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. Author manuscript; available in PMC: 2022 Feb 15.
Published in final edited form as: J Neural Eng. 2020 Oct 13;17(5):056014. doi: 10.1088/1741-2552/abb63b

Figure 4. Finger flexion decoding using gamma.

Figure 4.

(a) Confusion matrices for Naïve-Bayes classifier results. Percentage values above confusion matrices indicate classification accuracies. All algorithms could predict flexion above random chance (p < 0.01). Accuracies of PSCA and FFT are significantly greater than EMD in case P1a and P1b (p < 0.05, McNemar test). (b) Comparison of gamma accuracies based on classification method (NB = Naïve-Bayes, LDA = linear discriminant analysis, SVM = support vector machine). The hierarchy of performance between the four decomposition algorithms is comparable across all three classifier methods.