Adaptive-threshold neural spike detection by noise-envelope tracking

Traver, L.; Tarín, Cristina; Marti, Paula; Cardona, Narcís

doi:10.1049/el:20071631

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…Simple digital solutions on the other hand use limited or no temporary storage for spike data history, and/or use simple thresholds. Examples are spike detection with adaptive thresholding, based on Non-linear Energy Operator (NEO) and envelope detection [30], [15]. These solutions are simple to implement and only require storage for the previous and next sample for all sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

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Algorithm/Architecture Co-optimisation Technique for Automatic Data Reduction of Wireless Read-Out in High-Density Electrode Arrays

Yassin

Catthoor

Kloosterman

et al. 2018

ACM Trans. Embed. Comput. Syst.

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and Technology (NTNU) NICK VAN HELLEPUTTE, IMEC High-density electrode arrays used to read out neural activity will soon surpass the limits of the amount of data that can be transferred within reasonable energy budgets. This is true for wired brain implants when the required bandwidth becomes very high, and even more so for untethered brain implants that require wireless transmission of data. We propose an energy efficient spike data extraction solution for high-density electrode arrays, capable of reducing the data to be transferred by over 85%. We combine temporal and spatial spike data analysis with low implementation complexity, where amplitude thresholds are used to detect spikes and the spatial location of the electrodes is used to extract potentially useful sub-threshold data on neighboring electrodes. We tested our method against a state-of-the-art spike detection algorithm, with prohibitively high implementation complexity, and found that the majority of spikes are extracted reliably. We obtain further improved quality results when ignoring very small spikes below 30% of the voltage thresholds, resulting in 91% accuracy. Our approach uses digital logic and is therefore scalable with increasing number of electrodes. CCS Concepts: • Computer systems organization → Embedded and cyber-physical systems; Embedded systems;

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Section: Related Workmentioning

confidence: 99%

“…Different approaches have been proposed to meet the challenges presented in this section, such as online spike detection [30] and compression [36] solutions. Common for all approaches is their attempt to reduce the data to be transmitted by extracting the most valuable information.…”

Section: Introductionmentioning

confidence: 99%

Algorithm/Architecture Co-optimisation Technique for Automatic Data Reduction of Wireless Read-Out in High-Density Electrode Arrays

Yassin

Catthoor

Kloosterman

et al. 2018

ACM Trans. Embed. Comput. Syst.

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“…We show in Figure 2 a new extensive comparison of the performance of spike detection based on the continuous wavelet transform (CWT) with some frequently used methods: positive thresholding (PTH) (a spike is detected when a certain positive threshold is exceeded), negative thresholding (NTH) (a spike is detected when a certain negative threshold is exceeded), double thresholding (DTH) (spikes are detected when a positive or a negative threshold is exceeded [76]), nonlinear energy operator (NEO) [37,46], multiresolution Teager energy operator (MTEO) [13,14], and the stationary wavelet transform (SWT) [6]. In Figure 2, the probability of detection (PD) is plotted against the probability of false alarms (PFA) leading to the so-called receiver operator characteristic curves.…”

Section: Spike Detectionmentioning

confidence: 99%

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

Dijck

Hulle

2014

Biomedical Engineering / Biomedizinische Technik

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Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the "usefulness" of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The everincreasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing "good quality signals" or "signals of interest." This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on "signal quality." The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist's appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature "electronic depth control" in contrast to the mechanical repositioning of the electrode shafts in search of "good quality signals" or "signals of interest."

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“…Secondly, few methods offer an automatic threshold selection mechanism, thus allowing for a truly unsupervised operation. The available approaches [20][21][22][23] focus on the case when spike detection is done by amplitude thresholding (first category). For the above mentioned methods which rely on blind equalization, none or only heuristic values are given regarding the choice of an appropriate threshold.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised and Drift-Adaptive Spike Detection Algorithm Based on Hybrid Blind Beamforming

Natora

Obermayer

2010

EURASIP J. Adv. Signal Process.

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In the case of extracellular recordings, spike detection algorithms are necessary in order to retrieve information about neuronal activity from the data. We present a new spike detection algorithm which is based on methods from the field of blind equalization and beamforming and which is particularly adapted to the specific signal structure neuronal data exhibit. In contrast to existing approaches, our method blindly estimates several waveforms directly from the data, selects automatically an appropriate detection threshold, and is also able to track neurons by filter adaptation. The few parameters of the algorithm are biologically motivated, thus, easy to set. We compare our method with current state-of-the-art spike detection algorithms and show that the proposed method achieves favorable results. Realistically simulated data as well as data acquired from simultaneous intra/extracellular recordings in rat slices are used as evaluation datasets.

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Adaptive-threshold neural spike detection by noise-envelope tracking

Cited by 10 publications

References 7 publications

Algorithm/Architecture Co-optimisation Technique for Automatic Data Reduction of Wireless Read-Out in High-Density Electrode Arrays

Algorithm/Architecture Co-optimisation Technique for Automatic Data Reduction of Wireless Read-Out in High-Density Electrode Arrays

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

An Unsupervised and Drift-Adaptive Spike Detection Algorithm Based on Hybrid Blind Beamforming

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