Eye tracking is one of the most natural ways for people with amyotrophic lateral sclerosis and other locked-in and paralysis diseases to communicate. The majority of existing eye-tracking computer input systems use cameras to capture images of the user's eyes to track pupil movements. Most camera-based systems are expensive and not user-friendly. This paper proposes an eye-tracking system called Eyel.ive that uses discrete infrared sensors and emitters as the input device. The eye-tracking and ca libration algorithms classify eye positions into six categories, namely looking up, looking down, looking left, looking right, looking straight ahead (i.e. middle direction), and eye s closed. A graphical user interface optimized for EyeLive is also developed. It divides the screen into a nine-cell grid and uses a hierarchical selection approach for text input. EyeLive's hardware, eye-tracking algorithm, calibration, and user inter face arc compared to those of existing eye-tracking systems. The advantage s of the EyeLive system, such as low cost, user friendliness, and eye strain reduction, are discussed. The performance of the system in classifying eye positions is experimentally tested with eight health y individuals. The results show a 5.6% error rate in classifying eye positions in live directions and a 0% error rate in classifying closed eyes. Additional experiments show that the average typing speed is 1.95 words/min for a novice user and 2.9 1 words /min for an experience d user. The tradeoffs of lower typing speed versus other advantages comparing to other systems are explained.