Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 2464 - 2473
• Main Authors: Chang, Yu-Cheng, Wang, Yu-Kai, Pal, Nikhil R., Lin, Chin-Teng
• Format: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automobile Driving; Brain; Brain modeling; brain-computer interfaces (BCI); Cluster Analysis; Clustering; Clustering algorithms; Cognitive ability; EEG; Electroencephalography; Feature extraction; Firing; Fuzzy Logic; Fuzzy neural networks; Human factors; Human performance; Humans; Inference; Mental Processes; Neural networks; state estimation; Task analysis; Transition probabilities; Uncertainty
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2021.3126264

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.