Advanced spike sorting approaches in implantable VLSI wireless brain computer interfaces: a survey

Brain Computer/Machine Interfaces (BCI/BMIs) have substantial potential for enhancing the lives of disabled individuals by restoring functionalities of missing body parts or allowing paralyzed individuals to regain speech and other motor capabilities. Due to severe health hazards arising from skull incisions required for wired BCI/BMIs, scientists are focusing on developing VLSI wireless BCI implants using biomaterials. However, significant challenges, like power efficiency and implant size, persist in creating reliable and efficient wireless BCI implants. With advanced spike sorting techniques, VLSI wireless BCI implants can function within the power and size constraints while maintaining neural spike classification accuracy. This study explores advanced spike sorting techniques to overcome these hurdles and enable VLSI wireless BCI/BMI implants to transmit data efficiently and achieve high accuracy.Comment: Submitted to 37th International Conference on VLSI Design 202

NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting

Effects of intracranial stimulation and the involvement of the human parahippocampal cortex in perception

How the human brain translates photons hitting the retina into conscious perception remains an open question. Throughout the medial temporal lobe (MTL), there are neurons (called concept cells) that change their firing rate when that neuron's preferred concept, e.g., a specific person or object, is seen. The firing rate of concept cells is correlated with perception. Nevertheless, it remains unclear whether or to what extent concept cells are involved in perceptogenesis, i.e., the creation of conscious percepts. Inferring from studies in monkeys, concept-specific neurons involved in perceptogenesis would be expected along the ventral and dorsal stream of visual processing (also called the what and where pathway, respectively). Various regions that are part of the dorsal stream are connected to the parahippocampal cortex (PHC), a region within the MTL. Compared to other MTL regions, lower selectivity, the absence of multimodal responses, and especially the shorter response latencies do not exclude an involvement of the PHC in perceptogenesis. In fact, damage to the parahippocampal place area (PPA, a part of the PHC) results in topographical disorientation. The goal of this thesis is to test the involvement of the PHC in perception by using electrical stimulation during a forced-choice categorization task involving landscapes versus animals. First, we determined effective parameters for intracranial stimulation of brain tissue in epilepsy patients implanted with depth-electrodes for seizure monitoring. We investigated the effects of amplitude, phase width, frequency, and pulse-train duration on neuronal firing, the local field potential (LFP), and behavioral responses to evoked percepts. Frequency and charge per phase were the most influential parameters on all three signals. Both parameters showed a positive effect on event-related potentials (ERPs) in the LFP. Higher frequencies (especially around 200 Hz) lead to a short-term inhibition of neuronal firing, while higher charge per phase can have an inhibitory or excitatory effect on neuronal firing. All parameters had a positive effect on the reports of evoked percepts; on reports of phosphenes in response to stimulating close to the optic radiation as well as on reports of auditory verbal hallucinations in response to stimulating Heschl's gyrus. Using functional magnetic resonance imaging (fMRI), we found that the PPA, i.e., the part of the PHC that is most selective towards images of landscapes, is rather small (up to 1‰ of total brain volume per hemisphere) with varying degrees of hemispheric laterality. Stimulating the PHC outside of the PPA - using a 100 ms high-frequency pulse train delivered at the natural response latency of the PHC - had no effect on categorizing landscapes. However, stimulating inside the PPA, close to the peak activation of the fMRI cluster, resulted in a 7% to 10% increase in landscape responses to ambiguous stimuli. Furthermore, stimulating the PPA also led to an increase in behavioral response time, especially to images with a predominant landscape component. None of our patients reported visual hallucinations of places or scenes in response to our stimulation protocols. Our data suggests that the PPA is involved in the perceptogenesis of landscapes at a stage that does not reach awareness, while the rest of the PHC is unlikely to be involved in perceptogenesis, at least not as it pertains to the perception of landscapes or animals. We also developed an online spike sorting algorithm and an adaptive screening procedure for concept cells to pave the way for new paradigms involving informed feedback

Spike sorting for large, dense electrode arrays

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%

Uber-Claws : unsupervised pattern classification for multi-unit extracellular neuronal burst extraction

To further an understanding of how a neuronal population generates patterns of rhythmic activity, the temporal dynamics of the group of neurons must be formalized. Essential to this pursuit, is the ability to reliably detect and separate the classes of single-unit neuronal activity from multi-unit extracellular signals recorded in a single channel. This study proposes a unified approach to automatically detect and classify single-unit bursts, and to observe the precise onset and offset of burst activity. Existing approaches to the problem fundamentally depend on the statistics of spike waveform variability, both extrinsic and intrinsic to the neuron. In contrast, the proposed approach depends on statistics that characterize the burst variability. An unsupervised learning procedure is implemented using hierarchical clustering to derive a complete and natural description of the variability in terms of clusters of bursts that possess strong internal similarities. Redundant solution vectors are used to parameterize each cluster, and a fuzzy classification approach assigns each burst to a class. Accuracy of the technique is demonstrated on in vivo and in vitro recordings of the triphasic pyloric rhythm in stomatogastric ganglion of crab Cancer borealis. The results, evaluated against a widely used manual classification approach, show that the technique performs detection and classification with comparable accuracy and quantifiable certainty, and is robust to background activity and noise