Abstract
EEG data processing method is usually digital filter designed by the traditional method. Its disadvantage is the transition zone is wide and the filtering effect is poor. Using an improved particle swarm optimization algorithm on IIR digital filters design, the performances of filters designed by various methods are compared and analyzed. Experiments illustrate particle swarm optimization algorithm is effective in IIR filter design and its performance is promising.
Highlights
EEG signal, a kind of weak bioelectrical signal, usually has amplitudes from 5μv to 100μv and frequencies from 0.5Hz to 35Hz
In order to verify the effectiveness of the proposed algorithm in EEG signal processing, a fourth-order IIR digital filter design with passband 0.5Hz-35Hz is used as an example
This paper uses four particle swarm optimization intelligent algorithms to design IIR filters, and compared with filters designed by traditional methods
Summary
EEG signal, a kind of weak bioelectrical signal, usually has amplitudes from 5μv to 100μv and frequencies from 0.5Hz to 35Hz. EEG is the overall response of brain nerve cell conduction information to electrical activity on the surface of the cerebral cortex or scalp It is a spontaneous, rhythmic electrical activity signal from the brain cell population that can reflect brain activity and it contains a large amount of information for both diseases and physiological activity[3,4]. Designing IIR digital filter need to design the analog filter firstly, and transform it into a digital filter. Some scholars have applied particle swarm algorithms to the IIR filters design[15,16,17,18], but these algorithms may have shortcomings of local optimum or slow convergence.
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