DeepFocus: Deep Encoding Brainwaves and Emotions with Multi-Scenario Behavior Analytics for Human Attention Enhancement

Abstract

Evaluation of the degree of attention has very important practical significance and application prospects in our lives. It plays considerably important functions in the fields of education, medical treatment, and automatic driving, and assists people in assessing psychological states automatically and gives early warning. However, there are some problems in the existing research, such as simplex data modality and simple modeling, which make the algorithm difficult to deploy in real circumstances. To solve these problems, a new system of attention degree evaluation, called DeepFocus, is put forward in this article. We propose an attention evaluation method based on multi-modal data and multi-scenario modeling for the first time. In addition, the relationship between emotional data and attention is analyzed in depth, and labels are corrected with emotional data. Based on the DeepFocus system, a completely new attention enhancement system is constructed, and the algorithm is deployed in a practical application for students to perceive and enhance their attention. It can be foreseen that our algorithm could explore people s inward world and assess users attention degree accurately and comprehensively to help people work, study, and live better with higher efficiency in the near future.