Simulation of a Real-Time Brain Computer Interface for Detecting a Self-Paced Hitting Task

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…Each electrode records by default extracellular de- and hyperpolarizations in the close vicinity of its nano-to-micrometer-wide tip (to a distance of about 140 μm in the case of a single wire electrode), and given the propagating nature of action potentials, these deflections would not only be APs assigned to a particular neuron, or single unit activity (SUA) but rather the spatio-temporal summation of a neural population in the close proximity, multi-unit activities (MUAs), and local field potentials (Hong and Lieber, 2019 ; Tambaro et al, 2021 ). Most spike sorting techniques discard local field potentials by simply high pass filtering data and concentrate on the spatial and temporal contexts of signal propagation (Abbott et al, 2020 ), although invasive brain-machine interface (BMI) systems could also possibly profit from this frequency range (Hammad et al, 2016 ).…”

“…Analyzing spike trains and spatiotemporal properties of extracellular AP waveforms provides us precious evidence of a cell's functional profile and morphology, including dendritic tree architecture, surrounding environment, and relative position of the recording site (Chaure et al, 2018 ; Rodriguez-Collado and Rueda, 2021 ; Soleymankhani and Shalchyan, 2021 ) and sheds light on the meticulously orchestrated functioning of neural networks (Leibig et al, 2016 ; Luan et al, 2018 ). Besides providing insight into brain activity at the highest temporal resolution currently available (Rey et al, 2015 ; Wouters et al, 2021 ), facilitating the “reverse-engineering” of the brain (Petrantonakis and Poirazi, 2017 ), extracellular APs are eagerly sourced in the development of brain-machine interfaces, too (Hammad et al, 2016 ).…”