Motor Imagery Brain-Computer Interfaces (MI-BCIs) based on Electroencephalography (EEG) enable endogenous communication using the mental rehearsal of motor actions without performing real movements. Core challenges faced by MI-BCI include user discomfort from electrode caps, signal variability across individuals and recording sessions, and the high dimensionality and noisiness of EEG data, which complicates feature extraction and classification. Current literature primarily focuses on global channel reduction or feature selection strategies to overcome the high-dimension EEG problem. This study introduces a novel methodology to improve MI-BCIs classification rates by training individual classifiers for each EEG channel, diverging from traditional full-array electrode approaches. We evaluated this method in offline training and pseudo-online settings (simulating real-time EEG signal using unseen data), comparing its performance, both for individual channel classifiers and ensembles (Stacking, Majority Vote), against established feature extraction and selection techniques: Filter Bank Common Spatial Patterns (FBCSP), and Minimum Redundancy Maximum Relevance (mRMR). Our approach uses power bands (Theta, Alpha, Beta, Gamma) to train several classifiers (SVM, MLP, KNN, and LDA). Initial evaluation of the full set of 256 individual channel classifiers revealed suboptimal performance, often below mRMR and FBCSP. However, selecting the top-10 individual channel classifiers per subject significantly boosted accuracy by approximately 20%, with all subjects outperforming FBCSP and several surpassing mRMR. This highlights the potential of localized, personalized channel subsets over uniform electrode coverage. In over-time classification, the Stacking ensemble achieved consistent high accuracy, even when individual classifiers showed high variability. This framework offers a computationally efficient and personalized solution for MI-BCI, particularly promising for real-time adaptive systems.