A10.13uJ/classification 2-channel Deep Neural Network-based SoC for Emotion Detection of Autistic Children

Abstract

An EEG-based noninvasive neuro-feedback SoC for emotion classification of Autistic children is presented. The AFE comprises two entirely shared EEG-channels using sampling capacitors to reduce the area by 30% and achieve an overall integrated input-referred noise of 0.55µ VRMS with cross-talk of - 79dB. The 4-layers Deep Neural Network (DNN) classifier is integrated on-sensor to classify (4 emotions) with >85% accuracy. The 16mm2 SoC in 0.18um CMOS consumes 10.13µJ/classification for 2 channels.