Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis
The early prediction of Alzheimer’s disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MS...
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MDPI AG
2024-12-01
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| author | Ziyang Li Hong Wang Jianing Song Jiale Gong |
| author_facet | Ziyang Li Hong Wang Jianing Song Jiale Gong |
| author_sort | Ziyang Li |
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| description | The early prediction of Alzheimer’s disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time–frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. Subspace features (F24 and F216) were selected via <i>t</i>-tests and False Discovery Rate (FDR) multiple comparisons correction, and subsequently analyzed in the Time–Frequency Area Average Test (TFAAT) and Prefrontal Beta Time Series Test (PBTST). The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. Similarly, the Sternberg Memory Task demonstrates classification ability with the logistic regression model applied to the PBTST feature set, emphasizing the beta band power spectrum in the prefrontal cortex as a potential marker of AD risk. These findings confirm that task-state EEG provides stronger classification potential compared to resting-state EEG, offering valuable insights for advancing early AD prediction research. |
| format | Article |
| id | doaj-art-b9f7514456cc4bca9c368dc40cde83fe |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-b9f7514456cc4bca9c368dc40cde83fe2025-01-10T13:20:42ZengMDPI AGSensors1424-82202024-12-012515210.3390/s25010052Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral AnalysisZiyang Li0Hong Wang1Jianing Song2Jiale Gong3Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaThe early prediction of Alzheimer’s disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time–frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. Subspace features (F24 and F216) were selected via <i>t</i>-tests and False Discovery Rate (FDR) multiple comparisons correction, and subsequently analyzed in the Time–Frequency Area Average Test (TFAAT) and Prefrontal Beta Time Series Test (PBTST). The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. Similarly, the Sternberg Memory Task demonstrates classification ability with the logistic regression model applied to the PBTST feature set, emphasizing the beta band power spectrum in the prefrontal cortex as a potential marker of AD risk. These findings confirm that task-state EEG provides stronger classification potential compared to resting-state EEG, offering valuable insights for advancing early AD prediction research.https://www.mdpi.com/1424-8220/25/1/52AD detectioncross-subjecttask-state EEGmultitapermachine learning |
| spellingShingle | Ziyang Li Hong Wang Jianing Song Jiale Gong Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis Sensors AD detection cross-subject task-state EEG multitaper machine learning |
| title | Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis |
| title_full | Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis |
| title_fullStr | Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis |
| title_full_unstemmed | Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis |
| title_short | Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis |
| title_sort | exploring task related eeg for cross subject early alzheimer s disease susceptibility prediction in middle aged adults using multitaper spectral analysis |
| topic | AD detection cross-subject task-state EEG multitaper machine learning |
| url | https://www.mdpi.com/1424-8220/25/1/52 |
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