loadtxt('eeg_epileptic.csv', delimiter=',')
eeg_non_epileptic = np.loadtxt('eeg_non_epileptic.csv', delimiter=',')# Define CSP function
def csp(X, y, n_components):# Calculate covariance matrices for each classcovs = [np.cov(X[y==i].T) for i in np.unique(y)]# Calculate whitening transformation matrixD = np.concatenate(covs)E, U = np.linalg.eigh(D)W = np.dot(np.diag(np.sqrt(1/(E + 1e-6))), U.T)# Whiten dataX_white = np.dot(X, W.T)# Calculate spatial filtersS1 = np.dot(np.dot(covs[0], W.T), W)S2 = np.dot(np.dot(covs[1], W.T), W)E, U = np.linalg.eigh(S1, S1 + S2)W_csp = np.dot(U.T, W)# Apply spatial filtersX_csp = np.dot(X_white, W_csp.T)# Select top CSP componentsX_csp = X_csp[:, :n_components]return X_csp# Apply CSP to EEG data
X_epileptic_csp = csp(eeg_epileptic[:, :-1], eeg_epileptic[:, -1], 4)
X_non_epileptic_csp = csp(eeg_non_epileptic[:, :-1], eeg_non_epileptic[:, -1], 4)# Combine data and labels
X = np.concatenate([X_epileptic_csp, X_non_epileptic_csp])
y = np.concatenate([np.ones(len(X_epileptic_csp)), np.zeros(len(X_non_epileptic_csp))])# Train LDA classifier
lda = LDA()
lda.fit(X, y)# Load test EEG data
eeg_test = np.loadtxt('eeg_test.csv', delimiter=',')# Apply CSP to test EEG data
X_test_csp = csp(eeg_test[:, :-1], eeg_test[:, -1], 4)# Classify test EEG data using LDA
y_pred = lda.predict(X_test_csp)# Print predicted class labels
print(y_pred)

使用mne库函数实现CSP

import numpy as np
import mne
from mne.decoding import CSP
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA# Load EEG data from EDF files
eeg_epileptic = mne.io.read_raw_edf('eeg_epileptic.edf', preload=True)
eeg_non_epileptic = mne.io.read_raw_edf('eeg_non_epileptic.edf', preload=True)
eeg_test = mne.io.read_raw_edf('eeg_test.edf', preload=True)# Extract data (assuming the data is preprocessed)
# The way of extracting labels might change based on how they are stored in the EDF files
X_epileptic = eeg_epileptic.get_data().T  # Transpose to get correct shape
y_epileptic = np.ones(X_epileptic.shape[0])  # Replace with actual method of obtaining labelsX_non_epileptic = eeg_non_epileptic.get_data().T
y_non_epileptic = np.zeros(X_non_epileptic.shape[0])  # Replace with actual method of obtaining labelsX_test = eeg_test.get_data().T
# y_test for evaluation (if available)# Combine data and labels for training
X_train = np.concatenate([X_epileptic, X_non_epileptic])
y_train = np.concatenate([y_epileptic, y_non_epileptic])# Define and apply CSP
n_components = 4
csp = CSP(n_components=n_components, reg=None, log=None, norm_trace=False)
X_train_csp = csp.fit_transform(X_train, y_train)# Train LDA classifier
lda = LDA()
lda.fit(X_train_csp, y_train)# Apply CSP to test data and make predictions
X_test_csp = csp.transform(X_test)
y_pred = lda.predict(X_test_csp)# Print predicted class labels
print("Predicted labels:", y_pred)# Optional: Evaluate model performance (if y_test labels are available)
# y_test = ... # Extract test labels similar to training labels
# print("Accuracy:", accuracy_score(y_test, y_pred))
# print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))

参考文献

1.  Zoltan J. Koles, Michael S. Lazaret and Steven Z. Zhou, "Spatial patterns underlying population differences in the background EEG", Brain topography, Vol. 2 (4) pp. 275-284, 1990