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Fig. 12 | BMC Bioinformatics

Fig. 12

From: Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Fig. 12

The classification accuracy performances for all subjects with all algorithms. CNN and SVM (Linear) outperforms RNN in some subjects with high-level (over 60%) accuracies (S3, S7, S8, S9 in “Dataset 2a” and S4, S6, S8, S9 in “Dataset 2b”). However, in low-level (below 60%) accuracies of subjects, RNN outperforms CNN and SVM-Linear (S2, S4, S6 in “Dataset 2a” and S2, S3 in for “Dataset 2b”). In the average-level accuracies, RNN outperforms CNN and SVM (Linear). a Classification accuracy performances for “Dataset 2a” and b Classification accuracy performances for “Dataset 2b”

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