In this paper, an optimized hardware architecture is presented for a motor imagery-based Brain-Computer Interface (MI-BCI) tailored for real-time, wearable applications that demand high accuracy and low power consumption. The design captures and filters EEG signals to isolate motor imagery patterns, utilizing Linear Discriminant Analysis (LDA) for feature extraction followed by Euclidean distance-based classification. Key optimizations, including sequential matrix multiplication, pipelining, and an FSM-based control unit, achieve 91.67% accuracy with a power consumption of 2 mW on 180 nm technology, significantly enhancing performance compared to conventional designs.