In the attempt to fully understand the mechanism for the formation and realization of tissue-specific functions of living multicellular systems, a couple of experimental conditions is required; grasping both the whole picture and the state of elements of a multicellular system. From this viewpoint, selecting neuronal circuits as the target, we have developed an electrical recording method from cultured small neuronal circuits by combining a simple micropatterning technique with a extracellular recording method using a mobile microelectrode. The simple micropatterning method enabled formation of thousands of individual small neuronal circuits consist of single to tens of neurons in one common 35-mm culture dish without any microfabrication apparatus by means of spraying of poly-D-lysine solution onto non-adhesive culture surfaces. Those small neuronal circuits, derived from embryonic hippocampus of rats, showed spontaneous synchronous firing after 8 days after cell seeding. Any of these small neuronal circuits were accessible with a mobile microelectrode, and their spontaneous firings were recorded noninvasively with single-cell-resolution by positioning the tip on constituent neurons. This set of methods does not require any specialized microfabrication apparatus or chemicals, and has a possibility to be used as a practical recording method of electrophysiological activities of a variety of multicellular organisms
{"title":"Site-selective Electrical Recording from Small Neuronal Circuits using Spray Patterning Method and Mobile Microelectrodes","authors":"H. Moriguchi, N. Tamai, Y. Takayama, Y. Jimbo","doi":"10.1109/CNE.2007.369717","DOIUrl":"https://doi.org/10.1109/CNE.2007.369717","url":null,"abstract":"In the attempt to fully understand the mechanism for the formation and realization of tissue-specific functions of living multicellular systems, a couple of experimental conditions is required; grasping both the whole picture and the state of elements of a multicellular system. From this viewpoint, selecting neuronal circuits as the target, we have developed an electrical recording method from cultured small neuronal circuits by combining a simple micropatterning technique with a extracellular recording method using a mobile microelectrode. The simple micropatterning method enabled formation of thousands of individual small neuronal circuits consist of single to tens of neurons in one common 35-mm culture dish without any microfabrication apparatus by means of spraying of poly-D-lysine solution onto non-adhesive culture surfaces. Those small neuronal circuits, derived from embryonic hippocampus of rats, showed spontaneous synchronous firing after 8 days after cell seeding. Any of these small neuronal circuits were accessible with a mobile microelectrode, and their spontaneous firings were recorded noninvasively with single-cell-resolution by positioning the tip on constituent neurons. This set of methods does not require any specialized microfabrication apparatus or chemicals, and has a possibility to be used as a practical recording method of electrophysiological activities of a variety of multicellular organisms","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"20 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124157850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It is not yet clear how this effect of SR on the signal to noise ratio affects signal processing in neural networks. In this paper, we test the hypothesis that SR can improve information transmission in which sub-threshold stimuli are driven to distal positions on the dendritic trees of hippocampal CA1 neuron models. From spike firing times recorded at the soma, the inter spike intervals were generated and then "total" and "noise" entropies were estimated to obtain the mutual information and information rate of the spike trains. The simulation results show that the information rate reached a maximum value at a specific amplitude of the background noise in which sub-threshold stimuli were driven to distal positions on dendritic trees, while the information rate decreased as the noise intensity increased in which supra-threshold stimuli were driven to a proximal position. It is implied that SR can play a key role in improving the information transmission in the case of the sub-threshold input located at distal positions on the dendritic trees
{"title":"Enhancement of Information Transmission with Stochastic Resonance: Influence of Stimulating Position in Hippocampal CA1 Neuron Models","authors":"H. Mino, D. Durand","doi":"10.1109/CNE.2007.369764","DOIUrl":"https://doi.org/10.1109/CNE.2007.369764","url":null,"abstract":"Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It is not yet clear how this effect of SR on the signal to noise ratio affects signal processing in neural networks. In this paper, we test the hypothesis that SR can improve information transmission in which sub-threshold stimuli are driven to distal positions on the dendritic trees of hippocampal CA1 neuron models. From spike firing times recorded at the soma, the inter spike intervals were generated and then \"total\" and \"noise\" entropies were estimated to obtain the mutual information and information rate of the spike trains. The simulation results show that the information rate reached a maximum value at a specific amplitude of the background noise in which sub-threshold stimuli were driven to distal positions on dendritic trees, while the information rate decreased as the noise intensity increased in which supra-threshold stimuli were driven to a proximal position. It is implied that SR can play a key role in improving the information transmission in the case of the sub-threshold input located at distal positions on the dendritic trees","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121802573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Chatterjee, V. Aggarwal, A. Ramos, S. Acharya, N. Thakor
Advances in brain-computer interfaces (BCI) will require the integration of haptic feedback channels to add extra sensory dimensions for applications such as neuroprostheses. To the best of our knowledge, previous BCIs have relied on visual biofeedback to the user in the form of a computer interface or a device. This study demonstrates that it is possible to operate a BCI using only vibrotactile biofeedback. Our results show that subjects are able to use vibrotactile feedback to control the BCI with accuracy as high as 72% for a 1D targeting task. We also found that varying placement of the vibratory stimulation between the left and right biceps introduces a significant bias in accuracy figures. Further work to compensate for the use of vibratory or other haptic feedback modalities will lead to the development of novel BCIs suitable for neuroprosthesis control.
