Open AccessExploring Covert States of Brain Dynamics via Fuzzy Inference Encoding
Author(s) -
Yu-Cheng Chang,
Yu-Kai Wang,
Nikhil R. Pal,
Chin-Teng Lin
Publication year - 2021
Publication title -
ieee transactions on neural systems and rehabilitation engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.093
H-Index - 140
eISSN - 1558-0210
pISSN - 1534-4320
DOI - 10.1109/tnsre.2021.3126264
Subject(s) - bioengineering , computing and processing , robotics and control systems , signal processing and analysis , communication, networking and broadcast technologies
Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken.