Limits...
Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

Oliynyk A, Bonifazzi C, Montani F, Fadiga L - BMC Neurosci (2012)

Bottom Line: The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise.This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity.This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

View Article: PubMed Central - HTML - PubMed

Affiliation: Section of Human Physiology, Department of Biomedical Sciences and Advanced Therapies, Faculty of Medicine, University of Ferrara, Via Fossato di Mortara 17/19, 44121, Ferrara, Italy. lynnry@unife.it

ABSTRACT

Background: Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue.

Results: Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms.

Conclusions: This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

Show MeSH

Related in: MedlinePlus

Visualization of raw signal and collection of spike waveforms.A - information covering each trial of movement execution to monitor the uniformity of trials, where black vertical hatches represent spike occurrence detected using external hardware threshold discriminator; blue line is the infrared signal from the analogue IR-pair; thick coloured horizontal lines are retrieved from the set of digital sensors representing different kinematic parts of executed movement. B - the visualization of one trial in 3 sec multi-unit recordings, where spikes determined by software discriminator are marked with red dots. C - an expanding view of the same raw signal, in which the presence of different kinds of spikes is evident; D - extracted 1.8 ms of spikes waveforms, aligned to the peak of action potential by spline interpolation method; E - the same waveforms with 6 truncated samples at the ends, yielded 1.2 ms waveforms to be filled by the data matrix.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3473300&req=5

Figure 4: Visualization of raw signal and collection of spike waveforms.A - information covering each trial of movement execution to monitor the uniformity of trials, where black vertical hatches represent spike occurrence detected using external hardware threshold discriminator; blue line is the infrared signal from the analogue IR-pair; thick coloured horizontal lines are retrieved from the set of digital sensors representing different kinematic parts of executed movement. B - the visualization of one trial in 3 sec multi-unit recordings, where spikes determined by software discriminator are marked with red dots. C - an expanding view of the same raw signal, in which the presence of different kinds of spikes is evident; D - extracted 1.8 ms of spikes waveforms, aligned to the peak of action potential by spline interpolation method; E - the same waveforms with 6 truncated samples at the ends, yielded 1.2 ms waveforms to be filled by the data matrix.

Mentions: where x is the bandpass-filtered signal and is an estimate of the standard deviation of the background noise[24]. Whereas peaks with amplitude lower than the threshold were ignored, peaks higher than threshold were considered for further analysis as follows. Once a significant peak was detected, the whole waveform was collected (eight samples before the peak and ten samples after it, which with our sampling frequency resulted in a total duration of 1.8 ms) and was then interpolated twice to obtain 36 samples for each waveform with cubic spline interpolation method[25]. Six samples at the beginning and at the end of each interpolated shape were then removed, thus leading to 24 sample waveforms (1.2 ms, see Figure 4). These parameters were empirically found to be a good compromise between sampling as many points as possible to record all the important phases of action potential, and keeping the number of spike parameters compact to facilitate further analysis. A n×24 indexed array was then filled with these peak data, rejecting spikes that violate a minimum refractory period after the preceding threshold crossing in order to reduce false positives (see Additional file2).


Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

Oliynyk A, Bonifazzi C, Montani F, Fadiga L - BMC Neurosci (2012)

Visualization of raw signal and collection of spike waveforms.A - information covering each trial of movement execution to monitor the uniformity of trials, where black vertical hatches represent spike occurrence detected using external hardware threshold discriminator; blue line is the infrared signal from the analogue IR-pair; thick coloured horizontal lines are retrieved from the set of digital sensors representing different kinematic parts of executed movement. B - the visualization of one trial in 3 sec multi-unit recordings, where spikes determined by software discriminator are marked with red dots. C - an expanding view of the same raw signal, in which the presence of different kinds of spikes is evident; D - extracted 1.8 ms of spikes waveforms, aligned to the peak of action potential by spline interpolation method; E - the same waveforms with 6 truncated samples at the ends, yielded 1.2 ms waveforms to be filled by the data matrix.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3473300&req=5

Figure 4: Visualization of raw signal and collection of spike waveforms.A - information covering each trial of movement execution to monitor the uniformity of trials, where black vertical hatches represent spike occurrence detected using external hardware threshold discriminator; blue line is the infrared signal from the analogue IR-pair; thick coloured horizontal lines are retrieved from the set of digital sensors representing different kinematic parts of executed movement. B - the visualization of one trial in 3 sec multi-unit recordings, where spikes determined by software discriminator are marked with red dots. C - an expanding view of the same raw signal, in which the presence of different kinds of spikes is evident; D - extracted 1.8 ms of spikes waveforms, aligned to the peak of action potential by spline interpolation method; E - the same waveforms with 6 truncated samples at the ends, yielded 1.2 ms waveforms to be filled by the data matrix.
Mentions: where x is the bandpass-filtered signal and is an estimate of the standard deviation of the background noise[24]. Whereas peaks with amplitude lower than the threshold were ignored, peaks higher than threshold were considered for further analysis as follows. Once a significant peak was detected, the whole waveform was collected (eight samples before the peak and ten samples after it, which with our sampling frequency resulted in a total duration of 1.8 ms) and was then interpolated twice to obtain 36 samples for each waveform with cubic spline interpolation method[25]. Six samples at the beginning and at the end of each interpolated shape were then removed, thus leading to 24 sample waveforms (1.2 ms, see Figure 4). These parameters were empirically found to be a good compromise between sampling as many points as possible to record all the important phases of action potential, and keeping the number of spike parameters compact to facilitate further analysis. A n×24 indexed array was then filled with these peak data, rejecting spikes that violate a minimum refractory period after the preceding threshold crossing in order to reduce false positives (see Additional file2).

Bottom Line: The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise.This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity.This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

View Article: PubMed Central - HTML - PubMed

Affiliation: Section of Human Physiology, Department of Biomedical Sciences and Advanced Therapies, Faculty of Medicine, University of Ferrara, Via Fossato di Mortara 17/19, 44121, Ferrara, Italy. lynnry@unife.it

ABSTRACT

Background: Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue.

Results: Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms.

Conclusions: This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

Show MeSH
Related in: MedlinePlus