most citedUniversal Joint Feature Extraction for P300 EEG Classification using Multi-task Autoencoder

94 citations · 94 across the 1 of their papers we have counts for

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eess.SP2018★ 94 cited

Universal Joint Feature Extraction for P300 EEG Classification using Multi-task Autoencoder

Apiwat Ditthapron, Nannapas Banluesombatkul, Sombat Ketrat +2

The process of recording Electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this work, we propose the…