Objective Classifying motor imagery tasks via functional near-infrared spectroscopy (fNIRS) poses a significant challenge in Brain-Computer Interface (BCI) research due to the high-dimensional nature of the signals. This study aims to address this challenge by employing the Common Spatial Pattern (CSP) algorithm to reduce input dimensions for support vector machine (SVM) and linear discriminant analysis (LDA) classifiers. Methods Data were collected from 15 healthy right-handed volunteers performing tasks involving left-hand motion, left-hand motor imagery, right-hand motion, and right-hand motor imagery. Signals from 20-channel fNIRS were utilized, with input features including statistical descriptors such as mean, variance, slope, skewness, and kurtosis. The CSP algorithm was integrated into both SVM and LDA classifiers to reduce dimensionality. Main statistical methods included classification accuracy assessment and comparison. Results Mean and slope were found to be the most discriminative features. Without CSP, SVM and LDA classifiers achieved average accuracies of 59.81% ± 0.97% and 69% ± 11.42%, respectively. However, with CSP integration, accuracies significantly improved to 81.63% ± 0.99% and 84.19% ± 3.18% for SVM and LDA, respectively. This represents an increase of 21.82% and 15.19% in accuracy for SVM and LDA classifiers, respectively. Dimensionality reduction from 100 to 25 dimensions was achieved for SVM, leading to reduced computational complexity and faster calculation times. Additionally, the CSP technique enhanced LDA classifier accuracy by 3.31% for both motion and motor imagery tasks. Conclusion Integration of the CSP algorithm significantly enhances the accuracy of SVM and LDA classifiers in distinguishing between different motor imagery tasks, demonstrating promising potential for improving BCI systems' performance.