Name
Title | ||
|---|---|---|
EEG-based discrimination of different cognitive workload levels from mental arithmetic. |
Abstract | ||
|---|---|---|
Cognitive workload, which is the level of mental effort required for a cognitive task, can be assessed by monitoring the changes in neurophysiological measures such as electroencephalogram (EEG). This study investigates the performance of an EEG-based Brain-Computer Interface (BCI) to discriminate different difficulty levels in performing a mental arithmetic task. EEG data from 10 subjects were collected while performing mental addition with 3 difficulty levels (easy, medium and hard). EEG features were then extracted using band power and Common Spatial Pattern features and subsequently features were selected using Fisher Ratio to train a Linear Discriminant Classifier. The results from 10-fold cross-validation yielded averaged accuracy of 90% for 2 classes (easy versus hard tasks) and 66% for 3 classes (easy versus medium versus hard tasks). Hence the results showed the feasibility of using EEG-based BCI to measure cognitive workload in performing mental arithmetic. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8512675 | EMBC |
Field | DocType | Volume |
Task analysis,Computer science,Brain–computer interface,Arithmetic,Feature extraction,Linear discriminant analysis,Statistical classification,Classifier (linguistics),Cognition,Electroencephalography | Conference | 2018 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Yang Chin | 1 | 82 | 5.82 |
| Xin Zhang | 2 | 218 | 89.32 |
| Chuanchu Wang | 3 | 93 | 17.16 |
| Kai Keng Ang | 4 | 804 | 64.19 |