Abstract-Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially be used for control of arm prostheses. Restoring independent function to BCI users with such a system will likely require control of many degrees-offreedom (DOF). However, our ability to decode many-DOF arm movements from ECoG signals has not been thoroughly tested. To this end, we conducted a comprehensive study of the ECoG signals underlying 6 elementary upper extremity movements. Two subjects undergoing ECoG electrode grid implantation for epilepsy surgery evaluation participated in the study. For each task, their data were analyzed to design a decoding model to classify ECoG as idling or movement. The decoding models were found to be highly sensitive in detecting movement, but not specific in distinguishing between different movement types. Since sensitivity and specificity must be traded-off, these results imply that conventional ECoG grids may not provide sufficient resolution for decoding many-DOF upper extremity movements.

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Abstract-Four-sensor microelectrodes, commonly referred to as tetrodes, have the ability to significantly increase the signal-to-noise ratio of neuronal extracellular recordings. They also provide spatio-temporal information about extracellular action potentials (EAP) which may be used to localize and resolve individual neuronal signal sources. Since the relative position of sensors and neurons whose EAPs are recorded is not known during in vivo experiments, the accuracy and precision of neuronal source localization algorithms remain untested. In this study, electrical signals generated by a stimulator were recorded simultaneously with four recording micropipettes immersed in artificial cerebrospinal fluid. The location of the source was estimated using the multiple signal classification algorithm, with an accuracy and precision of ∼4 µm and ∼7 µm, respectively. These results suggest that in vivo localization and resolution of individual neuronal sources is feasible.

Multi-sensor microelectrodes for extracellular action potential recording have significantly improved the quality of in vivo recorded neuronal signals. These microelectrodes have also been instrumental in the localization of neuronal signal sources. However, existing neuron localization methods have been mostly utilized in vivo, where the true neuron location remains unknown. Therefore, these methods could not be experimentally validated. This article presents experimental validation of a method capable of estimating both the location and intensity of an electrical signal source. A four-sensor microelectrode (tetrode) immersed in a saline solution was used to record stimulus patterns at multiple intensity levels generated by a stimulating electrode. The location of the tetrode was varied with respect to the stimulator. The location and intensity of the stimulator were estimated using the Multiple Signal Classification (MUSIC) algorithm, and the results were quantified by comparison to the true values. The localization results, with an accuracy and precision of ∼10 µm, and ∼11 µm respectively, imply that MUSIC can resolve individual neuronal sources. Similarly, source intensity estimations indicate that this approach can track changes in signal amplitude over time. Together, these results suggest that MUSIC can be used to characterize neuronal signal sources in vivo.