Abstract
Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (hlong, hshort) and weight vector W parameters, which affect the convergence of the algorithm. This paper proposes a hybridization of Stone's BSS with Particle Swarm Optimization (PSO) to boost the separation process. An improved Stone's BSS (ISBSS) method is employed to reject eye blinking from the electroencephalogram (EEG) mixture. The electroencephalogram (EEG) mixed-signal is first centralized and whitened; then, it is incorporated into the particle swarm optimization (PSO) iterative algorithm to process the initial (hlong, hshort) and generate the weight vector W parameters randomly. Finally, the generalized eigenvalue decomposition (GEVD) method is applied to extract EEG singles to obtain a clean EEG signal. A clinical EEG database is used to test the improved and other algorithms. The GEVD method estimates the measurement performance of the proposed algorithm using a carrier-to-interference ratio and integral square error and compares the proposed algorithm with the conventional Stone's BSS, fast independent component analysis (FastICA), evolutionary fast independent component analysis (EFICA), and joint approximate diagonalization of eigen matrices (JADE) algorithms to check its effectiveness. The results show that the suggested hybrid method has a better performance and decreasing elapsed time than conventional Stone's BSS and other algorithms.
Highlights
In biomedical signal processing science, electro-encephalography is a method used to present the efficiency of the human brain
The real EEG waves are possessed until the third second (256 × 3 = 768) specimens, which are organized by the existence of eye artefacts compared with the output curve generated by other algorithms for the FP1 channel
The improved Stone’s BSS (ISBSS) algorithm is clearly superior to the other algorithms due to the removal of brain signals and isolation of eye blink artefacts
Summary
In biomedical signal processing science, electro-encephalography is a method used to present the efficiency of the human brain. Brain efficiency is determined by sensing elements attached to the patient’s head [1]. The essential characteristics of EEG signals are noted without difficulty by sensing elements. The EEG signals consist of a mixed-signal with unwanted signals during the recording process, such as eye blinking and power equipment noises, which make it difficult to understand the EEG brain activity. The EEG signals, which are in microvolts and a frequency range of 0–64 Hz, are contaminated by eye blinking in the 0–16-Hz frequency range. Such an unwanted mixture should be separated from the EEG. Eye blinking and eye movement are all out-of-control body movements that parasitize requisite brain signal results for operating a brain-computer interface (BCI)
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