Enhanced grasshopper optimization algorithm with extreme learning machines for motor‐imagery classification

Abstract In Brain Computer Interface (BCI), achieving a reliable motor‐imagery classification is a challenging task. The set of discriminative and relevant feature vectors plays a crucial role in classification. In this article, an enhanced optimization technique is implemented for selecting active feature vectors to enhance motor‐imagery classification using Electroencephalography (EEG) signals. After collecting the input EEG signals from BCI competition III‐4a and IV‐2a databases, the 6th‐order butter‐worth filter is employed for eliminating base‐line wander noise from the raw EEG signals. Further, the Variational Mode Decomposition technique is applied for separating the important signal components from the composite EEG signals, and then, the Higher Order Statistic, kurtosis, skewness, standard deviation, and entropy are utilized for feature extraction. The high‐dimensional feature values are given to the Enhanced Grasshopper Optimization Algorithm for optimum feature selection, which are given to the Extreme Learning Machines (ELM) classifier for motor‐imagery classification. Finally, in the resulting section, the optimized ELM model achieved 99.48% and 99.12% of accuracy on the BCI competition III‐4a and IV‐2a databases, where the achieved results are maximum compared to the traditional deep learning models.