Applications in brain-computer interfacing (BCI) have also been proposed. The P300 has a number of desirable qualities that aid in implementation of such systems. First, the waveform is consistently detectable and is elicited in response to precise stimuli. The P300 waveform can also be evoked in nearly all subjects with little variation in measurement techniques, which may help simplify interface designs and permit greater usability. The speed at which an interface is able to operate depends on how detectable the signal is despite "noise." One negative characteristic of the P300 is that the amplitude of the waveform requires averaging of multiple recordings to isolate the signal. This and other post-recording processing steps determine the overall speed of an interface. The algorithm proposed by Farwell and Donchin provides an example of a simple BCI that relies on the unconscious decision making processes of the P300 to drive a computer. A 6×6 grid of characters is presented to the subject, and various columns or rows are highlighted. When a column or row contains the character a subject desires to communicate, the P300 response is elicited (since this character is "special" it is the target stimulus described in the typical oddball paradigm). The combination of the row and column which evoked the response locates the desired character. A number of such trials must be averaged to clear noise from the EEG. The speed of the highlighting determines the number of characters processed per minute. Results from studies using this setup show that normal subjects could achieve a 95% success rate at 3.4–4.3 chars/min. Such success rates are not limited to non-disabled users; a study conducted in 2000 revealed that 4 paralyzed participants (one with complete paraplegia, three with incomplete paraplegia) performed as successfully as 10 normal participants.