(a) Overall fitness of the CLEEGN model across types of reference data using the ERN dataset. (b) Decoding performance of the CLEEGN-reconstructed EEG data (blue) and the corresponding reference data used for CLEEGN model training (red).

(a) Overall fitness of the CLEEGN model across types of reference data using the ERN dataset. (b) Decoding performance of the CLEEGN-reconstructed EEG data (blue) and the corresponding reference data used for CLEEGN model training (red).

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Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal quality susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact re...

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Context 1
... for the large amplitude artifacts that failed to be identified by ASR-32 and ASR-16, we can see that CLEEGN is able to mitigate those EOG artifacts. As shown in Figure 6(a), the reference data prepared by ICLabel offer the lowest MSE, i.e. the best fitness for CLEEGN training. We also compare the types of reference EEG data and their corresponding CLEEGN reconstruction results in the decoding performance of the ERN EEG dataset to assess their data quality. ...
Context 2
... also compare the types of reference EEG data and their corresponding CLEEGN reconstruction results in the decoding performance of the ERN EEG dataset to assess their data quality. As illustrated in Figure 6(b), CLEEGN reconstruction draws better performances for all denoising methods. Although the performance decreases with a smaller cutoff parameter k, CLEEGN can outperform the reference data used for its training. ...

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