Spike-feature based estimation of electrode position in extracellular neural recordings.

Detecting and sorting spikes in extracellular neural recordings are common procedures in assessing the activity of individual neurons. In chronic recordings, passive electrode movements introduce changes in the shape of detected spike waveforms, and may thus lead to problems with identification and tracking of spikes recorded at separate instances in time, which is an important step in long-term monitoring of individual neurons. Information about electrode movements after implantation is crucial to the evaluation of mechanical stability of different electrode designs. In this paper, we present a preliminary study of the relationship between electrode movements and the resulting movements of spike-features in feature space. We show that there is a characteristic relationship between the two movements and that this relationship can be modeled as a linear transformation between two coordinate systems. Finally, we show how the relationship can be used for estimating electrode positions based on measured spike waveforms without any prior knowledge about the type of neuron by introducing a learning procedure during electrode insertion.

Statistical modelling of spike libraries for simulation of extracellular recordings in the cerebellum.

Brain machine interfaces with chronically implanted microelectrode arrays for signal acquisition require algorithms for successful detection and classification of neural spikes. During the design of such algorithms, signals with a priori known characteristics need to be present. A common way to establish such signals is to model the recording environment, simulate the recordings and store ground truth about spiking activity for later comparison. In this paper, we present a statistical method to expand the spike libraries that are used in a previously presented simulation tool for the purpose described above. The method has been implemented and shown to successfully provide quick access to a large assembly of synthetic extracellular spikes with realistic characteristics. Simulations of extracellular recordings using synthesized spikes have shown to possess characteristics similar to those of in-vivo recordings in the cat cerebellum.

Spike library based simulator for extracellular single unit neuronal signals.

A well defined set of design criteria is of great importance in the process of designing brain machine interfaces (BMI) based on extracellular recordings with chronically implanted micro-electrode arrays in the central nervous system (CNS). In order to compare algorithms and evaluate their performance under various circumstances, ground truth about their input needs to be present. Obtaining ground truth from real data would require optimal algorithms to be used, given that those exist. This is not possible since it relies on the very algorithms that are to be evaluated. Using realistic models of the recording situation facilitates the simulation of extracellular recordings. The simulation gives access to a priori known signal characteristics such as spike times and identities. In this paper, we describe a simulator based on a library of spikes obtained from recordings in the cat cerebellum and observed statistics of neuronal behavior during spontaneous activity. The simulator has proved to be useful in the task of generating extracellular recordings with realistic background noise and known ground truth to use in the evaluation of algorithms for spike detection and sorting.

Implementation of a telemetry system for neurophysiological signals.

With an ever increasing need for assessment of neurophysiological activity in connection with injury and basic research, the demand for an efficient and reliable data acquisition system rises. Brain-machine interfaces is one class of such systems that targets the central nervous system. A necessary step in the development of a brain-machine interface is to design and implement a reliable and efficient measurement system for neurophysiological signals. The use of telemetric devices increases the flexibility of the devices in terms of subject mobility and unobtrusiveness of the equipment. In this paper, we present a complete system architecture for a wearable telemetry system for the acquisition of neurophysiological data. The system has been miniaturized and implemented using surface-mount technology. System performance has been successfully verified and bottlenecks in the architecture have been identified.