Implementation of EOG mouse using Learning Vector Quantization and EOG-feature based methods

It is difficult for patients with severe physical disabilities to communicate with others, such as amyotrophic lateral sclerosis and serious paraplegia. Owing to the illness in which they lost their limb motor function and language function, they cannot move even their muscles except eye. In order to provide an efficient means of communication for those patients, in this paper we proposed a system that uses EOG-feature based methods and Learning Vector Quantization algorithm to recognize eye motions. According to the recognition results, we use API (application programming interface) to control cursor movements. The recognition part consists of four steps. First, we measure EOG signals by every 1.8 seconds. Next, we make a judge whether eye motion subsists in the 1.8 seconds EOG data, if any, we extract the data of each motion from the 1.8 seconds EOG data. After that we use Fast Fourier Transform to obtain the frequency features of the extracted motion. Finally we use Learning Vector Quantization network and characteristics of EOG features at each motion to recognize eye motions. The LVQ network is trained beforehand. In this paper we recognized motions of rolling eye upward, rolling downward, rolling left, rolling right, blink and diagonal eye motions which contain rolling up-left, rolling up-right, rolling down-left, rolling down-right (the angle of the diagonal motion is 45°) and blink string of three times motion. 8 directions motions correspond to 8 directions cursor movement in this system. We regard blink motion as invalid signal and define blink string motions as double click action. Using this system we have obtained a high recognition accuracy of eye motions (The average correct detection rate on each subject was 97.8%, 97.6% and 92.7%). This EOG Mouse interface would be used as a means of communication to help those patients as ALS.