Flexible Microelectrode Arrays

Sensing brain activity with microelectrode arrays is one of the enablers of BMIs. This virtual company product is a flexible microelectrode array that can be fabricated in batch mode using special combination of materials and micromachining techniques and can be easily integrated with silicon technology.

• Dr. Nishida Toshi
• Neuroprosthetics Research Group (NRG) lead by Dr. Justin C. Sanchez

Integrate and Fire for remote sensing

TBF

• David Cheney, Aik Goh, Jie Xu, Karl Gugel, John G. Harris, Justin C. Sanchez, Jose C. Principe, Wireless, In Vivo Neural Recordings Using a Custom Integrated Bioamplifier andthe Pico System ", Neural Engineering Informatics", /home/users/www/cnel_docroot/files/1485291325.pdf, pp. 19-22, 5 2007

IF Analysis and Reconstruction

This project will characterize features of the IF sample representation such as data rates as a function of the input signal, and will develop on-line reconstruction algorithms in the back end.

Co-Adaptive BMIs

This project investigates closed-loop BMIs for reaching tasks in an animal model to develop new treatments for the disabled. Here, we focus on goal-based learning in motor BMIs to achieve control of a robotic arm. The approach is based on synergistic interaction between the user's neural modulations and an intelligent computational agent that are both seeking to maximize their own reward. For the development of BMIs, the RL framework provides a mechanism of learning that is very similar to operant conditioning of biological organisms because the learner (user) is not told what actions to take but must discover which actions yield the most reward by trying them.

• DiGiovanna J., Mahmoudi B., Fortes J., Principe J., Sanchez J., Co-adaptive Brain Machine Interface via Reinforcement Learning , Trans. on Biomedical Engineering, Vol. 54, No. 64, pp. 56-1, Jan 2009

• Neuroprosthetics Research Group (NRG) lead by Dr. Justin C. Sanchez

Cyberworkstation

TBF

• ACIS (Advanced Computing and Information Systems Laboratory

Monte Carlo Sequential Estimation for neural data analysis

This project seeks to evaluate the performance of the most general sequential estimation methodology for spike train analysis. We are using Monte Carlo techniques to lift the assumptions of Gaussianity and linearity in Bayesian filtering. The method requires accurate models of neural tuning functions, and indeed performs better than the rate models and Kalman filters in point processes.

• Y. Wang, A. Paiva, J. Principe, A Monte Carlo Sequential Estimation for Point Process Optimum Filtering ", Intl. Joint Conf. on Neural Networks", /home/users/www/cnel_docroot/files/1485291325.pdf, pp. 1846-1850, 7 2006

• Neuroprosthetics Research Group (NRG) lead by Dr. Justin C. Sanchez

Markov Chain Modeling for BMIs

The goal of this project is to use the theory of Markov models to create unsupervised algorithms that segment the multielectrode array data collected from motor cortex neurons during behavioral tasks. We are creating a graphical model with hidden dependencies to self-organize in space and in time the spike activity.

• * Darmanjian S., Principe J., Boosted and Linked Mixtures of HMMs for Brain Machine Interfaces, in European J. of Signal Processing, Special Issue on Machine Learning for Signal Processing, 2008
• Neuroprosthetics Research Group (NRG) lead by Dr. Justin C. Sanchez

Evaluation of ECoG for motor BMIs

TBF

• * Sanchez J., Gunduz A., Carney P., and J. Principe, “Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics,” in Journal of Neuroscience Methods – Special Issue on BCI, Volume 167, Issue 1, Pages 1-126, 2008
• Neuroprosthetics Research Group (NRG) lead by Dr. Justin C. Sanchez

Single Event ERP quantification

The electroencephalogram (EEG) is non-invasive and therefore it is the most likely candidate for the general use of brain activity to control external devices. The issue is the low signal to noise ratio and the nonspecific information carried by the EEG. Most often averaging is used to increase the SNR as done in event related potentials for BMIs. Here we develop a new single ERP algorithm that takes advantage of the massive number of electrodes of the current EEG systems to improve SNR, together with a generative model of the data to extract latencies and amplitudes of the ERP components (e.g. N100, P300). Preliminary results show that it is possible to quantify single ERP components in negative SNR (down to -15 dB) with reasonable accuracy. This project is conducted in cooperation with the NIH Center for Emotion and Attention at the UF.

• * Li R., Principe J., Bradley M., Ferrari V., “A novel spatiotemporal filtering methodology for single-trial ERP estimation”, accepted in IEEE Trans. Biomedical Eng., 2008
• CSEA (Center for the Study of Emotion and Attention)

A RKHS for spike train analysis

One of the theoretical issues when working with spike data is the point process nature of the signal, which is very different from conventional time series, where the information is contained in the amplitude of the signals. Point processes carry the information in the time occurrence of the pulses. Normally point processes are studied as stochastic signals, but we are developing a functional analysis approach based on reproducing kernel Hilbert spaces (RKHS). We have defined bottom up a RKHS for the intensity function of PP, and have proposed kernels with and without memory that are applicable not only to Poisson but also renewal processes. The advantage of the RKHS formalism is that it creates an Hilbert space where conventional signal processing algorithms based on inner products can be readily developed and applied to point processes.

Paiva A., Park I., Principe J., "Reproducing Kernel Hilbert Spaces for Spike Train Analysis", in Proc. IEEE ICASSP 2008, Las Vegas.

Hybrid (biological-silicon) Liquid State Machines

Dissociated Cortical Tissue (DCTs) on MicroElectrode Arrays (MEAs) offer the possibility of studying the excitability of neural tissue in vitro. Our goal is to develop a hybrid (biological/silicon) computer based on the Liquid State machine (LSM) paradigm. Here the liquid is the DCT and the readout is a traditional computer. We have studied stimulation strategies of DCTs in MEAs to allow the coding of input signals into the firing of the DCT. We were able to show that the property of separation is indeed verified in DCTs and were able to create hybrid LSM classifiers. However, due to the stimulation artifacts and the periodic bursting present in DCTs, processing of time series is still not possible.

• Dr. DeMarse's Lab