Clustering Sparse Swarm Decomposition for Automated Recognition of Upper Limb Movements From Nonhomogeneous Cross-Channel EEG Signals
Shailesh Vitthalrao Bhalerao, Ram Bilas Pachori
Abstract
Decoding motor imagery (MI) electroencephalogram (EEG) (MI-EEG) based upper limb movements become a prominent tool to people with neuro-muscular diseases. In this letter, the clustering sparse swarm decomposition method (CSSDM) is proposed to extract homogeneous spectral characteristics across non-homogeneous multichannel MI-EEG sensor data with significant channel selection for improving decomposition and enhancing the performance of automatic upper limb movement recognition. CSSDM, a novel approach proposed to address the limitation of processing non-homogeneous signals like EEG, extends the capabilities of existing swarm decomposition. In CSSDM, first, the non-homogeneous EEG signal is analyzed by a density-based spatial clustering algorithm based on canonical correlation analysis-mutual information measure into homogeneous EEG clusters. The CSSDM adopts modified swarm filtering and sparse spectrum to automatically deliver into optimal band-limited modes, which shows the mutual characteristics across channels. Further, the time-frequency graph spectral (TFGS) features are extracted from CSSDM modes. The experimental results on the 7-class BNCI EEG (001-2017) database reveal that CSSDM-based classification frameworks outperformed all baseline models and achieved the highest accuracy of 49.02 ± 0.61% using 10-fold cross-validation.