Abstract: 

Autism Spectrum Disorder (ASD) is a “spectrum” neurological disorder causing different physical and cognitive disabilities. A major dilemma faced by ASD patients is the deregulated emotions causing certain, unpredicted, and instantaneous bursts of negative emotions. These negative emotion outbursts (NEOB) cause severe self-injuries and are a major hurdle for the treatment and rehabilitation of ASD patients. The unavailability of biomarkers for early prediction along with the life-long nature of the disorder requires life-long medical care and assistance for ASD patients. A physical or cognitive assistance system that can ease the severe difficulties faced by ASD children due to these certain NEOBs is direly required. 

This Ph.D. thesis has targeted the early prediction of NEOB for ASD children. Early prediction can help the parents and caregivers control and regularize their emotions. I have proposed and developed a wearable system-on-chip (SoC) based digital back-end (DBE) processor for negative emotion and NEOB prediction using Electroencephalogram (EEG) signals. The feasibility of the wearable SoC processor is highly dependent on the size (area), battery life (energy), number of EEG electrodes, scalp location of electrodes for the patient’s comfort, classification results, and the validation of the processor. A miniaturized, low-power SoC processor with a limited number of electrodes can be embedded in a headband as a patch sensor for continuous (24/7) prediction of NEOB/negative emotions. I have proposed and developed two (1st generation and 2nd generation) SoC-based DBE processors for the NEOB prediction. The identification of the most suitable channels and features for emotion prediction is an important challenge for researchers. I have also performed an extensive large-scale feature extraction using multiple benchmark emotions prediction data sets to identify the most suitable channels and features.

The 1st generation processor (DBE1) and 2nd generation processor (DBE2) provide negative emotions prediction with 73.4% and 85.4% classification accuracy respectively. The DBE processors were designed and developed using a 180nm CMOS process. The DBE1 processor was implemented using only eight EEG channels, whereas DBE2 was implemented using only two (minimum) channels. The DBE1 and DBE2 utilize an area of 5.4 mm2 and 16 mm2 respectively. The energy utilization of DBE1 and DBE2 is 16μJ and 10.13μJ per prediction respectively. The temporal and frontal location EEG electrodes are used in both DBE processors. ASD children are highly sensitive. Therefore, the location of the EEG electrodes is very important. The temporal and frontal locations cause minimum discomfort for the patients. Both DBE processors were validated using SEED and DEAP emotion prediction data sets.

List of Publications: