An important problem in brain signal processing is the presence of noise and artifacts in neural recordings A systematic method of recognition, identification and artifact removal of Electroencephalography (EEG) waves is essential to reduce the probability of misinterpretation of brain waves and to limit its consequences. Electrophysiological signals produced by eye movement, eye blinks, head movement and muscle noise are typical causes of artifacts. So this paper mainly concentrates on removal of these artifacts from the recorded data using Independent component analysis (ICA) approach. ICA is a statistical method to extract independent source signals from the multivariate data. This paper uses orthogonal property of matrices to reduce the number of calculations and complexity of the 2-channel ICA algorithm. Using this property, the artifacts are removed with reduced area and power consumption. Simulation is also carried out for 9-channel EEG using Fast Confluence Adaptive ICA (FCAICA) algorithm. High convergence speed is also achieved by this adaptive method. Signal to Interference Ratio (SIR) is improved using IEEE single precision floating-point arithmetic.
Contrast function optimization; Convergence speed; EEG Artifact Removal; Independent component analysis
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