@article {2100, title = {Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.}, journal = {Neural Netw}, volume = {22}, year = {2009}, month = {11/2009}, pages = {1257-70}, abstract = {

Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients{\textquoteright} hand trajectories. Traditionally, the power of ECoG rhythms (\<200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85\% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.

}, keywords = {Algorithms, Brain, Brain Mapping, Electrodes, Implanted, Electrodiagnosis, Epilepsy, Feasibility Studies, Hand, Humans, Linear Models, Motor Activity, Neural Networks (Computer), Nonlinear Dynamics, Signal Processing, Computer-Assisted}, issn = {1879-2782}, doi = {10.1016/j.neunet.2009.06.036}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19647981}, author = {Gunduz, Aysegul and Sanchez, Justin C and Carney, Paul R and Principe, Jose} } @article {2106, title = {Analysis of the correlation between local field potentials and neuronal firing rate in the motor cortex.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {1}, year = {2006}, month = {09/2006}, pages = {6185-8}, abstract = {

Neuronal firing rate has been the signal of choice for invasive motor\ brain\ machine\ interfaces\ (BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals.

}, keywords = {Action Potentials, Animals, Brain, Brain Mapping, Electric Stimulation, Electrodes, Evoked Potentials, Motor, Male, Models, Statistical, Motor Cortex, Neurons, Rats, Rats, Sprague-Dawley, Signal Processing, Computer-Assisted, Synaptic Transmission}, issn = {1557-170X}, doi = {10.1109/IEMBS.2006.260516}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17946745}, author = {Wang, Yiwen and Sanchez, Justin C and Principe, Jose and Mitzelfelt, Jeremiah D and Gunduz, Aysegul} }