Why sharing matters for electrophysiological data analysis

Smith, Leslie S.

doi:10.1016/j.brainresbull.2015.06.009

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…For this reason, spike sorting has been and continues to be a central problem in computational neuroscience. Freely available spike sorting software and data sets, such as Wave clus and its associated simulated data set, are valuable resources that help the field of computational neuroscience move forward [27].…”

Section: Discussionmentioning

confidence: 99%

Taller Peaks: an improved spike detection algorithm that simultaneously reduces type I and type II errors for Wave{\_}clus

Okatan¹

2017

Turk J Elec Eng & Comp Sci

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Abstract:Wave clus is an unsupervised spike detection and sorting algorithm that has been used in dozens of experimental studies as a spike sorting tool. It is often used as a benchmark for comparing the performance of new spike sorting algorithms. For these reasons, the spike detection performance of Wave clus is important for both experimental and computational studies that involve spike sorting. Two measures of spike detection performance are the number of false positive detections (type I error) and the number of missed spikes (type II error). Here, a new spike detection algorithm is proposed that reduces the number of misses and false positives of Wave clus in a widely used simulated data set across the entire range of commonly used detection thresholds. The algorithm accepts a spike if its amplitude is larger than the amplitude of its two immediate neighbors, where an immediate neighbor is the nearest peak of the same polarity within ± 1 refractory period. The simultaneous reduction that is achieved in the number of false positives and misses is important for experimental and computational studies that use Wave clus as a spike sorting tool or as a benchmark. A software patch that incorporates the algorithm into Wave clus as an optional spike detection algorithm is provided.

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Section: Discussionmentioning

confidence: 99%

Taller Peaks: an improved spike detection algorithm that simultaneously reduces type I and type II errors for Wave{\_}clus

Okatan¹

2017

Turk J Elec Eng & Comp Sci

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“…With the innovation of artificial intelligence, electrophysiological data have been extended to brain modeling, robot control, and brain–computer interfaces . Big electrophysiology data and various applications have imposed challenges on data transfer, storage, data standardization, visualization, statistical analysis, real‐time computing, data mining, and multi‐institution collaboration online or offline . Moreover, large electrophysiological datasets also need advanced data storage methods such as distributed file systems (DFSs) and NoSQL, computing architecture such as cloud computing, and online parallel data mining algorithms integrated in big data platforms.…”

Section: Introductionmentioning

confidence: 99%

“…20 Big electrophysiology data and various applications have imposed challenges on data transfer, storage, data standardization, visualization, statistical analysis, real-time computing, data mining, and multi-institution collaboration online or offline. 12,[21][22][23][24][25] Moreover, large electrophysiological datasets also need advanced data storage methods such as distributed file systems (DFSs) and NoSQL, computing architecture such as cloud computing, and online parallel data mining algorithms integrated in big data platforms. Hence, we believe that an online data sharing and analysis platform is an essential solution to cope with these challenges, which are illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

An overview of online based platforms for sharing and analyzing electrophysiology data from big data perspective

Chen

Wang

Yuan

2017

WIREs Data Min & Knowl

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With the development of applications and high‐throughput sensor technologies in medical fields, scientists and scientific professionals are facing a big challenge—how to manage and analyze the big electrophysiological datasets created by these sensor technologies. The challenge exhibits several aspects: one is the size of the data (which is usually more than terabytes); the second is the format used to store the data (the data created are generally stored using different formats); the third is that most of these unstructured, semi‐structured, or structured datasets are still distributed over many researchers' own local computers in their laboratories, which are not open access, to become isolated data islands. Thus, how to overcome the challenge and share/mine the scientific data has become an important research topic. The aim of this paper is to systematically review recent published research on the developed web‐based electrophysiological data platforms from the perspective of cloud computing and programming frameworks. Based on this review, we suggest that a conceptual scientific workflow‐based programming framework associated with an elastic cloud computing environment running big data tools (such as Hadoop and Spark) is a good choice for facilitating effective data mining and collaboration among scientists. WIREs Data Mining Knowl Discov 2017, 7:e1206. doi: 10.1002/widm.1206 This article is categorized under: Application Areas > Health Care Fundamental Concepts of Data and Knowledge > Information Repositories Technologies > Computer Architectures for Data Mining

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Why sharing matters for electrophysiological data analysis

Cited by 2 publications

References 23 publications

Taller Peaks: an improved spike detection algorithm that simultaneously reduces type I and type II errors for Wave{\_}clus

Taller Peaks: an improved spike detection algorithm that simultaneously reduces type I and type II errors for Wave{\_}clus

An overview of online based platforms for sharing and analyzing electrophysiology data from big data perspective

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