Summary form only given. Neuroengineering is defined as the interdisciplinary field of engineering and computational approaches, as applied to problems in basic and clinical neuroscience. The NeuroEngineering Training Initiative at Johns Hopkins seeks to balance engineering, mathematics, and computer science with molecular, cellular, and systems neurosciences. The program leverages the educational and research resources of both the engineering and medical schools. The life sciences training consists of courses taught either through the medical school or through the basic biomedical science curriculum. The engineering training consists of rigorous coursework in mathematics, computation, and other engineering subjects appropriate for the particular student's focus. NETI trainees also forge collaborations between faculty members, participate in weekly seminars, and present their research at various conferences. Through schoolwork, seminars, social events, conferences, and collaborative interactions, our students provide the nexus between basic science, clinical, and engineering research. The NIH has recognized the importance of this initiative and has provided funding for the program. NETI trainees have the unique opportunity to be part of the pioneer program in neuroengineering training while taking advantage of the world class institution that is Johns Hopkins
{"title":"NETI: the NeuroEngineering Training Initiative","authors":"N. Davidovics, G. Colón","doi":"10.1109/CNE.2007.369762","DOIUrl":"https://doi.org/10.1109/CNE.2007.369762","url":null,"abstract":"Summary form only given. Neuroengineering is defined as the interdisciplinary field of engineering and computational approaches, as applied to problems in basic and clinical neuroscience. The NeuroEngineering Training Initiative at Johns Hopkins seeks to balance engineering, mathematics, and computer science with molecular, cellular, and systems neurosciences. The program leverages the educational and research resources of both the engineering and medical schools. The life sciences training consists of courses taught either through the medical school or through the basic biomedical science curriculum. The engineering training consists of rigorous coursework in mathematics, computation, and other engineering subjects appropriate for the particular student's focus. NETI trainees also forge collaborations between faculty members, participate in weekly seminars, and present their research at various conferences. Through schoolwork, seminars, social events, conferences, and collaborative interactions, our students provide the nexus between basic science, clinical, and engineering research. The NIH has recognized the importance of this initiative and has provided funding for the program. NETI trainees have the unique opportunity to be part of the pioneer program in neuroengineering training while taking advantage of the world class institution that is Johns Hopkins","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"97 10 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":"127996855","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. Reutsky, D. Ben-Shimol, N. Farah, S. Levenberg, S. Shoham
Neuroprosthetic retinal interfaces depend upon the ability to bypass the damaged photoreceptor layer and directly activate populations of retinal ganglion cells (RGCs). Current approaches to this task largely rely on electrode array implants. We are pursuing an alternative, light-based approach towards direct activation of the RGCs, by artificially causing them to express Channelrhodopsin II (ChR2), a light-gated cation channel. In addition to being non-contact, optical techniques lend themselves relatively easily to a variety of technologies for achieving patterned stimulation with high temporal and spatial resolution. In early studies, we are using viral vectors to obtain wide spread expression of ChR2 in rat retinas, and have developed a system capable of controlled large-scale, flexible stimulation of the retinal tissue with high temporal accuracy through adaptations of video projection technology. Finally, we demonstrate a PC-based wearable system that can perform the image processing transformations required for optical retinal neuroprosthetic interfaces in real time.
