A New Spike Sorting Algorithm Based on Continuous Wavelet Transform and Investigating Its Effect on Improving Neural Decoding Accuracy

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

“…Sensor types can play an additional role in compression. Metal oxide memristive integrative sensors record and compress information in parallel (Gupta et al, 2016 ), while neuromorphic sensors are capable of event-driven recording and transmission, therefore improving temporal precision and reducing power consumption and data bandwidth (Liu et al, 2018 ; Soleymankhani and Shalchyan, 2021 ).…”

“…This gain of waveform information is called feature extraction, where only the most critical elements, the so-called principal components, are retained for further assessment (Ravikumar, 2021 ), which means that later, dimensionality reduction also takes place (Mahallati et al, 2019 ). For maximizing spike sorting accuracy, it is reasonable to choose principal components wisely, preferably ones that are noise-independent (Soleymankhani and Shalchyan, 2021 ) and are distinctly discriminative (Lefebvre et al, 2016 ; Zamani et al, 2020b ) but cheap at implementation (Zamani et al, 2020a ); by this means, we can map neural data in an informative but lower dimensional space. There is also a difference between first (waveform amplitude) and second (slope of the waveform) principal components (Navratilova et al, 2016 ).…”

“…There is another dissimilar branch of methods that prioritizes cutting back on hardware complexity and template matching (Laboy-Juárez et al, 2019 ) that is an increasingly popular alternative for clustering. Discrete derivatives (Zamani et al, 2018 ) or optimal wavelet transforms (Yang and Mason, 2017 ; Soleymankhani and Shalchyan, 2021 ), which are sub-band selective, can stand for filtering as well (Soleymankhani and Shalchyan, 2021 ), whereas zero crossing features (Oh et al, 2017 ) or first and second derivative spike features (Caro-Martín et al, 2018 ) are methods that can tackle this condition. These methods are concentrated on global features gripping waveform morphology similarities of action potentials, but local feature extraction, e.g., Laplacian eigenmaps, could constitute another strategy as well (Chah et al, 2011 ; Huang et al, 2021 ).…”

“…Regarded as a foundation stone in neurophysiology, recorded extracellular APs (commonly referred to as spikes) are the fingerprints of single neurons' activities, an observation that has been fueling neuroscientific research for almost a century (Carlson and Carin, 2019 ; Zhang and Constandinou, 2021a ). 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 ).…”

Field programmable gate arrays implementation of different standard deviation estimation techniques

“…In the old systems, a fixed noise level was employed as a threshold in a variety of applications that needed to eliminate noise, but since noise levels are not constant and vary over time [7], [8], this technique becomes inapplicable. So, researchers concentrate on calculating an appropriate value for the threshold, with the aim of real-time estimate, acceptable power consumption, area occupancy, and accuracy.…”