Spike detection and spike sorting with a hidden Markov model improves offline decoding of motor cortical recordings

Abstract

Objective. Detection and sorting (classification) of action potentials from extracellular recordings are two important pre-processing steps for brain–computer interfaces (BCIs) and some neuroscientific studies. Traditional approaches perform these two steps serially, but using shapes of action potential waveforms during detection, i.e. combining the two steps, may lead to better performance, especially during high noise. We propose a hidden Markov model (HMM) based method for combined detecting and sorting of spikes, with the aim of improving the final decoding accuracy of BCIs. Approach. The states of the HMM indicate whether there is a spike, what unit a spike belongs to, and the time course within a waveform. The HMM outputs probabilities of spike detection, and from this we can calculate expectations of spike counts in time bins, which can replace integer spike counts as input to BCI decoders. We evaluate the HMM method on simulated spiking data. We then examine the impact of using this method on decoding real neural data recorded from primary motor cortex of two Rhesus monkeys. Main results. Our comparisons on simulated data to detection-then-sorting approaches and combined detection-and-sorting algorithms indicate that the HMM method performs more accurately at detection and sorting (0.93 versus 0.73 spike count correlation, 0.73 versus 0.49 adjusted mutual information). On real neural data, the HMM method led to higher adjusted mutual information between spike counts and kinematics (monkey K: 0.034 versus 0.027; monkey M: 0.033 versus 0.022) and better neuron encoding model predictions (K: 0.016 dB improvement; M: 0.056 dB improvement). Lastly, the HMM method facilitated higher offline decoding accuracy (Kalman filter, K: 8.5% mean squared error reduction, M: 18.6% reduction). Significance. The HMM spike detection and sorting method offers a new approach to spike pre-processing for BCIs and neuroscientific studies.

A MOOC-Based Hybrid Teaching Model of College English

“…The changes in knowledge nodes and student status nodes in the Bayesian knowledge tracking model in Figure 1 are introduced by students' learning behavior. The probability model in the figure is based on the hidden Markov time series model, which describes the randomly generated state sequence and generates the observable sequence from the random state sequence [21]. According to the Markov chain structure in the figure, the following probability formula (1) is obtained.…”

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

“…All the algorithms and technologies presented so far converge toward clustering, since the goal of decoding extracellular action potentials is acquired by this step. The ideal clustering algorithm runs real-time, implements sequential processing, it is fully unsupervised, but preferably as uncomplicated as clustering and parallel operations may be carried out on the recording device (Wood et al, 2004 ; Li et al, 2019 ; Toosi et al, 2020 ). For simplicity, clustering algorithms may be arranged into model- or non-model-based categories, admitting that even within these groups, algorithms highly differ from each other ( Figure 2 ).…”

“…These tactics are unusually resilient to noise associations, and over and above cluster visualization is facilitated (Mahallati et al, 2019 ). Modeling a mixture of drifting t-distributions enables differentiating overlapping clusters from heavy tails (Shan et al, 2017 ), while hidden Markov models have been successfully utilized during joint detection and sorting analyses (Li et al, 2019 ); however, the foremost strain of computational requirements is faintly resolved. The threat of cluster overseparation is also considerable, especially when non-Gaussian clusters, in fact, are assumed to represent Gaussian distribution (Keshtkaran and Yang, 2017 ; Rezaei et al, 2021 ).…”

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