A vital input for a task using the brain in BCI (Brain Computer Interface) applications is the motor imagery (MI) signal from the brain. Users of BCI systems can operate external equipment by using their brain activity, using motor imagery as a control method. Innumerous Electroencephalography (EEG) channels are used to gather nerve impulses from the brain, which are the most prevalent input for Brain Computer Interface systems while they are minimally invasive, flexible, and low in price. The computational overhead is increased by multichannel BCI systems’ high-dimensional data, which causes processing to be slower and to cost more money. EEG details are regularly gathered from over 100 different brain regions; therefore, it is essential to use channel selection algorithms to choose the ideal channels for a given circumstance. However, the primary objective of choosing the channel in EEG data analysis is to lessen the computer intricacy, improve the precision of classification by eliminating over fitting, and save setup time. In this study, we suggested a remora optimization technique that was inspired by nature to lessen the computational load brought on by several channels. Using predetermined criteria, a number of channel selection evaluation techniques, whether classification-based methods used or not it extracted the proper channel subsets. In order to determine the greatest classification accuracy, the classification procedures were utilized in the end. Three publicly available EEG datasets are used to validate the experiment (BCI Competition IV-1,2a, Competition III-3a), and it resulted in superior classification accuracy.

In the rapidly evolving digital landscape, trend forecasting has become a critical task for decision-makers across industries. Traditional methods struggle with adaptability, scalability, and real-time trend identification. This paper presents a novel framework that integrates Generative AI with the Proposed Guided Remora Optimization Algorithm (PGROA) to enhance trend prediction accuracy while maintaining robustness across dynamic and multimodal datasets. The framework leverages transformer-based architectures for feature extraction, adaptive learning mechanisms for real-time updates, and cross-domain generalization techniques to ensure scalability. Additionally, interpretability methods such as SHAP values and attention mechanisms provide transparency in model predictions. The proposed system is evaluated on diverse datasets, demonstrating superior performance with an accuracy of 94.8%, an F1-score of 93.8%, and a significantly reduced RMSE of 0.072, outperforming existing deep learning and hybrid models. This research establishes a scalable and interpretable AI-driven approach to trend prediction, equipping decisionmakers with actionable insights for dynamic environments