An 8 Channel Patient Specific Neuromorphic Processor for the Early Screening of Autistic Children through Emotion Detection

Autism Spectrum Disorder (ASD) is a neurodevelopment disorder that affects children's development and can lead to handicap life if remain untreated. Scalp Electroencephalography (EEG) data can be used as a biomarker to characterize the human emotions on the valence-arousal scale. This work presents a machine learning patient-specific emotion detection (PSED) classification processor based on an eight-channel EEG signal. The proposed PSED classification processor integrates a hardware-efficient feature extraction engine and patient-specific support vector machine (SVM) classifier to discriminate the emotions in real-time. To utilize minimal hardware resources a hardware realizable feature set comprising of power spectral density (PSD), an absolute difference of inter-hemispheric power asymmetry (IHPD), and the scaled inter-hemispheric power asymmetry ratio (SIHPR) of eight electrode pairs are evaluated. To avoid high overhead of area and power consumption for an integer divider for SIHPR; simple LUT based divider is proposed that calculates the approximated value of SIHPR with a minimal overhead of 64 Bytes. The classification is performed using a Linear SVM and resulted in an accuracy of 63% and 60% for valence and arousal, respectively, based on the database for emotion analysis using physiological signals (DEAP). The PSED processor is synthesized using a 65nm CMOS technology with an overall energy efficiency of 10uJ/classification.