• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.468-474
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• 2005

Brain-machine interface (BMI) based on neuronal spike trains is regarded as one of the most promising means to restore basic body functions of severely paralyzed patients. The spike train decoding algorithm, which extracts underlying information of neuronal signals, is essential for the BMI. Previous studies report that a linear filter is effective for this purpose and there is no noteworthy gain from the use of nonlinear mapping algorithms, in spite of the fact that neuronal encoding process is obviously nonlinear. We designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR), and show that the nonlinear algorithms are superior in general. The MLP often showed unsatisfactory performance especially when it is carelessly trained. The nonlinear SVR showed the highest performance. This may be due to the superiority of the SVR in training and generalization. The advantage of using nonlinear algorithms were more profound for the cases when there are false-positive/negative errors in spike trains.

• 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.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.212-217
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• 2007

As a preliminary study for the development of electrical stimulation strategy of artificial retina, we set up a method fur the reconstruction of input intensity variation from retinal ganglion cell(RGC) responses. In order to estimate light intensity variation, we used an optimal linear filter trained from given stimulus intensity variation and multiple single unit spike trains from RGCs. By applying ON/OFF stimulation(ON duration: 2 sec, OFF duration: 5 sec) repetitively, we identified three functional types of ganglion cells according to when they respond to the ON/OFF stimulus actively: ON cell, OFF cell, and ON-OFF cell. Experiments were also performed using a Gaussian random stimulus and a binary random stimulus. The input intensity was updated once every 90 msec(i. e. 11 Hz) to present the stimulus. The result of reconstructing 11 Hz Gaussian and binary random stimulus was not satisfactory and showed low correlation between the original and reconstructed stimulus. In the case of ON/OFF stimulus in which temporal variation is slow, successful reconstruction was achieved and the correlation coefficient was as high as 0.8.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.342-351
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• 2017

The neural decoding is a procedure that uses spike trains fired by neurons to estimate features of original stimulus. This is a fundamental step for understanding how neurons talk each other and, ultimately, how brains manage information. In this paper, the strategies of neural decoding are classified into three methodologies: rate decoding, temporal decoding, and population decoding, which are explained. Rate decoding is the firstly used and simplest decoding method in which the stimulus is reconstructed from the numbers of the spike at given time (e. g. spike rates). Since spike number is a discrete number, the spike rate itself is often not continuous and quantized, therefore if the stimulus is not static and simple, rate decoding may not provide good estimation for stimulus. Temporal decoding is the decoding method in which stimulus is reconstructed from the timing information when the spike fires. It can be useful even for rapidly changing stimulus, and our sensory system is believed to have temporal rather than rate decoding strategy. Since the use of large numbers of neurons is one of the operating principles of most nervous systems, population decoding has advantages such as reduction of uncertainty due to neuronal variability and the ability to represent a stimulus attributes simultaneously. Here, in this paper, three different decoding methods are introduced, how the information theory can be used in the neural decoding area is also given, and at the last machinelearning based algorithms for neural decoding are introduced.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.271-279
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• 2007

We suggest a novel method for the evaluation of cochlear implant (CI) speech processing strategy based on neural spike train decoding. From formant trajectories of input speech and auditory nerve responses responding to the electrical pulse trains generated from a specific CI speech processing strategy, optimal linear decoding filter was obtained, and used to estimate formant trajectory of incoming speech. Performance of a specific strategy is evaluated by comparing true and estimated formant trajectories. We compared a newly-developed strategy rooted from a closer mimicking of auditory periphery using nonlinear time-varying filter, with a conventional linear-filter-based strategy. It was shown that the formant trajectories could be estimated more exactly in the case of the nonlinear time-varying strategy. The superiority was more prominent when background noise level is high, and the spectral characteristic of the background noise was close to that of speech signals. This confirms the superiority observed from other evaluation methods, such as acoustic simulation and spectral analysis.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.694-702
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• 2007