{"title":"Patterned optical activation of Channelrhodopsin II expressing retinal ganglion cells","authors":"I. Reutsky, D. Ben-Shimol, N. Farah, S. Levenberg, S. Shoham","doi":"10.1109/CNE.2007.369609","DOIUrl":"https://doi.org/10.1109/CNE.2007.369609","url":null,"abstract":"Neuroprosthetic retinal interfaces depend upon the ability to bypass the damaged photoreceptor layer and directly activate populations of retinal ganglion cells (RGCs). Current approaches to this task largely rely on electrode array implants. We are pursuing an alternative, light-based approach towards direct activation of the RGCs, by artificially causing them to express Channelrhodopsin II (ChR2), a light-gated cation channel. In addition to being non-contact, optical techniques lend themselves relatively easily to a variety of technologies for achieving patterned stimulation with high temporal and spatial resolution. In early studies, we are using viral vectors to obtain wide spread expression of ChR2 in rat retinas, and have developed a system capable of controlled large-scale, flexible stimulation of the retinal tissue with high temporal accuracy through adaptations of video projection technology. Finally, we demonstrate a PC-based wearable system that can perform the image processing transformations required for optical retinal neuroprosthetic interfaces in real time.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"12 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":"117058006","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}
D. Cheney, A. Goh, Jie Xu, K. Gugel, J.G. Harris, J. Sánchez, J. Príncipe
This paper describes a wireless system for sampling multiple channels of neural activity based on a low-power, custom 80dB-gain integrated bioamplifier, Texas Instrument's MSP430 microprocessors, and Nordic Semiconductor's ultra low power, high bandwidth RF transmitter/receivers. The system's features are presented as well as results of spike potentials from a live subject.
{"title":"Wireless, In Vivo Neural Recording using a Custom Integrated Bioamplifier and the Pico System","authors":"D. Cheney, A. Goh, Jie Xu, K. Gugel, J.G. Harris, J. Sánchez, J. Príncipe","doi":"10.1109/CNE.2007.369601","DOIUrl":"https://doi.org/10.1109/CNE.2007.369601","url":null,"abstract":"This paper describes a wireless system for sampling multiple channels of neural activity based on a low-power, custom 80dB-gain integrated bioamplifier, Texas Instrument's MSP430 microprocessors, and Nordic Semiconductor's ultra low power, high bandwidth RF transmitter/receivers. The system's features are presented as well as results of spike potentials from a live subject.","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":"121220115","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}
J. Suzurikawa, M. Nakao, R. Kanzaki, Y. Jimbo, H. Takahashi
In order to probe the spatio-temporal activity of cultured neural network, microelectrode arrays (MEAs) have been widely used. MEAs, however, have limitations of their electrode numbers and densities, resulting in low spatial resolutions of stimulation and recording. Here, to overcome this problem, we propose and develop an experimental setup for light-addressed stimulation and simultaneous fluorescence calcium imaging, using the previously published light-addressable electrode. The electrode has a translucent thin-film-laminated structure and allows optical access from both sides of the substrate. We, thus, provided the fluorescence excitation light from the topside and an addressing illumination from the bottom. By instantly shutting out the fluorescence excitation light during the stimulus application, we prevented the excitation light from interfering with the addressing illumination. With this experimental setup, we successfully measured spatio-temporal patterns of neuronal activities evoked by light-addressed stimuli. Evoked fluorescence transients with hundred-millisecond latencies suggested the possibility that some neurons were activated by recurrent synaptic inputs, which were possibly overlooked by previous MEA studies.
{"title":"Light-Addressed Stimulation and Simultaneous Calcium Imaging for Probing Spatio-Temporal Activity of Cultured Neural Network","authors":"J. Suzurikawa, M. Nakao, R. Kanzaki, Y. Jimbo, H. Takahashi","doi":"10.1109/CNE.2007.369611","DOIUrl":"https://doi.org/10.1109/CNE.2007.369611","url":null,"abstract":"In order to probe the spatio-temporal activity of cultured neural network, microelectrode arrays (MEAs) have been widely used. MEAs, however, have limitations of their electrode numbers and densities, resulting in low spatial resolutions of stimulation and recording. Here, to overcome this problem, we propose and develop an experimental setup for light-addressed stimulation and simultaneous fluorescence calcium imaging, using the previously published light-addressable electrode. The electrode has a translucent thin-film-laminated structure and allows optical access from both sides of the substrate. We, thus, provided the fluorescence excitation light from the topside and an addressing illumination from the bottom. By instantly shutting out the fluorescence excitation light during the stimulus application, we prevented the excitation light from interfering with the addressing illumination. With this experimental setup, we successfully measured spatio-temporal patterns of neuronal activities evoked by light-addressed stimuli. Evoked fluorescence transients with hundred-millisecond latencies suggested the possibility that some neurons were activated by recurrent synaptic inputs, which were possibly overlooked by previous MEA studies.","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":"130772273","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}
M. Schiefer, K. Polasek, G. Pinault, R. Triolo, D. Tyler
Functional electrical stimulation (FES) can produce coordinated contractions of paralyzed muscles through activation of the peripheral motor nerves to partially restore the functional movements lost due to spinal cord injury (SCI). The long-term goal of our research is to restore standing and stepping function using a flat interface nerve electrode (FINE) via selective activation of target muscles innervated by the femoral nerve. The data presented here come from the first intraoperative experiment ever conducted using a FINE on a human volunteer. Implantation and explantation of the FINE were accomplished successfully and without complications or need for special surgical instruments. Selective and graded stimulation of muscles occurred when using an 8 channel FINE.
