Detection¶

Overview¶

Voltage samples will be compared to some threshold value to determine if spiking has occured. Different methods of detection can be defined by declaring 1) a Type that is the data structure for the algorithm, 2) a function “detect” that implements the algorithm 3) a function “threshold” to determine the threshold for comparison during the training period.

Often methods will need to be initialized during a calibration period, such as calculating the running sum of the last n samples in power detection. These initialization procedures are defined in “detectprepare” functions.

All datatypes are members of the abstract type Detect. They should have default constructors to be initialized as follows:

#Create instance of power detection for use in sorting
mypowerdetection=DetectPower()

#Create instance of median absolute value threshold detection for use in sorting
mymedian=DetectSignal()

Methods¶

Currently Implemented¶

Median Threshold¶

Each timepoint is compared to a threshold, where the threshold is generated as some multiple of the formula below:

\[\sigma_n = median \left\{ \frac{|x|}{.6745} \right\}\]

Usually the threshold is set as 4 sigma.

type DetectSignal <: Detect
end

Reference:

Quiroga, R Quian and Nadasdy, Z. and Ben-Shaul, Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. 2004

Nonlinear Energy Operator (NEO)¶

Also known as Teager energy operator (TEO). The NEO is calculated at each time point based on the current, future and past sample and compared to a threshold.

\[\psi [x(n)] = x^2(n) - x(n+1) x(n-1)\]

type DetectNEO <: Detect
end

References:

Mukhopadhyay S and Ray G C. A new interpretation of nonlinear energy operator and its efficacy in spike detection. 1998

Choi, Joon Hwan and Jung, Hae Kyung and Kim, Taejeong. A new action potential detector using the MTEO and its effects on spike sorting systems at low signal-to-noise ratios. 2006.

Power¶

This method looks at the local energy built up over n samples, where by default n=20.

\[P(t) = \left\{ \frac{1}{n} \sum_{i=1}^n (f(t-i) - \bar{f}(t))^2 \right\}^{1/2}\]

\[\bar{f}(t) = \frac{1}{n} \sum_{i=1}^n f(t-i)\]

Some value of power is chosen as a threshold.

type DetectPower <: Detect
        a::Int64 #sum of last n samples
        b::Int64 #sum of squares of last n samples
        c::Int64 #value of sample at t-n
end

Reference:

Kim and Kim, “Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier,” 2002.

Partially Implemented¶

Wavelet - Multiscale Correlation of Wavelet Coefficients¶

References:

Yang, Chenhui and Olson, Byron and Si, Jennie. A multiscale correlation of wavelet coefficients approach to spike detection. 2011.

Yuan, Yuan and Yang, Chenhui and Si, Jennie. The M-Sorter: an automatic and robust spike detection and classification system. 2012.

Yang, Chenhui and Yuan, Yuan and Si, Jennie. Robust spike classification based on frequency domain neural waveform features. 2013.

To Do¶

Amplitude detection - Multiple¶

Reference:

Kamboh, Awais M. and Mason, Andrew J. Computationally efficient neural feature extraction for spike sorting in implantable high-density recording systems. 2013.

EC-PC Spike Detection¶

Reference:

Yang, Zhi et al. A new EC–PC threshold estimation method for in vivo neural spike detection. 2012.

Dynamic Multiphasic Detector¶

Reference:

Swindale, Nicholas V. and Spacek, Martin A. Spike detection methods for polytrodes and high density microelectrode arrays. 2014.

Nonlinear Energy Operator - smoothed (SNEO)¶

Reference:

Azami, Hamed and Sanei, Saeid. Spike detection approaches for noisy neuronal data: Assessment and comparison. 2014.

Normalised cumulative energy difference (NCED)¶

Reference:

Mtetwa, Nhamoinesu and Smith, Leslie S. Smoothing and thresholding in neuronal spike detection. 2006.

Precise Timing Spike Detection¶

Reference:

Maccione, Alessandro et al. A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals. 2009.

Spatial Interpolation Detection¶

Reference:

Muthmann et al. Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays. 2015.

Summation¶

Reference:

Mtetwa, Nhamoinesu and Smith, Leslie S. Smoothing and thresholding in neuronal spike detection. 2006.

Wavelet - Continuous Wavelet Transform¶

References:

Nenadic, Zoran and Burdick, Joel W. Spike detection using the continuous wavelet transform. 2005.

Benitez, Raul and Nenadic, Zoran. Robust unsupervised detection of action potentials with probabilistic models. 2008.

Wavelet - Stationary Wavelet Transform¶

Reference:

Kim, Kyung Hwan and Kim, Sung June. A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio. 2003.

Wavelet - Wavelet Footprints¶

Reference:

Kwon, and Oweiss. Wavelet footprints for detection and sorting of extracellular neural action potentials. 2011.

Kwon, Ki Yong and Eldawlatly, Seif and Oweiss, Karim. NeuroQuest: a comprehensive analysis tool for extracellular neural ensemble recordings. 2012.