We here studied the efficacy of wide-band frequency spectra (WBFS) features using multi-taper (MT) spectral analysis in application to motor imagery based Brain Computer Interfaces. We acquired motor imagery task related human scalp electroencephalography (EEG) signals for left vs. right hand movements using 3 different pairs of visual arrow cues. Left vs. right movement imagery discrimination was conducted using a Naïve Bayesian classifier using WBFS features and commonly used Mu-Beta spectral features for EEG signals from central+parietal and central only electrode positions. Task discrimination accuracy results showed that WBFS features using MT spectral analysis provided significantly better performance (with a 95% confidence level) than that of using Mu-Beta spectral features commonly used. The use of central+parietal electrode signals improved discrimination accuracy significantly when compared to the accuracy using the central only signals, implying that sensory information enhanced task discrimination significantly.