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

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Investigating well potential parameters on neural spike enhancement in a stochastic-resonance pre-emphasis algorithm

Objective.Background noise experienced during extracellular neural recording limits the number of spikes that can be reliably detected, which ultimately limits the performance of next-generation neuroscientific work. In this study, we aim to utilize stochastic resonance (SR), a technique that can help identify weak signals in noisy environments, to enhance spike detectability.Approach.Previously, an SR-based pre-emphasis algorithm was proposed, where a particle inside a 1D potential well is exerted by a force defined by the extracellular recording, and the output is obtained as the displacement of the particle. In this study, we investigate how the well shape and damping status impact the output signal-to-noise ratio (SNR). We compare the overdamped and underdamped solutions of shallow- and steep-wall monostable wells and bistable wells in terms of SNR improvement using two synthetic datasets. Then, we assess the spike detection performance when thresholding is applied on the output of the well shape-damping status configuration giving the best SNR enhancement.Main results.The SNR depends on the well-shape and damping-status type as well as the input noise level. The underdamped solution of the shallow-wall monostable well can yield to more than four orders of magnitude greater SNR improvement compared to other configurations for low noise intensities. Using this configuration also results in better spike detection sensitivity and positive predictivity than the state-of-the-art spike detection algorithms for a public synthetic dataset. For larger noise intensities, the overdamped solution of the steep-wall monostable well provides better spike enhancement than the others.Significance.The dependence of SNR improvement on the input signal noise level can be used to design a detector with multiple outputs, each more sensitive to a certain distance from the electrode. Such a detector can potentially enhance the performance of a successive spike sorting stage.

EEG based emotion recognition using minimum spanning tree

Emotion is a fundamental factor that influences human cognition, motivation, decision making and social interactions. This psychological state arises spontaneously and goes with physiological changes that can be recognized by computational methods. In this study, changes in minimum spanning tree (MST) structure of brain functional connectome were used for emotion classification based on EEG data and the obtained results were employed for interpretation about the most informative frequency content of emotional states. For estimation of interaction between different brain regions, several connectivity metrics were applied and interactions were calculated in different frequency bands. Subsequently, the MST graph was extracted from the functional connectivity matrix and its features were used for emotion recognition. The results showed that the accuracy of the proposed method for separating emotions with different arousal levels was 88.28%, while for different valence levels it was 81.25%. Interestingly, the system performance for binary classification of emotions based on quadrants of arousal-valence space was also higher than 80%. The MST approach allowed us to study the change of brain complexity and dynamics in various emotional states. This capability provided us enough knowledge to claim lower-alpha and gamma bands contain the main information for discrimination of emotional states.