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- W4285336399 abstract "Meditation has been shown as an efficient way to promote human well-being. Most studies focused on meditation in sitting posture. However, meditation in walking posture was rarely studied. In order to identify these two meditation states (i.e., sitting and walking), we proposed a classification framework by leveraging different features extracted from the EEG signals and the random forest classifier. This study first investigated different single-modal features, including original power, power ratio, and non-linear dynamics. Further, we also concatenated all the single-modal features into a multi-modal feature. The experimental results show that the original power feature is better than the non-linear dynamics feature in meditation state classification. Moreover, the multi-modal feature outperforms all the single-modal features and can identify sitting and walking meditation with high accuracy." @default.
- W4285336399 created "2022-07-14" @default.
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- W4285336399 date "2021-11-01" @default.
- W4285336399 modified "2023-09-29" @default.
- W4285336399 title "Feature Representation for Meditation State Classification in EEG Signal" @default.
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- W4285336399 doi "https://doi.org/10.1109/itme53901.2021.00062" @default.
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