Spatio-Temporal Detection of Divided Attention in Reading Applications Using EEG and Eye Tracking

Reading is central to learning and communicating, however, divided attention in the form of distraction may be present in learning environments, resulting in a limited understanding of the reading material. This paper presents a novel system that can spatio-temporally detect divided attention in users during two different reading applications: typical document reading and speed reading. Eye tracking and electroencephalogra-phy (EEG) monitor the user during reading and provide a classifier with data to decide the user’s attention state. The multimodal data informs the system where the user was dis-tracted spatially in the user interface and when the user was distracted. Classification was evaluated with two exploratory experiments. The first experiment was designed to divide the user’s attention with a multitasking scenario. The second ex-periment was designed to divide the users attention by sim-ulating a real-world scenario where the reader is interrupted by unpredictable audio distractions. Results from both exper-iments show that divided attention may be detected spatio-temporally well above chance on a single-trial basis. Author Keywords Attention classification; EEG; eye tracking; learning tools