{"title":"Intraoperative Evaluation of the First Flat Interface Nerve Electrode for a Standing Neuroprosthesis: A Case Report","authors":"M. Schiefer, K. Polasek, G. Pinault, R. Triolo, D. Tyler","doi":"10.1109/CNE.2007.369599","DOIUrl":"https://doi.org/10.1109/CNE.2007.369599","url":null,"abstract":"Functional electrical stimulation (FES) can produce coordinated contractions of paralyzed muscles through activation of the peripheral motor nerves to partially restore the functional movements lost due to spinal cord injury (SCI). The long-term goal of our research is to restore standing and stepping function using a flat interface nerve electrode (FINE) via selective activation of target muscles innervated by the femoral nerve. The data presented here come from the first intraoperative experiment ever conducted using a FINE on a human volunteer. Implantation and explantation of the FINE were accomplished successfully and without complications or need for special surgical instruments. Selective and graded stimulation of muscles occurred when using an 8 channel FINE.","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":"130893023","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}
M. Giannì, F. Maggio, M. Liberti, A. Paffi, F. Apollonio, G. D'Inzeo
Neuronal networks with feedforward architecture are typical of peripheral nervous system. A realistic stochastic model of feedforward network was here implemented and used to investigate the sensitivity of neuronal sensory pathways to input electromagnetic (EM) fields. Aim of this work was to address and characterize EM signal detectability throughout the network, pointing out the biophysical properties underlying possible signal amplification. Synaptic noise is shown to enhance signal transduction according to the stochastic resonance paradigm, and pooling neuron assemblies in a feedforward configuration is evidenced to give rise to amplification throughout the network layers. This may be relevant in a biomedical perspective, where techniques based on electric or magnetic stimulation of the nervous system could take advantage from signal transduction optimization.
{"title":"Enhancement of EM Signal Detectability in a Realistic Model of Feedforward Neuronal Network","authors":"M. Giannì, F. Maggio, M. Liberti, A. Paffi, F. Apollonio, G. D'Inzeo","doi":"10.1109/CNE.2007.369765","DOIUrl":"https://doi.org/10.1109/CNE.2007.369765","url":null,"abstract":"Neuronal networks with feedforward architecture are typical of peripheral nervous system. A realistic stochastic model of feedforward network was here implemented and used to investigate the sensitivity of neuronal sensory pathways to input electromagnetic (EM) fields. Aim of this work was to address and characterize EM signal detectability throughout the network, pointing out the biophysical properties underlying possible signal amplification. Synaptic noise is shown to enhance signal transduction according to the stochastic resonance paradigm, and pooling neuron assemblies in a feedforward configuration is evidenced to give rise to amplification throughout the network layers. This may be relevant in a biomedical perspective, where techniques based on electric or magnetic stimulation of the nervous system could take advantage from signal transduction optimization.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"11 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":"130899682","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}
S. Acharya, V. Aggarwal, F. Tenore, Hyun-Chool Shin, R. Etienne-Cummings, M. Schieber, N. Thakor
Advances in brain-computer interfaces (BCI) have enabled direct neural control of robotic and prosthetic devices. However, it remains unknown whether cortical signals can be decoded in real-time to replicate dexterous movements of individual fingers and the wrist. In this study, single unit activity from 115 task-related neurons in the primary motor cortex (Ml) of a trained rhesus monkey were recorded, as it performed individuated movements of the fingers and wrist of the right hand. Virtual multi-unit ensembles, or voxels, were created by randomly selecting contiguous subpopulations of these neurons. Non-linear hierarchical filters using artificial neural networks (ANNs) were designed to asynchronously decode the activity from multiple virtual ensembles, in real-time. The decoded output was then used to actuate individual fingers of a robotic hand. An average real-time decoding accuracy of greater than 95 % was achieved with all neurons from randomly placed voxels containing 48 neurons, and up to 80% with as few as 25 neurons. These results suggest that dexterous control of individual digits and wrist of a prosthetic hand can be achieved by real-time decoding of neuronal ensembles from the Ml hand area in primates.
