EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 1; pp. 43 - 51
• Main Authors: Fan, Jing, Wade, Joshua W., Key, Alexandra P., Warren, Zachary E., Sarkar, Nilanjan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adolescents; Affective computing; Algorithms; Autism; Autism Spectrum Disorder - physiopathology; Autism Spectrum Disorder - psychology; Autism Spectrum Disorder - rehabilitation; Automobile Driving; Bins; Boredom; Brain; Brain models; Classification; Computer applications; Computer simulation; Data acquisition; Driving ability; EEG; electroencephalogram; Electroencephalography; Electroencephalography - methods; Emotional behavior; Feature extraction; Female; Frequencies; Frustration; Human-computer interface; Humans; Implants; Male; mental workload recognition; Signal Processing, Computer-Assisted; Solid modeling; Training; Variable speed drives; Virtual environments; Virtual reality; virtual reality-based driving simulator; Workload; Workloads

Objective: To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods: Twenty adolescents with ASD participated in a six-session virtual reality driving simulator-bas...