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- W4205228134 abstract "EEG signals have shown their potential for creating a new way of robust subject identification and authentication. The portability of such EEG device will be crucial for its fast adoption in the market. To achieve this, it will be necessary to design a device with the minimum required EEG channels and the best possible method for feature extraction. We analyzed three different methods for feature extraction and channels reduction, creating thus, the necessary knowledge for training machine-learning-based models. The methods were tested in a population of 40 subjects using EEG signals of resting-state from eight different tasks/sessions. The results obtained using multi-class classification have shown accuracies up to 1.00 with only two EEG channels, using different number of instances. These results show that subject identification using EEG signals from only a few electrodes is feasible, and this work offers a good starting point for further explorations using large datasets." @default.
- W4205228134 created "2022-01-26" @default.
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- W4205228134 date "2022-01-01" @default.
- W4205228134 modified "2023-10-12" @default.
- W4205228134 title "EEG-based subject identification with multi-class classification" @default.
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- W4205228134 doi "https://doi.org/10.1016/b978-0-12-820125-1.00027-0" @default.
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