E-OptEEG: A Hybrid Ensemble Metaheuristic Feature Optimization for EEG-Based Sentiment Analysis on Resource-Constrained Edge Devices

Electroencephalogram (EEG) signal processing is essential for achieving accurate and efficient real-time edge computing, particularly in resource-constrained smart wearables. They demand lightweight and optimized solutions for rapid analysis and decision-making. However, the higher dimensionality of EEG signals introduces challenges of latency and computational cost on edge devices. This article presents E-OptEEG, a hybrid ensemble metaheuristic framework designed to bring edge-optimized EEG processing and feature selection specifically for low-powered devices. The E-OptEEG framework integrates the strengths of multiple evolutionary feature selection algorithms, leveraging an ensemble threshold voting scheme to combine their outputs and identify the most relevant features. Further, E-OptEEG employs a fruit-fly optimization algorithm with deep metric feature transformation based on cosine similarity to transform the refined feature set to a minimal latent space. E-OptEEG demonstrates its ability to identify and select the most relevant features, enabling effective emotion and sentiment analysis from EEG signals captured through edge-powered wearable devices. Experimental evaluations against state-of-the-art feature selection techniques on three different EEG-based behavior modeling datasets highlight the framework’s effectiveness, achieving an average accuracy exceeding 98% with an average accuracy improvement of 2.55%. The E-OptEEG framework exemplifies the potential for lightweight artificial intelligence solutions to enable real-time, resource-efficient decision-making in wearable electronics.