# EEG based Mental Workload Assessment by Power Spectral Density Feature

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/eeg-based-mental-workload-assessment-by-power-spectral-density-feature/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Yang Liu,Shanshan Shi,Yu Song,Qiang Gao,Zeyu Li,Haotian Song,Siyuan Pang,Dong Li |
| Citations | 8 |
| DOI | 10.1109/icma54519.2022.9856376 |
| Fields | Engineering,Medicine,Neuroscience |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4292873486 |
| Type | conference-paper |
| Year | 2022 |

## Paper authors

- [Shanshan Shi](https://scholariq.org/researchers/shanshan-shi/)

## Paper journal

- [2022 IEEE International Conference on Mechatronics and Automation (ICMA)](https://scholariq.org/journals/2022-ieee-international-conference-on-mechatronics-and-automation-icma/)

## Paper primary topic

- [EEG and Brain-Computer Interfaces](https://scholariq.org/topics/eeg-and-brain-computer-interfaces/)

## Paper topics

- [EEG and Brain-Computer Interfaces](https://scholariq.org/topics/eeg-and-brain-computer-interfaces/)
- [Non-Invasive Vital Sign Monitoring](https://scholariq.org/topics/non-invasive-vital-sign-monitoring/)
- [Heart Rate Variability and Autonomic Control](https://scholariq.org/topics/heart-rate-variability-and-autonomic-control/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.