ABSTRACT
The phase information provided by the beat note between frequency combs and two continuous-wave lasers is used to extrapolate the phase evolution of comb modes found in a spectral region obtained via nonlinear broadening. This thereafter enables using interferogram self-correction to fully retrieve the coherence of a dual-comb beat note between two independent fiber lasers. This approach allows the $ f - 2f $f-2f self-referencing of both combs, which is a significant simplification. Broadband near-infrared methane spectroscopy has been conducted to demonstrate the simplified system's preserved performance.
ABSTRACT
The present study demonstrates the application of the Unsupervised Spike Sorting algorithm (USS) to separation of multi-unit recordings and investigation of neuronal activity patterns in the subthalamic nucleus (STN). This nucleus is the main target for deep brain stimulation (DBS) in Parkinsonian patients. The USS comprises a fast unsupervised learning procedure and allows sorting of multiple single units, if any, out of a bioelectric signal. The algorithm was tested on a simulated signal with different levels of noise and with application of Time and Spatial Adaptation (TSA) algorithm for denoising. The results of the test showed a good quality of spike separation and allow its application to investigation of neuronal activity patterns in a medical application. One hundred twenty-four single channel multi-unit records from STN of 6 Parkinsonian patients were separated with USS into 492 single unit trains. Auto- and crosscorrellograms for each unit were analyzed in order to reveal oscillatory, bursting and synchronized activity patterns. We analyzed separately two brain hemispheres. For each hemisphere the percentage of units of each activity pattern were calculated. The results were compared for the first and the second operated hemispheres of each patient and in total.
