In order to extract discriminative information, time-frequency matrix is often transformed into a 1D vector followed by principal component analysis. This study contributes a two-directional two-dimensional principal component analysis (2D 2 PCA) based technique for time-frequency feature extraction. 2D 2 PCA is directly conducted on the time-frequency matrix obtained from the S transform rather than 1D vectors for feature extraction. The proposed method can significantly reduce the

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... l cost while capture the directions of maximal time-frequency matrix variance. The efficiency and effectiveness of the proposed method is demonstrated by classifying eight hand motions using four-channel myoelectric signals recorded in health subjects and amputees.