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An electroencephalogram (EEG)-based non-invasive 2-channel neuro-feedback SoC is presented to predict and report negative emotion outbursts (NEOB) of Autistic patient. The SoC incorporates area-and-power efficient dual-channel Analog Front-End (AFE), and a deep neural network (DNN) emotion classification processor. The classification processor utilizes only the two-feature vector per channel to minimize the area and overfitting problems. The 4-layers customized DNN classification processor is integrated on-sensor to predict the NEOB. The AFE comprises two entirely shared EEG channels using sampling capacitors to reduce the area by 30%. Moreover, it achieves an overall integrated input-referred noise, NEF and crosstalk of 0.55VRMS, 2.71 and -79dB, respectively. The 16mm2 SoC is implemented in 0.18um 1P6M, CMOS process and consumes 10.13J/classification for 2 channel operation while achieving an average accuracy of >85% on multiple emotion databases and real-time testing.

Autism Spectrum Disorder (ASD) is the prevalent child neurological and developmental disorder causing cognitive and behavioral impairments. The early diagnosis is an urgent need for treatment and rehabilitation of ASD patients. This work presents an electroencephalogram (EEG) based ASD classification processor that targets a patch-form factor sensor that can be used for long time monitoring in a wearable environment. The selection of frontal and parietal lobe electrodes causes minimum uneasiness to the children. The proposed and implemented algorithm utilizes only four EEG electrodes. The processor is implemented and validated on Artix-7 FPGA which requires only 26K lookup tables and 15K flip flops. The hardware efficient implementation of the complex kurtosis value and Katz fractal dimension (KFD) features using kurtosis value indicator and KFD indicator with 54% and 38% efficient implementations, respectively, is provided. A hardware feasible shallow neural network architecture is used for the ASD classification. The system classifies the ASD with a high classification accuracy of 85.5% using the power and latency of $8.62 \mu \mathrm{W}$ and 2.25ms, respectively.

Autism spectrum disorder (ASD) is the chronic ailment of the central nervous system that causes the degradation of emotional and cognitive abilities. Long-term continuous monitoring with neurofeedback of human emotions for patients with ASD is crucial in mitigating its harmful effect. ASD is highly intimidating chronic disorder because of its sharp increase in the number of patients due to late detection. Lack of emotions’ regularization worsens their daily life by certain negative outbursts of emotions. These negative emotion outbursts cause self-injuries and degrade learning abilities. The timely emotion’s prediction can be used to regulate the emotions by controlling these outbursts. Noninvasive electroencephalogram (EEG) can be used for the emotion’s prediction using the valence and arousal scales. This work proposes a real-time wearable on-chip processor for the early prediction of the emotions. The miniaturized low-power processor can be embedded in a headband (patch sensor) for timely prediction of negative emotion’s outbursts. A linear support vector machine classifier is used with power spectral density, logarithmic interhemispheric power spectral ratio and the interhemispheric power spectral difference of eight EEG channel locations suitable for a wearable noninvasive classification system. A lookup table–based logarithmic division unit (LDU) is proposed to represent the division features in machine learning (ML) applications. The proposed LDU minimizes the cost of integer division by 34% for ML applications. The proposed emotion’s classification processor achieved accuracies of 72.96% and 73.14%, respectively, for the valence and arousal classification on multiple publicly available databases.

Long-term, continuous monitoring of human brain activity with closed-loop precise neurostimulation can potentially help to treat conditions such as epilepsy and Parkinson. These implantable devices are used to sense the brain signal, detect an abnormality, and stimulate once the abnormal activity is detected to mitigate the adverse effect. The target is to achieve the maximum efficacy while balancing the signal acquisition and intelligent processing to ensure minimize battery replacement frequency. This paper presents the design of the implantable Electrocorticography (ECoG) based system for intractable epileptic seizure treatment. The challenges, design choices, and trade-offs, related to the neurological disorder in the implantable environment are discussed. A multi-channel ultra-low-power instrumentation amplifier (IA) with digital electrode offset rejection loop (EORL) with a cutoff frequency of < 0.5Hz to mitigate the electrode offset effect with a fast settling of < 0.1 Sec to ensure real-time recording. The implantable system is realized in 180nm CMOS process to ensure area-and-power efficient design.

Chronic neurological disorders (CND's) are lifelong diseases and cannot be eradicated, but their severe effects can be alleviated by early preemptive measures. CND's, such as Alzheimer's, Autism Spectrum Disorder (ASD), and Amyotrophic Lateral Sclerosis (ALS), are the chronic ailment of the central nervous system that causes the degradation of emotional and cognitive abilities. Long term continuous monitoring with neuro-feedback of human emotions for patients with CND's is crucial in mitigating its harmful effect. This paper presents hardware efficient and dedicated human emotion classification processor for CND's. Scalp EEG is used for the emotion's classification using the valence and arousal scales. A linear support vector machine classifier is used with power spectral density, logarithmic interhemispheric power spectral ratio, and the interhemispheric power spectral difference of eight EEG channel locations suitable for a wearable non-invasive classification system. A look-up-table based logarithmic division unit (LDU) is to represent the division features in machine learning (ML) applications. The implemented LDU minimizes the cost of integer division by 34% for ML applications. The implemented emotion's classification processor achieved an accuracy of 72.96% and 73.14%, respectively, for the valence and arousal classification on multiple publicly available datasets. The 2 x 3mm2 processor is fabricated using a 0.18 μm 1P6M CMOS process with power and energy utilization of 2.04 mW and 16 μJ/classification, respectively, for 8-channel operation.

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.

This live demonstration showcases a noninvasive glucose monitoring device based on a single wavelength near-infrared (NIR) spectroscopy. The analog frontend record the Photoplethysmographic (PPG) signal from the fingertip. Ten discriminating features are extracted from the PPG signal to predict the blood glucose level using (Exponential Gaussian Process) machine learning regression. Visitors can easily insert their fingers into the finger-clip part of the system to measure their blood glucose level and display it on a mobile phone.

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.