The adaptation effect originating from the chemical synapse between auditory nerve and inner hair cell gives advantage in accurate representation of temporal cues of incoming speech such as speech onset. Thus it is expected that the modification of conventional speech processing strategies of cochlear implant(CI) by incorporating the adaptation effect will result in considerable improvement of speech perception performance such as consonant perception score. Our purpose in this paper was to evaluate our new CI speech processing strategy incorporating the adaptation effect by the observation of auditory nerve responses. By classifying the presence or absence of speech from the auditory nerve responses, i. e. spike trains, we could quantitatively compare speech onset detection performances of conventional and improved strategies. We could verify the effectiveness of the adaptation effect in improving the speech onset representation characteristics.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.207-208
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• 1998

Classification of extracellularly recorded action potential into each unit is an important procedure for further analysis of spike trains as point process. We utilize feedforward neural network structures, multilayer perceptron and radial basis function network to implement spike classifier. For the efficient training of classifiers, nonlinear energy operator that can trace the instantaneous frequency as well as the amplitude of the input signal is used. Trained classifiers shows successful operation, up to 90% correct classification was possible under 1.2 of signal-to-noise ratio.

• The Korean Journal of Physiology and Pharmacology
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• v.13 no.3
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• pp.221-227
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• 2009

For successful restoration of visual function by a visual neural prosthesis such as retinal implant, electrical stimulation should evoke neural responses so that the informat.ion on visual input is properly represented. A stimulation strategy, which means a method for generating stimulation waveforms based on visual input, should be developed for this purpose. We proposed to use the decoding of visual input from retinal ganglion cell (RGC) responses for the evaluation of stimulus encoding strategy. This is based on the assumption that reliable encoding of visual information in RGC responses is required to enable successful visual perception. The main purpose of this study was to determine the influence of inter-dependence among stimulated RGCs activities on decoding accuracy. Light intensity variations were decoded from multiunit RGC spike trains using an optimal linear filter. More accurate decoding was possible when different types of RGCs were used together as input. Decoding accuracy was enhanced with independently firing RGCs compared to synchronously firing RGCs. This implies that stimulation of independently-firing RGCs and RGCs of different types may be beneficial for visual function restoration by retinal prosthesis.

• Progress in Medical Physics
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• v.18 no.2
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• pp.73-80
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• 2007

Fruitful findings have been produced from five out of sixty cells which were obtained from each 63 individual Aplisia caught at the Jeju coast. Spiking patterns of three out of five cells, such as relaxation oscillator, bursting within a short time of the inter-burst interval, chaotic bursting, period doubling sequences, bursting with long trains of action potentials separated by short silent periods, regular repeated beating or elliptic bursting, and silent states had been changed in order as the temperature was lowered to $10^{\circ}C\;from\;32^{\circ}C$. In the intervals of every about 40 minutes repeated ups and downs of temperature produced similar firing patterns at the allowable temperature ranges. The other two cells showed difference from these. The amplitudes of the action potentials of the two cells will not be highly decreased in 24 hours. Average spike frequencies, the inter-burst interval, peak to peak spike amplitude of action potentials, minimum potential values are compared and analyzed by using the computer programme. The spike frequencies according to temperature show the distribution of bell type, with maximal spike frequencies at intermediate temperatures and minimal ones at either end. The most common pattern consist of high spike frequency during failing and low one during rising temperatures.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.347-354
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• 2009

For the reliable transmission of meaningful visual information using prosthetic electrical stimulation, it is required to develop an effective stimulation strategy for the generation of electrical pulse trains based on input visual information. The characteristics of neuronal activities of retinal ganglion cells (RGCs) evoked by electrical stimulation should be understood for this purpose. In this study, for the development of an optimal stimulation strategy for visual prosthesis, we analyzed the neuronal responses of RGCs in rd1 mouse, photoreceptor-degenerated retina of animal model of retinal diseases (retinitis pigmentosa). Based on the in-vitro model of epiretinal prosthesis which consists of planar multielectrode array (MEA) and retinal patch, we recorded and analyzed multiunit RGC activities evoked by amplitude-modulated electrical pulse trains. Two modes of responses were observed. Short-latency responses occurring at 3 ms after the stimulation were estimated to be from direct stimulation of RGCs. Long-latency responses were also observed mainly at 2 - 100 ms after stimulation and showed rhythmic firing with same frequency as the oscillatory background field potential. The long-latency responses could be modulated by pulse amplitude and duration. From the results, we expect that optimal stimulation conditions such as pulse amplitude and pulse duration can be determined for the successful transmission of visual information by electrical stimulation.