Engagement monitoring is crucial in many clinical and therapy applications such as early learning preschool classes for children with developmental delays including autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), or cerebral palsy; as it is challenging for the instructors to evaluate the individual responses of these children to determine the effectiveness of the teaching strategies due to the diverse and unique need of each child who might have difficulty in verbal or behavioral communication. This paper presents an ambulatory scalp electroencephalogram (EEG) NeuroMonitor platform to study brain engagement activities in natural settings. The developed platform is miniature (size: 2.2” x 0.8” x 0.36”, weight: 41.8 gm with 800 mAh Li-ion battery and 3 snap leads) and low-power (active mode: 32 mA low power mode: under 5mA) with 2 channels (Fp1, Fp2) to record prefrontal cortex activities of the subject in natural settings while concealed within a headband. The signals from the electrodes are amplified with a low-power instrumentation amplifier; notch filtered (fc = 60Hz), then band-passed by a 2nd-order Chebyshev-I low-pass filter cascaded with a 2nd-order low-pass (fc = 125Hz). A PSoC ADC (16-bit, 256 sps) samples this filtered signal, and can either transmit it through a Class-2 Bluetooth transceiver to a remote station for real-time analysis or store it in a microSD card for offline processing. This platform is currently being evaluated to capture data in the classroom settings for engagement monitoring of children, aimed to study the effectiveness of various teaching strategies that will allow the development of personalized classroom curriculum for children with developmental delays.
Epilepsy affects 2.5 million people in the USA, 15% of which cannot be treated with traditional methods. Effective treatments require reliable prediction of seizures to increase their effectiveness and quality-of-life. Phase synchronization phenomenon of two distant neuron populations for a short period of time just prior to a seizure episode is utilized for such prediction. This paper presents a hardware efficient prediction algorithm using phase-difference (PD) method instead of the commonly used phase-locking statistics (PLS). The dataset has been collected from publicly available “CHB-MIT Scalp EEG Database” that consists of scalp EEG recordings from 10 pediatric subjects with intractable seizures. The seizure channel is selected based on the maximum value of the standard deviation during seizure, while the reference channel has the minimum value of the standard deviation. Data from these two channels is conditioned with a band-pass (flc = 10Hz, fhc = 12.5Hz) 6th order Chebyshev Type II filter or a FIR filter. The analytical signals are derived using Hilbert Transform to allow phase extraction. PLS and PD are calculated from the mean of the phase-differences using an overlapping sliding-window technique. PD method demonstrates the same characteristics as PLS, while achieving 2.35 times faster computation rate in MATLAB than PLS. With 51 seizure episodes, prediction latency was between 51 seconds to 188 minutes with sensitivity of 88.2%. PD yields to lower hardware requirement and reduces computational complexity.
There is a need to model complementary aspects of various data channels in distributed sensor networks in order to
provide efficient tools of decision support in rapidly changing, dynamic real life scenarios. Our aim is to develop an
autonomous cyber-sensing system that supports decision support based on the integration of information from diverse
sensory channels. Target scenarios include dismounts performing various peaceful and/or potentially malicious
activities. The studied test bed includes Ku band high bandwidth radar for high resolution range data and K band low
bandwidth radar for high Doppler resolution data. We embed the physical sensor network in cyber network domain to
achieve robust and resilient operation in adversary conditions. We demonstrate the operation of the integrated sensor
system using artificial neural networks for the classification of human activities.