Timothy G. Constandinou

This paper introduces a real-time mmWave radar framework for activity classification and tracking in dementia home monitoring, achieving high accuracy in detecting stationary, walking, and absent states.

This paper validates polymeric cuff electrodes for selective peripheral nerve fascicle stimulation and highlights the need for improved modeling to enhance simulation accuracy.

This study evaluates the long-term stability of silicone-encapsulated CMOS ICs in biofluid environments through 4.3 years of accelerated life testing, finding no insulation failures and limited failures at wire bonds.

This study evaluates the accuracy of ultra-wideband radar sensors in detecting gait asymmetry during overground walking, achieving high accuracy in step time symmetry ratio estimation.

The paper evaluates the long-term stability of implantable silicon-ICs, showing that PDMS coating prevents degradation while bare die ICs remain electrically stable for at least a year.

The paper identifies NOS1 neurons in the preoptic hypothalamus as sleep-active and shows they regulate NREM and REM sleep and body temperature.

This study uses deep learning to infer entire spiking activity from local field potentials in monkeys, showing higher accuracy than traditional methods.

This paper examines how varying the binning period of multi-unit activity affects BCI decoding performance in macaques, finding a small but significant decrease in performance with longer binning periods.

This paper develops a deep learning method for decoding hand kinematics from neural signals, achieving high accuracy and robustness over long-term recordings in non-human primates.

The paper evaluates how different referencing schemes affect LFP-based BMI decoding performance and finds that CAR with LMP features improves long-term decoding.

The paper identifies a circuit pathway in mice where sleep-deprived prefrontal cortex neurons trigger nesting and sleep via connections to the preoptic and hypothalamus.

The paper introduces BAKS, a Bayesian method for estimating neuronal firing rates from single trials, showing improved performance over existing methods on synthetic and real data.

This study characterizes the recovery dynamics of neural function after high-frequency alternating current (HFAC) nerve block, showing that recovery time and completeness depend on signal frequency and amplitude.