{"title":"Towards a Brain-Computer Interface for Dexterous Control of a Multi-Fingered Prosthetic Hand","authors":"S. Acharya, V. Aggarwal, F. Tenore, Hyun-Chool Shin, R. Etienne-Cummings, M. Schieber, N. Thakor","doi":"10.1109/CNE.2007.369646","DOIUrl":"https://doi.org/10.1109/CNE.2007.369646","url":null,"abstract":"Advances in brain-computer interfaces (BCI) have enabled direct neural control of robotic and prosthetic devices. However, it remains unknown whether cortical signals can be decoded in real-time to replicate dexterous movements of individual fingers and the wrist. In this study, single unit activity from 115 task-related neurons in the primary motor cortex (Ml) of a trained rhesus monkey were recorded, as it performed individuated movements of the fingers and wrist of the right hand. Virtual multi-unit ensembles, or voxels, were created by randomly selecting contiguous subpopulations of these neurons. Non-linear hierarchical filters using artificial neural networks (ANNs) were designed to asynchronously decode the activity from multiple virtual ensembles, in real-time. The decoded output was then used to actuate individual fingers of a robotic hand. An average real-time decoding accuracy of greater than 95 % was achieved with all neurons from randomly placed voxels containing 48 neurons, and up to 80% with as few as 25 neurons. These results suggest that dexterous control of individual digits and wrist of a prosthetic hand can be achieved by real-time decoding of neuronal ensembles from the Ml hand area in primates.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"37 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":"128699811","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}
We study the problem of distinguishing between individual finger movements of one hand using electrocorticographic (ECOG) signals. In previous work, we have shown that ECOG signals have high predictive accuracy and spatial resolution for classifying hand versus tongue movements. In this paper, we significantly extend this paradigm by studying the first 5-class classification problem for ECOG, and show that an average 5-class accuracy of 23% across 6 subjects is possible using as little as 10min of training data. In addition to opening up possibilities for higher-bandwidth brain-computer interfaces, the use of finger movements for control may yield a more intuitive mapping from ECOG signals to control of a prosthetic. Although this study uses real movements, our results provide the foundation for understanding ECOG signal changes during finger movement.
