Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding

In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10-17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing.

Automated summary

In myoelectrical pattern recognition, the lack of hemiplegic data and limited knowledge of skeletomuscular function make feature extraction methods for stroke-oriented applications challenging and discordant. The need for robust, subject-independent feature generation in the presence of technical and clinical barriers is also a concern when using supervised learning. This study investigates the brute-force analysis of feature vectors for stroke gesture recognition in acute patients, revealing the advantage of using post-brute-force singular vectors concatenated via a Fibonacci-like spiral net ranking for feature selection, which improves classification rate performance compared to canonical feature sets.