Detecting the Early Drop of Attention using EEG Signal - Fergyanto E Gunawan; Krisantus Wanandi; Benfano Soewito; Sevenpri Candra (Proceeding of the Electrical Engineering Computer Science and Informatics, 2017)
The capability   to detect the drop of attention as early as possible has many practical applications including for the development of the early warning system for those who involve in high-risk works that  require a constant level of concentration. This study intends to  develop such the capability on the basis of the data of the brain   waves: delta, theta, alpha, beta, and gamma. For the purpose, a number of participants are asked  to participate in the study where their  brain waves are recorded by using a low-cost Neurosky Mindwave EEG sensor. In the process, the  participants are performing a continuous performance test from which their attention levels are directly measured in  the form of the response time in conjunction to those waves. When the response time is much longer than  a normal one, the participant attention is assumed  to be dropped. A simple k-NN classification method is used with the k = 3. The results are the following. The best detection of the attention drop is achieved when  the attention features are extracted   from the earliest stage of the brain wave signals. The brain wave signal should be  recorded longer than 1 s since the time the stimulus is presented as a short signal  leads to a poor categorization. A significant drop in the level of response time is required to provide the brain signal that better predicts the change of the attention.