• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.99-102
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• 1997

A neural spike sorting technique has been developed that also has the capability of template learning. A system of software has been written that first obtains the templates by learning, and then performs the sorting of the spikes into single units. The spike sorting can be done in real time. The template learning consists of spike detection based on the discrete Haar transform (DHT), feature extraction by clustering of spike amplitude and duration, classification based on rms error, and fabrication of templates. The developed algorithms can be implemented into real time systems using digital signal processors.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.346-351
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• 2007

다중 전극으로 측정한 활동 전위의 분류(Multi-electrode spike sorting)는 단일 전극(single-electrode)보다 더 정확한 결과를 보여준다. 그러나 다중 전극에서 주어지는 활동 전위 크기들의 클러스터는 일반적으로 분류하기 쉴지 않은 문제이다. 이 논문에서는 고전적인 클러스터링 알고리듬 중의 하나인 Mountain method를 수정하여 다중 전극 활동전위의 분류에 적합한 알고리듬을 제안한다. 통상적인 데이터 클러스터링이 아닌 공간 분할을 통해 신경 데이터의 다양한 클러스터에 대해서 적응도가 높아지고 빠른 분류를 하게 된다.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.469-475
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• 2000

신경신호의 계측은 신경계의 연구에 필수적인 도구로 최근 반도체미세전극기술 등 수십, 수백개의 채널로부터 신경신호를 기록할 수 있는 방법들이 발달함에 따라 많은 수의 뉴런으로부터 신경 신호를 측정하여 컴퓨터로 그 신호를 처리할 수 있는 시스템의 필요성은 더욱 커지고 있다. 본 연구에서는 최대 16채널의 신경신호를 실시간에 측정하여 기록하고, 저장된 신호로부터 활동전위를 검출하며, 단일 뉴런들로부터의 신호를 분류하여 spike train의 형태로 저장한 뒤 여러 뉴런들간의 상관관계를 분석하기 위한 spike train 해석이 가능한 시스템을 개발하였다. 이 시스템은 보통사양의 PC이외에는 단지 신호획득보드만을 포함하여 다채널미세전극으로부터 뉴런의 신호를 측정, 증폭하여 호스트PC로 전송하고 저장하며 이로부터 활동전위를 검출하여 단일뉴런으로부터의 spike train으로 분류할 수 있다. 또한 저장된 spike train들로부터 신경회로망을 이루는 여러뉴런 들간의 관계를 분석하여 기능들이 시스템에 포함되어있다. 개발된 시스템을 사용하여 개구리 감각 신경의 신호를 실시간에 동시기록하여 활동전위을 검출하고 특징추출방법과 principal component analysis를 이용하여 분류한 뒤 spike train 해석을 수행하였다.

• Research in Vestibular Science
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• v.17 no.4
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• pp.142-151
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• 2018

Objectives: Excitability o medial vestibular nucleus (MVN) in the brainstem can be affected by changes in the arterial blood pressure. Several animal studies have demonstrated that acute hypotension results in the alteration of multiunit activities and expression of cFos protein in the MVN. In the field of extracellular electrophysiological recording, tetrode technology and spike sorting algorithms can easily identify single unit activity from multiunit activities in the brain. However, detailed properties of electrophysiological changes in single unit of the MVN during acute hypotension have been unknown. Methods: Therefore, we applied tetrode techniques and electrophysiological characterization methods to know the effect of acute hypotension on single unit activities of the MVN of rats. Results: Two or 3 types of unit could be classified according to the morphology of spikes and firing properties of neurons. Acute hypotension elicited 4 types of changes in spontaneous firing of single unit in the MVN. Most of these neurons showed excitatory responses for about within 1 minute after the induction of acute hypotension and then returned to the baseline activity 10 minutes after the injection of sodium nitroprusside. There was also gradual increase in spontaneous firing in some units. In contrast small proportion of units showed rapid reduction of firing rate just after acute hypotension. Conclusions: Therefore, application of tetrode technology and spike sorting algorithms is another method for the monitoring of electrical activity of vestibular nuclear during acute hypotension.

• Progress in Medical Physics
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• v.16 no.3
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• pp.148-154
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• 2005

Since the output of retina for visual stimulus is carried by neurons of very diverse functional properties, it is not adequate to use conventional single electrode for recording the retinal action potential. For this purpose, we used newly developed multichannel recording system for monitoring the simultaneous electrical activities of many neurons in a functioning piece of retina. Retinal action potentials are recorded with an extra-cellular planar array of 60 microelectrodes. In studying the collective activity of the ganglion cell population it is essential to recognize basic functional distinctions between individual neurons. Therefore, it is necessary to detect and to classify the action potential of each ganglion cell out of mixed signal. We programmed M-files with MATLAB for this sorting process. This processing is mandatory for further analysis, e.g. poststimulus time histogram (PSTH), auto-correlogram, and cross-correlogram. We established MATLAB based protocol for waveform classification and verified that this approach was effective as an initial spike sorting method.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.4
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• pp.279-285
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• 2016

Reliable recording and analysis of peripheral nerve activity is important to recognize the user's intention for controlling a neuro-prosthetic hand. In this paper, we present a peripheral nerve recording system that consisted of an intrafascicular multi-electrode array, an electrode insertion device, and a multi-channel neural amplifier. The 16 channel multi-electrode array was stably implanted into the sciatic nerve of the rat under anesthesia using the electrode insertion device. During passive movements and mechanical stimuli, muscle and cutaneous afferent signals were recorded with the multi-channel neural amplifier. Furthermore, we propose a spike sorting method to isolate individual neuronal unit. The muscle proprioceptive units were classified as muscle spindle afferents or Golgi tendon organ afferents, and the skin exteroceptive units were categorized as slow adapting afferents or fast adapting afferents. Experimental results showed that the proposed method could be applicable to record and analyze peripheral nerve activity in neuro-prosthetic systems.

• Clinical and Experimental Pediatrics
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• v.55 no.10
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• pp.383-387
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• 2012

Purpose: Although benign rolandic epilepsy (BRE) is a benign condition, it may be associated with a spectrum of behavioral, psychiatric, and cognitive disorders. This study aimed to assess the cognitive and other neuropsychological profiles of children with BRE. Methods: In total, 23 children with BRE were consecutively recruited. All children underwent sleep electroencephalography (EEG) and were assessed on a battery of comprehensive neuropsychological tests including the Korean versions of the Wechsler intelligence scale for children III, frontal executive neuropsychological test, rey complex figure test, Wisconsin card sorting test, attention deficit diagnostic scale, and child behavior checklist scale. Results: The study subjects included 13 boys and 10 girls aged $9.0{\pm}1.6$ years. Our subjects showed an average monthly seizure frequency of $0.9{\pm}0.7$, and a majority of them had focal seizures (70%). The spike index (frequency/min) was $4.1{\pm}5.3$ (right) and $13.1{\pm}15.9$ (left). Of the 23 subjects, 9 showed frequent spikes (>10/min) on the EEG. The subjects had normal cognitive and frontal executive functions, memory, and other neuropsychological sub-domain scores, even though 8 children (35%) showed some evidence of learning difficulties, attention deficits, and aggressive behavior. Conclusion: Our data have limited predictive value; however, these data demonstrate that although BRE appears to be benign at the onset, children with BRE might develop cognitive, behavioral, and other psychiatric disorders during the active phase of epilepsy, and these problems may even outlast the BRE. Therefore, we recommend scrupulous follow-up for children with BRE.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.