This work analyzes the trade-off between classification accuracy, energy consumption, and execution time of 10 wrapper methods for EEG classification in the context of Brain-Computer Interface. The evaluated wrapper methods consist of multiple classification algorithms, such as linear classifiers, boosting-based methods, and neural network-based approaches, in combination with bioinspired optimization techniques whose objective is the feature selection to enhance the accuracy achieved by the classifiers. The experimental study uses the dataset 2a from the BCI Competition 2008, which focuses on motor-imagery tasks. The results reveal that linear classifiers provide fast processing times and low energy consumption, whereas neural networks and ensemble methods achieve superior classification rates at the expense of increased computational costs. The study also highlights the importance of balancing model complexity with efficiency requirements for biomedical applications.