Classifying blinking and winking EOG signals using statistical analysis and LSTM algorithm

Abstract

Abstract Detection of eye movement types whether the movement of the eye itself or blinking has attracted a lot of recent research. In this paper, one method to detect the type of wink or blink produced by the eye is scrutinized and another method is proposed. We discuss what statistical analysis can teach us about detection of eye movement and propose a method based on long short-term memory (LSTM) networks to detect those types. The statistical analysis is composed of two main steps, namely calculation of the first derivative followed by a digitization step. According to the values of the digitized curve and the duration of the signal, the type of the signal is detected. The success rate reached 86.6% in detection of the movement of the eye when those volunteers are not trained on using our system. However, when they are trained, the detection success rate reached 93.3%. The statistical analysis succeeds in achieving detection of all types of eye movement except one type which is the non-intentional blinking. Although rate of success achieved is high, but as the number of people using this system increases, the error in detection increases that is because it is fixed and not adaptive to changes. However; we learnt from statistical analysis that the first derivative is a very important feature to classify the type of an EOG signal. Next, we propose using the LSTM network to classify EOG signals. The effect of using the first derivative as a feature for identifying the type of EOG signals is discussed. The LSTM algorithm succeeds in detecting the type of EOG signals with a percentage equal to 92% for all types of eye movement.

Keywords

• Electrooculography signal
• Human computer interface
• Signal duration
• Eye muscle motion
• Feature extraction