The identification of the intentionality of movement is a key-aspect for the development of brain-computer interfaces (BCIs) applicable to daily life in neurological patients. We present a novel method of processing of electroencephalography (EEG) signals for the extraction of movement intention in neurological patients with upper limb tremor. This method is based on event-related EEG desynchronization, considering α (8-12 Hz), β (13-30 Hz), and γ (30-40 Hz) bands. We have analyzed the EEG signals from the sensorimotor areas of 4 neurological patients presenting an upper limb tremor (grade 1 to 3/4) and executing successive finger-to-nose movements. A Quality Parameter (QP) for the detection of intentionality of movement has been extracted, by considering: (a) the changes in the β²/α and β/α ratio (representing bursts of β-γ frequencies) during the pre-movement period; (b) an appropriate threshold predicting the movement; (c) the number of movements executed. This QP allows the prediction of the voluntary movement with a probability between 70% and 90%. This method could be implemented in a wearable BCI to detect the intentionality of movement and could be used, for instance, to trigger the electrical stimulation in selected muscles of upper limbs with the aim of blocking the emergence of tremor.