Identifying patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related ERP.
- Authors
- Li, Xiaoou; Yan, Yuning; Wei, Wenshi
- Year
- 2013
- Journal
- Computational and mathematical methods in medicine
- PMID
- 24233152
- DOI
- 10.1155/2013/658501
- PMCID
- PMC3819888
The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76% and 82.23% for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function.
ICA-OEMD.
EMK-SVM.
The general diagram of the proposed methodology.
N-back task timeline and electrode positions. Here trial time sequences for 0-back and 1-back conditions. Black squares represent each stimulus in the task. The symbol ∗ stands for the target number during each trial. Five brain regions: frontocentral (FCentral): FP1, FP2, F7, F3, Fz, F4, and F8; left sensorimotor (LSM): C3 and P3; central: Cz and Pz; right sensorimotor (RSM): C4 and P4; and occipital: O1 and O2.
Task behavior performances with hit rate and reaction time. Here HC represents healthy control and SP represents stroke patient.
Accuracy comparisons of 0-back task classification.
Accuracy comparisons of 1-back task classification.
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In this knowledge base
| Title | Year | PMID |
|---|---|---|
| Advances in Electrophysiological Research. | 2015 | 26259089 |
External
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|---|---|---|---|---|
| Spatial Cognitive EEG Feature Extraction and Classification Based on MSSECNN and PCMI. | Wan X et al. | — | 2024 | → |
| Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network. | Wen D et al. | — | 2022 | → |
| Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information. | Liu Y et al. | — | 2021 | → |
| Feature Classification Method of Resting-State EEG Signals From Amnestic Mild Cognitive Impairment With Type 2 Diabetes Mellitus Based on Multi-View Convolutional Neural Network. | Wen D et al. | — | 2020 | → |
| Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network. | Wen D et al. | — | 2020 | → |
| The Current Research of Spatial Cognitive Evaluation and Training With Brain-Computer Interface and Virtual Reality. | Zhou Y et al. | — | 2019 | → |
| Advances in Electrophysiological Research. | Kamarajan C et al. | — | 2015 | → |