期刊
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
卷 30, 期 6, 页码 -出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065720500331
关键词
Longitudinal covert attention training; EEG; machine learning classification; latency features
资金
- KU Leuven [C24/18/098]
- European Union's Horizon 2020 research and innovation programme [857375]
- Belgian Fund for Scientific Research - Flanders [G088314N, G0A0914N, G0A4118N]
- Interuniversity Attraction Poles Programme - Belgian Science Policy [IUAP P7/11]
- Hercules Foundation [AKUL 043]
- [PFV/10/008]
Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are increasingly adopted to classify single-trial EEG responses thereby primarily relying on amplitude-based features instead of latency-based features. In this study, we investigated changes in EEG response signatures of nine healthy older subjects when performing 10 sessions of covert attention training. We show that, when we trained classifiers to distinguish recorded EEC patterns between the two experimental conditions (a target stimulus is present or not present), latency-based classifiers outperform the amplitude-based ones and that classification accuracy improved along with behavioral accuracy, providing supportive evidence of brain plasticity.
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