Name
Abstract | ||
|---|---|---|
Mental fatigue induced by long time mental work can cause deterioration in task performance and increase the risk of accidents. Recently, electroencephalogram (EEG)-based monitoring of mental fatigue has received increasing attention in the field of brain-computer interfaces (BCI). This study aims to employ EEG signals to measure the mental fatigue level by estimating reaction time (RT) in a psychomotor vigilance task (PVT). In a 36-hour sleep deprivation experiment, EEG data from 18 subjects were recorded every four hours in nine blocks, each consisting of three tasks: a 6-minute PVT task and two 3-minute resting states (eyes closed and eyes open). The mean RT in the PVT task showed a generally increasing trend during the 36-hour awake period, reflecting the increase of fatigue over time. For each task, multiple EEG features were extracted and selected to better estimate RT using a multiple linear regression (MLR) method. The correlation between predicted RT and actual RT was evaluated using a leave-one-subject-out (LOSO) validation strategy. After parameter optimization, EEG data from the PVT task obtained a mean correlation coefficient of $0.81 \pm 0.16$ across all subjects. Resting-state EEG data showed lower correlations (eyes-closed: $0.65 \pm 0.20$, eyes-open: $0.50 \pm 0.30)$ partially due to the involvement of shorter data lengths. These results demonstrate the feasibility and robustness of the EEG-based fatigue monitoring method, which could be potential for applications in operational environments. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8512666 | EMBC |
Field | DocType | Volume |
Computer vision,Psychomotor vigilance task,Task analysis,Computer science,Brain–computer interface,Sleep deprivation,Vigilance (psychology),Correlation,Artificial intelligence,Audiology,Electroencephalography,Linear regression | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sen Tian | 1 | 0 | 0.68 |
| Yijun Wang | 2 | 308 | 46.68 |
| Guoya Dong | 3 | 1 | 2.19 |
| Weihua Pei | 4 | 64 | 13.18 |
| Hongda Chen | 5 | 99 | 20.06 |