Event detection and classification from multimodal time series with application to neural data.

The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series.

Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making.

When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time.

A point-process matched filter for event detection and decoding from population spike trains.

Objective: Information encoding in neurons can be described through their response fields. The spatial response field of a neuron is the region of space in which a sensory stimulus or a behavioral event causes that neuron to fire. Neurons can also exhibit temporal response fields (TRFs), which characterize a transient response to stimulus or behavioral event onsets.

Decoding Spike Trains from Neurons with Spatio-Temporal Receptive Fields.

The point-process filter (PPF) is a real-time recursive algorithm that computes the minimum mean-squared error estimate of a behavioral state, given neural spiking observations. When used with stimulus-sensitive neurons that represent behavioral states transiently, the PPF needs to know the times at which stimuli will occur. However, these times will not be known a-priori.

Multi-Contrast Imaging and Digital Refocusing on a Mobile Microscope with a Domed LED Array.

We demonstrate the design and application of an add-on device for improving the diagnostic and research capabilities of CellScope--a low-cost, smartphone-based point-of-care microscope. We replace the single LED illumination of the original CellScope with a programmable domed LED array. By leveraging recent advances in computational illumination, this new device enables simultaneous multi-contrast imaging with brightfield, darkfield, and phase imaging modes.