{"title":"Operation of a Brain-Computer Interface Using Vibrotactile Biofeedback","authors":"A. Chatterjee, V. Aggarwal, A. Ramos, S. Acharya, N. Thakor","doi":"10.1109/CNE.2007.369639","DOIUrl":"https://doi.org/10.1109/CNE.2007.369639","url":null,"abstract":"Advances in brain-computer interfaces (BCI) will require the integration of haptic feedback channels to add extra sensory dimensions for applications such as neuroprostheses. To the best of our knowledge, previous BCIs have relied on visual biofeedback to the user in the form of a computer interface or a device. This study demonstrates that it is possible to operate a BCI using only vibrotactile biofeedback. Our results show that subjects are able to use vibrotactile feedback to control the BCI with accuracy as high as 72% for a 1D targeting task. We also found that varying placement of the vibratory stimulation between the left and right biceps introduces a significant bias in accuracy figures. Further work to compensate for the use of vibratory or other haptic feedback modalities will lead to the development of novel BCIs suitable for neuroprosthesis control.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116707246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we present a simple technique for removing myoelectric interference in neural recording tripoles. Cuff imbalance is simply unavoidable with the conventional quasi-tripole (QT) configuration and this technique is based on a modified version of the QT, which is capable of compensating for cuff imbalance that is causing electromyogram interference to be present at the output of an amplifier. By carrying out in-vitro experiments we show that, with a tripole that was intentionally made imbalanced, the interference can be reduced about 10-fold.
{"title":"Myoelectric and Common-Mode Interference Rejection in a Quasi-Tripole Amplifier Configuration","authors":"I. Pachnis, A. Demosthenous, N. Donaldson","doi":"10.1109/CNE.2007.369626","DOIUrl":"https://doi.org/10.1109/CNE.2007.369626","url":null,"abstract":"In this paper we present a simple technique for removing myoelectric interference in neural recording tripoles. Cuff imbalance is simply unavoidable with the conventional quasi-tripole (QT) configuration and this technique is based on a modified version of the QT, which is capable of compensating for cuff imbalance that is causing electromyogram interference to be present at the output of an amplifier. By carrying out in-vitro experiments we show that, with a tripole that was intentionally made imbalanced, the interference can be reduced about 10-fold.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125839516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are many commercially available neural data acquisition systems, and all of them use proprietary data formats for internal communication and file storage. This can make it difficult for researchers to develop open, non-proprietary interface and control systems that must access neural data in real time. In this paper, we present a novel packet-based protocol for description and configuration of an array of signal processing instruments, software modules that run on these processors, physical and logical connections, and management of binary data streams between system elements and external devices. This protocol is being prepared as an open industry standard, along with supporting open source software libraries and tools
{"title":"eXtensible Instrument Processing Protocol (XIPP): A Real-Time Signal Processing Framework for Neural Interfaces","authors":"K. S. Guillory, C. Gyulai, J. D. Brederson","doi":"10.1109/CNE.2007.369706","DOIUrl":"https://doi.org/10.1109/CNE.2007.369706","url":null,"abstract":"There are many commercially available neural data acquisition systems, and all of them use proprietary data formats for internal communication and file storage. This can make it difficult for researchers to develop open, non-proprietary interface and control systems that must access neural data in real time. In this paper, we present a novel packet-based protocol for description and configuration of an array of signal processing instruments, software modules that run on these processors, physical and logical connections, and management of binary data streams between system elements and external devices. This protocol is being prepared as an open industry standard, along with supporting open source software libraries and tools","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125666880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in mu and/or beta rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called sub-band common spatial pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into linear discriminant analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: recursive band elimination (RBE) and meta-classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process.
