Novel model based features are introduced in the discrimination of motor imagery tasks using human scalp electroencephalography (EEG) towards the development of Brain Computer Interfaces (BCI). We have acquired human scalp EEG under open-loop and feedback conditions in response to cue-based motor imagery tasks. EEG signals, transformed into frequency specific bands such as mu, beta and movement related potentials, were used for feature extraction with the aim to discriminate tasks. Data were classified using features such as power spectrum and model-based parameters. Two different feature selection methods: stepwise and principal component analysis (PCA), were combined with linear discriminant analysis (LDA). Different training/validation criteria were applied for classification of task related features. Results show that the scalp EEG correlate of the imagery tasks of hands/toes/tongue movements under open-loop conditions and left/right hand movements under feedback conditions, can be well discriminated with classification errors below 20%. Model based techniques, which resulted in classification errors in the range of 2%-30%, have the potential to use advanced control systems theory in the development of BCI to achieve improved performance compared to the performance achieved by currently applied proportional control or filter algorithms.