The research presented explores the efficacy of a hybrid hardware/software architecture using a single-channel EEG for data acquisition and subsequent processing to achieve maximum accuracy in motor task classification for a Brain Computer Interface (BCI) System. The work focuses on classifying motor task-related EEG data using a single-electrode system and leveraging Machine Learning (ML) algorithms, in tandem with advanced filtering and feature extraction techniques. Results show that precise classification of motor tasks is possible using this hybrid Hardware–Software approach. SVM, Naive Bayes and Random Forest algorithms were used for the classification task and their efficacy was compared. An accuracy of 89% was achieved by the best classification model on data acquired using a single-channel EEG system.