Influence of task combination on EEG spectrum modulation for driver workload estimation.

Objective: This study investigates the feasibility of using a method based on electroencephalography (EEG) for deriving a driver's mental workload index.

Background: The psychophysiological signals provide sensitive information for human functional states assessment in both laboratory and real-world settings and for building a new communication channel between driver and vehicle that allows for driver workload monitoring.

Methods: An experiment combining a lane-change task and n-back task was conducted.

A data-driven algorithm for offline pupil signal preprocessing and eyeblink detection in low-speed eye-tracking protocols.

Event detection is the conversion of raw eye-tracking data into events--such as fixations, saccades, glissades, blinks, and so forth--that are relevant for researchers. In eye-tracking studies, event detection algorithms can have a serious impact on higher level analyses, although most studies do not accurately report their settings. We developed a data-driven eyeblink detection algorithm (Identification-Artifact Correction [I-AC]) for 50-Hz eye-tracking protocols.