(2017). Task-Independent Mental Workload Classification Based Upon Common Multiband EEG Cortical Connectivity. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(11), 1940–1949. https://doi.org/10.1109/tnsre.2017.2701002
Περίληψη
Efficient classification of mental workload, an important issue in neuroscience, is limited, so far to single task, while cross-task classification remains a challenge. Furthermore, network approaches have emerged as a promising direction for studying the complex organization of the brain, enabling easier interpretation of various mental states. In this paper, using two mental tasks (N-back and mental arithmetic), we present a framework for cross- as well as within-task workload discrimination by utilizing multiband electroencephalography (EEG) cortical brain connectivity. In detail, we constructed functional networks in EEG source space in different frequency bands and considering the individual functional connections as classification features, we identified salient feature subsets based on a sequential feature selection algorithm. These connectivity subsets were able to provide accuracy of 87% for cross-task, 88% for N-back task, and 86% for mental arithmetic task. In conclusion, our method achieved to detect a small number of discriminative interactions among brain areas, leading to high accuracy in both within-task and cross-task classifications. In addition, the identified functional connectivity features, the majority of which were detected in frontal areas in theta and beta frequency bands, helped delineate the shared as well as the distinct neural mechanisms of the two mental tasks.
- DOI
- 10.1109/tnsre.2017.2701002
- Τύπος
- Άρθρο σε Περιοδικό
- Έτος
- 2017
Σύνδεσμοι
BibTeX
@article{dimitrakopoulos2017task,
title = {Task-Independent Mental Workload Classification Based Upon Common Multiband EEG Cortical Connectivity},
author = {Georgios N. Dimitrakopoulos and Ioannis Kakkos and Zhongxiang Dai and Julian Lim and Joshua Souza and Anastasios Bezerianos and Yu Sun},
url = {https://doi.org/10.1109/tnsre.2017.2701002},
doi = {10.1109/tnsre.2017.2701002},
year = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
volume = {25},
number = {11},
pages = {1940–1949},
publisher = {Institute of Electrical and Electronics Engineers},
abstract = {Efficient classification of mental workload, an important issue in neuroscience, is limited, so far to single task, while cross-task classification remains a challenge. Furthermore, network approaches have emerged as a promising direction for studying the complex organization of the brain, enabling easier interpretation of various mental states. In this paper, using two mental tasks (N-back and mental arithmetic), we present a framework for cross- as well as within-task workload discrimination by utilizing multiband electroencephalography (EEG) cortical brain connectivity. In detail, we constructed functional networks in EEG source space in different frequency bands and considering the individual functional connections as classification features, we identified salient feature subsets based on a sequential feature selection algorithm. These connectivity subsets were able to provide accuracy of 87% for cross-task, 88% for N-back task, and 86% for mental arithmetic task. In conclusion, our method achieved to detect a small number of discriminative interactions among brain areas, leading to high accuracy in both within-task and cross-task classifications. In addition, the identified functional connectivity features, the majority of which were detected in frontal areas in theta and beta frequency bands, helped delineate the shared as well as the distinct neural mechanisms of the two mental tasks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}