Modèle spatial commun (CSP) is a statistical technique used primarily in traitement du signal and apprentissage automatique to analyze and extract features from spatially distributed data, often in the context of brain-computer interfaces (BCIs) and biomedical signal analysis. CSP aims to enhance the discrimination between different classes of signals by identifying spatial patterns that maximize variance for one class while minimizing variance for another.
The algorithm works by computing spatial filters that transform the input signals into a new space where the target classes can be better separated. Typically, CSP is applied to multi-channel data, such as electroencephalogram (EEG) signals, where the spatial relationships between different channels are crucial for effective classification.
En pratique, la CSP implique les étapes suivantes :
- Prétraitement des données: Cela inclut le filtrage, la segmentation et la normalisation des signaux bruts.
- Covariance Estimation: Calculer les matrices de covariance des signaux pour chaque classe.
- Valeur propre Décomposition: Perform eigenvalue decomposition on the covariance matrices to find the spatial patterns.
- Filtrage spatial : Create spatial filters that maximize the variance of one class while minimizing the variance of the other.
- Extraction de caractéristiques: Apply the spatial filters to the raw signals to obtain features that can be used for classification.
CSP has been widely used in applications involving brain-computer interfaces, such as controlling devices through thought, and in other areas like reconnaissance des émotions and motor imagery tasks. Its effectiveness in enhancing signal classification performance makes it a popular choice for researchers and practitioners working with spatially distributed data.