TY - JOUR T1 - Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features. JF - Neural Netw Y1 - 2009 A1 - Gunduz, Aysegul A1 - Sanchez, Justin C A1 - Carney, Paul R A1 - Principe, Jose KW - Algorithms KW - Brain KW - Brain Mapping KW - Electrodes, Implanted KW - Electrodiagnosis KW - Epilepsy KW - Feasibility Studies KW - Hand KW - Humans KW - Linear Models KW - Motor Activity KW - Neural Networks (Computer) KW - Nonlinear Dynamics KW - Signal Processing, Computer-Assisted AB -

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' 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.

VL - 22 UR - http://www.ncbi.nlm.nih.gov/pubmed/19647981 IS - 9 ER - TY - JOUR T1 - Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics. JF - J Neurosci Methods Y1 - 2008 A1 - Sanchez, Justin C A1 - Gunduz, Aysegul A1 - Carney, Paul R A1 - Principe, Jose KW - Adolescent KW - Biofeedback, Psychology KW - Brain Mapping KW - Cerebral Cortex KW - Electroencephalography KW - Epilepsies, Partial KW - Female KW - Hand KW - Humans KW - Magnetic Resonance Imaging KW - Physical Therapy Modalities KW - Psychomotor Performance KW - Signal Processing, Computer-Assisted KW - Spectrum Analysis KW - User-Computer Interface AB -

Electrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz) ECoG from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.

VL - 167 UR - http://www.ncbi.nlm.nih.gov/pubmed/17582507 IS - 1 ER -