{"title":"Finger Movement Classification for an Electrocorticographic BCI","authors":"P. Shenoy, K. Miller, J. Ojemann, R. Rao","doi":"10.1109/CNE.2007.369644","DOIUrl":"https://doi.org/10.1109/CNE.2007.369644","url":null,"abstract":"We study the problem of distinguishing between individual finger movements of one hand using electrocorticographic (ECOG) signals. In previous work, we have shown that ECOG signals have high predictive accuracy and spatial resolution for classifying hand versus tongue movements. In this paper, we significantly extend this paradigm by studying the first 5-class classification problem for ECOG, and show that an average 5-class accuracy of 23% across 6 subjects is possible using as little as 10min of training data. In addition to opening up possibilities for higher-bandwidth brain-computer interfaces, the use of finger movements for control may yield a more intuitive mapping from ECOG signals to control of a prosthetic. Although this study uses real movements, our results provide the foundation for understanding ECOG signal changes during finger movement.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"21 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":"131136367","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}
This paper presents an analysis on the performance of the prewhitening beamformer when applied to MEG experiments involving dual (task and control) conditions. We analyze the performance of the prewhitening method under two kinds of realistic scenarios. In the first scenario, we assume that some sources exist only in the control condition but they not in the task condition. In the second scenario, we assume that some signal sources exist both in the control and the task conditions, and that they change intensity between the two conditions. Our analysis shows that the prewhitening method is very robust to these non-ideal scenarios, and the results of this analysis are validated in our experiments.
{"title":"Performance of prewhitening beamforming in MEG dual experimental conditions","authors":"K. Sekihara, K. Hild, S. Dalal, S. Nagarajan","doi":"10.1109/CNE.2007.369657","DOIUrl":"https://doi.org/10.1109/CNE.2007.369657","url":null,"abstract":"This paper presents an analysis on the performance of the prewhitening beamformer when applied to MEG experiments involving dual (task and control) conditions. We analyze the performance of the prewhitening method under two kinds of realistic scenarios. In the first scenario, we assume that some sources exist only in the control condition but they not in the task condition. In the second scenario, we assume that some signal sources exist both in the control and the task conditions, and that they change intensity between the two conditions. Our analysis shows that the prewhitening method is very robust to these non-ideal scenarios, and the results of this analysis are validated in our experiments.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"36 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":"123791539","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}
Temporal processing of neural recordings with high-density microelectrode arrays implanted in the cortex is highly desired to alleviate the data telemetry bottleneck. By exploiting the energy compactness capabilities of the discrete wavelet transform (DWT), our previous work has shown that it is a viable data compression tool that faithfully preserves the information in the neural data. This paper describes an area-power minimized hardware implementation of the multi-level, multi-channel DWT. Performance tradeoffs and key design decisions for implantable applications are analyzed. A 32-channel, 4-level version of the circuit has been custom designed in 0.18mum CMOS and occupies only 0.16mm2, making it very suitable for high-yield intra-cortical neural interface applications.
利用植入大脑皮层的高密度微电极阵列对神经记录进行时间处理是缓解数据遥测瓶颈的迫切需要。通过利用离散小波变换(DWT)的能量紧凑性，我们之前的工作已经表明它是一种可行的数据压缩工具，忠实地保留了神经数据中的信息。本文描述了一种面积功耗最小的多级、多通道DWT的硬件实现。分析了可植入应用的性能权衡和关键设计决策。该电路的32通道4电平版本已在0.18 mm CMOS上定制设计，占地面积仅为0.16mm2，非常适合用于高产量的皮质内神经接口应用。
{"title":"A High-Yield Area-Power Efficient DWT Hardware for Implantable Neural Interface Applications","authors":"A. Kamboh, A. Mason, K. Oweiss","doi":"10.1109/CNE.2007.369649","DOIUrl":"https://doi.org/10.1109/CNE.2007.369649","url":null,"abstract":"Temporal processing of neural recordings with high-density microelectrode arrays implanted in the cortex is highly desired to alleviate the data telemetry bottleneck. By exploiting the energy compactness capabilities of the discrete wavelet transform (DWT), our previous work has shown that it is a viable data compression tool that faithfully preserves the information in the neural data. This paper describes an area-power minimized hardware implementation of the multi-level, multi-channel DWT. Performance tradeoffs and key design decisions for implantable applications are analyzed. A 32-channel, 4-level version of the circuit has been custom designed in 0.18mum CMOS and occupies only 0.16mm2, making it very suitable for high-yield intra-cortical neural interface applications.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"54 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":"123828174","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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