{"title":"Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface","authors":"Q. Novi, Cuntai Guan, T. H. Dat, P. Xue","doi":"10.1109/CNE.2007.369647","DOIUrl":"https://doi.org/10.1109/CNE.2007.369647","url":null,"abstract":"Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in mu and/or beta rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called sub-band common spatial pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into linear discriminant analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: recursive band elimination (RBE) and meta-classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"408 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114932677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Ayaz, M. Izzetoglu, S. Bunce, T. Heiman-Patterson, B. Onaral
The ideal non-invasive brain computer interface (BCI) transforms signals originating from human brain into commands that can control devices and applications. Hence, BCI provides a way for brain output that does not involve neuromuscular system. This represents an advantage for those individuals suffering from neuromuscular impairments such as amyotrophic lateral sclerosis (ALS) or various types of paralysis. In this study we propose to design a new noninvasive BCI that is based on optical means to measure brain activity by monitoring hemodynamic response. The proposed system uses functional near infrared (fNIR) spectroscopy to detect cognitive activity from prefrontal cortex elicited voluntarily by performing a mental task namely N-back test. Our findings indicate that fNIR signal correlates with cognitive tasks associated with working memory. These experimental outcomes compare favorably with previous functional magnetic resonance imaging (fMRI) and complement electroencephalogram (EEG) findings. Since fNIR can be implemented in the form of a wearable and minimally intrusive device, it also has the capacity to monitor brain activity under real life conditions in everyday environments leading the way to potential applications of fNIR in BCI development for communication and entertainment purposes.
{"title":"Detecting cognitive activity related hemodynamic signal for brain computer interface using functional near infrared spectroscopy","authors":"H. Ayaz, M. Izzetoglu, S. Bunce, T. Heiman-Patterson, B. Onaral","doi":"10.1109/CNE.2007.369680","DOIUrl":"https://doi.org/10.1109/CNE.2007.369680","url":null,"abstract":"The ideal non-invasive brain computer interface (BCI) transforms signals originating from human brain into commands that can control devices and applications. Hence, BCI provides a way for brain output that does not involve neuromuscular system. This represents an advantage for those individuals suffering from neuromuscular impairments such as amyotrophic lateral sclerosis (ALS) or various types of paralysis. In this study we propose to design a new noninvasive BCI that is based on optical means to measure brain activity by monitoring hemodynamic response. The proposed system uses functional near infrared (fNIR) spectroscopy to detect cognitive activity from prefrontal cortex elicited voluntarily by performing a mental task namely N-back test. Our findings indicate that fNIR signal correlates with cognitive tasks associated with working memory. These experimental outcomes compare favorably with previous functional magnetic resonance imaging (fMRI) and complement electroencephalogram (EEG) findings. Since fNIR can be implemented in the form of a wearable and minimally intrusive device, it also has the capacity to monitor brain activity under real life conditions in everyday environments leading the way to potential applications of fNIR in BCI development for communication and entertainment purposes.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116192942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Real-time applications of spike sorting, e.g., neural decoding, generally require high numbers of channels, and manual spike sorting methods are extremely time consuming, subjective and, generally, do not perform well for low signal-to-noise ratio (SNR) signals. Hence, an automatic method is sought which is efficient and robust in both detecting neural spikes and constructing a classification model of spikes arriving with underlying statistics that are time-varying. We present such a system under study for application with a microelectrode array of 96 channels with typically three or four units (Le., neurons) per channel. There are several novel elements of the system including filtering the neural signal to a frequency band having better SNR for spike detection, a fixed feature space for simple implementation yet adequate resolving capabilities, a Gaussian statistics model also for simple implementation as a log-likelihood classifier, a systematic approach to determining the number of clusters in a pattern recognition problem, and a robust linear discriminant, histogram-based technique for determining boundaries between feature space clusters
