Abstract
Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user’s mental state considered. However, in real-life situations, different aspects of the user’s state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI—for example both mental workload and stress level might be related to an aircraft pilot’s risk of error—and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.
Highlights
The main objective of this study was to investigate the feasibility of performing simultaneous classification of mental workload and stress level in an online passive brain–computer interfaces (BCI)
Our results showed that mental workload level (Easy vs. Difficult) and affective state (Relaxed vs. Stressed) could be classified in a manner suitable for online implementation with accuracies of 77.5% ± 6.9 and 84.1% ± 5.9, respectively, across 18 participants
We investigated the ability to classify both mental workload level and affective state simultaneously using methods appropriate for implementation in an online
Summary
Passive brain–computer interfaces (BCI) are systems that aim to monitor the mental state (either cognitive or affective) of a user and exploit this information to adapt an ongoing human–machine interaction in some useful way [1,2]. A passive BCI could improve road safety by first detecting states of extreme fatigue or drowsiness in the driver of a car or transport truck and using this information to initiate alarms or other safety measures to help avoid an accident. In passive BCI systems, information on the user’s state is extracted from neural signals collected using an appropriate functional imaging modality. Electroencephalography (EEG), which measures the electrical activity of the brain, is considered as perhaps the most promising modality given its portability, high temporal resolution, non-invasiveness, and relatively low cost [3]
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