In recent years, Big data has become an important issue with the rapid increase of digitalization. The problems that find solutions to the problems of finding and evaluating the features of big data are called optimization problems. In this paper, data sets containing EEG signals have been studied. The core of the problem is to identify true EEG signals and minimize the extra brain activity signals in the captured data, thus providing more reliable interpretation accuracy. In this study, a new algorithm based on swarm intelligence is proposed to solve Big Data optimization problems (BigOpt). An Improved PSO-Q was proposed by updating the random walking phase of the Particle Swarm Optimization on based quantum behaved algorithm (PSO-Q) for BigOpt problems. PSO-Q's local search capability has been improved. The success of PSO-Q and IPSO-Q has been thoroughly tested in various cycles (maximum iterations) (300, 400, 500, and 1000) and population sizes (10, 25, and 50) on six data sets. PSO-Q and IPSO-Q results were statistically evaluated with the Wilcoxon Signed-Rank Test. PSO-Q and IPSO-Q have been compared with newly developed swarm-based algorithms (BA, TSA, Jaya, AOA, etc.) in the literature in recent years. The success of IPSO-Q has been shown by evaluating the results obtained. The results showed that IPSO-Q can be used as an alternative algorithm in BigOpt problems.