{"title":"Automatic Spike Sorting For Real-time Applications","authors":"D. Sebald, A. Branner","doi":"10.1109/CNE.2007.369761","DOIUrl":"https://doi.org/10.1109/CNE.2007.369761","url":null,"abstract":"Real-time applications of spike sorting, e.g., neural decoding, generally require high numbers of channels, and manual spike sorting methods are extremely time consuming, subjective and, generally, do not perform well for low signal-to-noise ratio (SNR) signals. Hence, an automatic method is sought which is efficient and robust in both detecting neural spikes and constructing a classification model of spikes arriving with underlying statistics that are time-varying. We present such a system under study for application with a microelectrode array of 96 channels with typically three or four units (Le., neurons) per channel. There are several novel elements of the system including filtering the neural signal to a frequency band having better SNR for spike detection, a fixed feature space for simple implementation yet adequate resolving capabilities, a Gaussian statistics model also for simple implementation as a log-likelihood classifier, a systematic approach to determining the number of clusters in a pattern recognition problem, and a robust linear discriminant, histogram-based technique for determining boundaries between feature space clusters","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122880191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I.H. Song, Y. Ji, B. K. Cho, J. Ku, Y. Chee, J.S. Lee, M. Lee, I.Y. Kim, S.I. Kim
The aim of this study is to investigate the possibility that human sleep EEGs can be characterized by a multifractal spectrum using wavelet transform modulus maxima (WTMM). We used sleep EEGs taken from healthy subjects during the four stages of sleep and REM sleep. Our findings showed that the dynamics in human sleep EEGs could be adequately described by a set of scales and characterized by multifractals. We performed multivariate discriminate analysis to evaluate the use of multifractal features for classification. The multivariate discriminate analysis using within-groups covariance matrices for all sleep stages yielded a total error rate of 41.8%. In conclusion, multifractal formalism, based on the WTMM, appears to be a good tool for characterizing dynamics in sleep EEGs
{"title":"Multifractal Analysis of Sleep EEG Dynamics in Humans","authors":"I.H. Song, Y. Ji, B. K. Cho, J. Ku, Y. Chee, J.S. Lee, M. Lee, I.Y. Kim, S.I. Kim","doi":"10.1109/CNE.2007.369730","DOIUrl":"https://doi.org/10.1109/CNE.2007.369730","url":null,"abstract":"The aim of this study is to investigate the possibility that human sleep EEGs can be characterized by a multifractal spectrum using wavelet transform modulus maxima (WTMM). We used sleep EEGs taken from healthy subjects during the four stages of sleep and REM sleep. Our findings showed that the dynamics in human sleep EEGs could be adequately described by a set of scales and characterized by multifractals. We performed multivariate discriminate analysis to evaluate the use of multifractal features for classification. The multivariate discriminate analysis using within-groups covariance matrices for all sleep stages yielded a total error rate of 41.8%. In conclusion, multifractal formalism, based on the WTMM, appears to be a good tool for characterizing dynamics in sleep EEGs","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114282385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoying Li, T.G. Constandinoir, A. Eftekhar, P. Georgiou, C. Toumazoi
This paper presents a biologically-inspired 'silicon neural link', encompassing a neurochemical sensor array, an asynchronous artificial-neural bus and an interleaved biphasic stimulus generator. The proposed system is intended for neuroprosthetic application; employing an array of ISFET-based spiking neurons to convey measured neuronal data across a damaged neural pathway to a target stimulation site. A 48 dB dynamic range is achieved by encoding the neural signal in the time-domain, using accumulating address-events to modulate the biphasic waveform. The electrical stimulation is delivered via a bipolar electrode configuration in a continuous interleave sampling strategy. This has been implemented in a commercially available 035mum CMOS technology.
{"title":"Towards a Bionic Neural Link for Implantable Prosthetics","authors":"Xiaoying Li, T.G. Constandinoir, A. Eftekhar, P. Georgiou, C. Toumazoi","doi":"10.1109/CNE.2007.369618","DOIUrl":"https://doi.org/10.1109/CNE.2007.369618","url":null,"abstract":"This paper presents a biologically-inspired 'silicon neural link', encompassing a neurochemical sensor array, an asynchronous artificial-neural bus and an interleaved biphasic stimulus generator. The proposed system is intended for neuroprosthetic application; employing an array of ISFET-based spiking neurons to convey measured neuronal data across a damaged neural pathway to a target stimulation site. A 48 dB dynamic range is achieved by encoding the neural signal in the time-domain, using accumulating address-events to modulate the biphasic waveform. The electrical stimulation is delivered via a bipolar electrode configuration in a continuous interleave sampling strategy. This has been implemented in a commercially available 035mum CMOS technology.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114499246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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