Step (1): utilize the FDDL algorithm to learn a structured dictionary D.
In this experiment, we compare the proposed FDDL-ELM with three baseline algorithms, including ELM, FDDL, and H-ELM.
From the results shown in Table 3, it is evidenced that the FDDL-ELM algorithm achieved comparable performance with other state-of-the-art methods, such as single-layer ELM, FDDL, and H-ELM with deep architecture.
We compared the proposed method FDDL-ELM with ELM, FDDL, and H-ELM on BCI Competition III Datasets IVa and Ilia and BCI Competition IV Dataset IIa.
From Table 4, it can be seen that the proposed method outperformed the ELM and FDDL algorithms on almost all subjects (except subject B2) in binary-classification applications.
Note that our method also outperformed ELM and FDDL in 8 of the 9 subjects (except subject C8).
The nonlinear property of FDDL-ELM allowed for its superior performance over the FDDL approach when processing the nonstationary EEG signals.
Method Accuracy (%) SRC 77.34 K-SVD 89.41 D-KSVD 90.60 LC-KSVD 91.78 WBDL (global) 94.00 WBDL (local) 95.33 Table 3: The recognition accuracies (%) of ScSPM, SRC, K-SVD, D-KSVD, LC-KSVD1, LC-KSVD2, FDDL
, SVGDL, and WBDL on Caltech101 database.