The proposed IEFD-based automated framework with FFNN classifiers achieved classification accuracies up to 99.82% in subject-specific scenarios, outperforming conventional signal decomposition methods.
The proposed IEFD-based automated framework provides highly accurate and robust classification of motor and mental imagery EEG signals for brain-computer interface systems.
Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic components or modes from electroencephalogram (EEG) signals for the development of robust brain-computer interface (BCI) systems. This study proposes a novel automated computerized framework for proficient identification of motor and mental imagery (MeI) EEG tasks by employing empirical Fourier decomposition (EFD) and improved EFD (IEFD) methods. Specifically, the multiscale principal component analysis (MSPCA) is rendered to denoise EEG data first, and then, EFD is utilized to decompose nonstationary EEG into subsequent modes, while the IEFD criterion is proposed for a single conspicuous mode selection. Finally, the time- and frequency-domain features are extracted and classified with a feedforward neural network (FFNN) classifier. Extensive experiments are conducted on four multichannel motor and MeI data sets from BCI competitions II and III using a tenfold cross-validation strategy. Results compared with the other existing methods demonstrated that the highest classification accuracies of 99.82% (data set IV-a), 93.33% (data set IV-b), 91.96% (data set III), and 88.08% (data set V) in subject-specific scenarios, while 82.70% (data set IV-a) in the subject-independent framework are achieved for IEFD with FFNN classifiers collectively. The overall exploratory results authenticate that the proposed IEFD-based automated computerized framework not only outperforms the conventional SD methods but is also robust and computationally efficient for the development of subject-dependent and subject-independent BCI systems.
Yu et al. (Fri,) conducted a other in Motor and mental imagery EEG tasks for BCI systems. Improved empirical Fourier decomposition (IEFD) with feedforward neural network (FFNN) classifier vs. Conventional signal decomposition methods was evaluated on Classification accuracy. The proposed IEFD-based automated framework with FFNN classifiers achieved classification accuracies up to 99.82% in subject-specific scenarios, outperforming conventional signal decomposition methods.
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