MATLAB for Neural Electrical Signal Analysis

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Feature extraction and classification of time-series EMG signals using AR modeling techniques with MATLAB implementation

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We can utilize the Autoregressive (AR) model for feature extraction and classification of time-series electromyographic (EMG) signals. The AR model, standing for "Autoregressive Model," is a fundamental time series analysis method that describes the relationship between a time series and its past values. In MATLAB implementation, this typically involves using functions like `ar()` or `aryule()` to estimate model coefficients, where the order selection (e.g., using Akaike Information Criterion) determines how many previous samples influence the current value. For EMG analysis, we extract AR coefficients as features that capture signal dynamics, which can then be fed into classifiers like SVM or k-NN. This approach enables us to obtain richer and more detailed data representations, facilitating better understanding of muscle activation patterns and fatigue indicators through signal characteristics and behavioral analysis.

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