Microprobe array with low impedance electrodes and highly flexible polyimide cables for acute neural recording

This paper reports on a novel type of silicon-based microprobes with linear, two and three dimensional (3D) distribution of their recording sites. The microprobes comprise either single shafts, combs with multiple shafts or 3D arrays combining two combs with 9, 36 or 72 recording sites, respectively. The electrical interconnection of the probes is achieved through highly flexible polyimide ribbon cables attached using the MicroFlex Technology which allows a connection part of small lateral dimensions. For an improved handling, probes can be secured by a protecting canula. Low-impedance electrodes are achieved by the deposition of platinum black. First in vivo experiments proved the capability to record single action potentials in the motor cortex from electrodes close to the tip as well as body electrodes along the shaft

Toward Automated Electrode Selection in the Electronic Depth Control Strategy for Multi-unit Recordings

Multi-electrode arrays contain an increasing number of electrodes. The manual selection of good quality signals among hundreds of electrodes becomes impracticable for experimental neuroscientists. This increases the need for an automated selection of electrodes containing good quality signals. To motivate the automated selection, three experimenters were asked to assign quality scores, taking one of four possible values, to recordings containing action potentials obtained from the monkey primary somatosensory cortex and the superior parietal lobule. Krippendorff’s alpha-reliability was then used to verify whether the scores, given by different experimenters, were in agreement. A Gaussian process classifier was used to automate the prediction of the signal quality using the scores of the different experimenters. Prediction accuracies of the Gaussian process classifier are about 80% when the quality scores of different experimenters are combined, through a median vote, to train the Gaussian process classifier. It was found that predictions based also on firing rate features are in closer agreement with the experimenters’ assignments than those based on the signal-to-noise ratio alone.status: publishe