{"title":"Deep brain stimulation of the subthalamic nucleus: model-based analysis of the effects of electrode capacitance on the volume of activation","authors":"C. Butson, C.C. Mclntyre","doi":"10.1109/CNE.2005.1419589","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419589","url":null,"abstract":"Deep brain stimulation (DBS) has rapidly emerged as an effective clinical treatment for movement disorders. However, our understanding of the neural effects of DBS is limited, and significant opportunities exist to optimize electrode design to enhance therapeutic effectiveness. To address these issues, we have developed computational tools to predict the neural response to stimulation. For decades the electrostatic approximation has been applied in neural stimulation modeling, treating the electrode as a perfect current source and the neural tissue as a purely conductive medium. However, clinical DBS electrodes are voltage controlled, utilize an asymmetrical biphasic stimulus waveform, and are surrounded by a 3D anisotropic, inhomogeneous tissue medium. To more accurately model DBS in the human, we have developed finite element models (FEM) of the electrode and tissue medium that incorporate a Fourier FEM solver to determine the potential distribution in the tissue in time and space simultaneously. The field data is then coupled to multi-compartment neuron models to predict neural activation. Our results show that electrostatic models overestimate the volume of activation (VOA) by -30% compared to voltage-controlled stimulation for typical therapeutic stimulation parameter settings. The error is directly related to the electrode capacitance and the stimulation pulse width. These results illustrate the need for detailed models of neural stimulation to accurately predict the effects of DBS","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126213734","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}
{"title":"A Fully-Automated Neural Spike Sorting Based on Projection Pursuit and Gaussian Mixture Model","authors":"Kyung Hwan Kim","doi":"10.1109/CNE.2005.1419576","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419576","url":null,"abstract":"Existing algorithms for neural spike sorting have been unsatisfactory when the signal-to-noise ratio (SNR) is low, especially for the fully automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with the system based on principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm. The system consists of 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 with the PCA, and the proposed combination of feature extraction and unsupervised classification yields much better performance than the PCA-FCM","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"18 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125935612","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}
{"title":"Design of Two-Dimensional Grid Electrodes Considering Spatial Characteristics of SEMG Signal","authors":"M.M. Rahman, M. Ferdjallah, G. Harris","doi":"10.1109/CNE.2005.1419616","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419616","url":null,"abstract":"Surface grid electrode is a noninvasive technique, which can be utilized for topographic analysis of EMG signals. Grid electrode increases the uptake area and provides a better picture of spatial and temporal characteristics of muscle activity. Determination of clinical parameters such as muscle conduction velocity, location of innervation zone, firing pattern, size and location of motor unit, are some of the challenging applications of multi-electrode array. However, an optimum grid electrode requires detail examination regarding the dimension and inter-electrode distance of the array. In this paper a systematic approach is presented for selecting a grid spacing to reduce spatial aliasing. The characteristics of isotropic layers of subcutaneous fat and skin tissues are incorporated in a computer muscle model in order to analyze their effects on complete potential profile and grid spacing. The variation of spatial cutoff frequency and corresponding inter electrode distance with depth of muscle fiber inside the muscle are simulated in both time and frequency domain for three different levels of accuracy","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129951012","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}
{"title":"A Comparison of EEG Preprocessing Methods using Time Delay Neural Networks","authors":"R. Rao, R. Derakhshani","doi":"10.1109/CNE.2005.1419607","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419607","url":null,"abstract":"Multichannel recordings of EEG data during various mental tasks are processed using two popular methods, independent component analysis (ICA) and matching pursuit (MP). The results are fed to a time delay neural network (TDNN) for classification of each mental task. Based on the results of the test sets, we analyzed the effectiveness of ICA and MP methods for use in EEG preprocessing and TDNN classification. It is shown that ICA is more effective than MP in lowering the neural network classification error; however this advantage is not significant","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129556880","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}
{"title":"An Integrated System for Multichannel Neuronal Recording with Spike / LFP Separation and Digital Output","authors":"Y. Perelman, Ran Ginosar","doi":"10.1109/CNE.2005.1419637","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419637","url":null,"abstract":"A mixed-signal front-end processor for multichannel neuronal recording is described. It receives twelve differential-input channels of implanted recording electrodes. A programmable cutoff HPF blocks DC and low frequency input drift. The signals are band-split at about 200 Hz to low frequency local field potential (LFP) and high-frequency spike data (SPK), which is band limited by a programmable-cutoff LPF. Amplifier offsets are compensated by calibration DACs. The SPK and LFP channels provide variable amplification rates of up to 5000 and 500, respectively. The analog signals are converted into digital form, and streamed out over a serial digital bus. A threshold filter supresses inactive portions of the signal and emits only spike segments. A prototype has been fabricated on a 0.35/mum CMOS process and tested successfully","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126427059","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}
U. Hoffmann, G. garcia, J. Vesin, K. Diserens, T. Ebrahimi
{"title":"A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces","authors":"U. Hoffmann, G. garcia, J. Vesin, K. Diserens, T. Ebrahimi","doi":"10.1109/CNE.2005.1419562","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419562","url":null,"abstract":"Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) trials. The algorithm is used to detect the P300 in the human EEG and to build a brain-computer interface (BCI), specifically a spelling device. Important features of the method described here are its high classification accuracy and its conceptual simplicity. The algorithm was tested with datasets recorded in our lab and one benchmark dataset from the BCI Competition 2003. The number of correctly inferred symbols with the P300 speller paradigm varied between 90% and 100%. In particular, all of the inferred symbols were correct for the BCI competition dataset","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127862527","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}
{"title":"Synchronous stereo-video and biosignal recording - a basic setup for Human-Computer-Interface applications","authors":"O. Burmeister, M. Litza, M. Nitschke, U. Hofmann","doi":"10.1109/CNE.2005.1419669","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419669","url":null,"abstract":"The work presented reports on the development of a data acquisition system intended to be used in human-computer-interfacing applications to synchronously record upper limb movement and corresponding biosignals, be it EEG, ECoG, EMG or multi unit neuronal signals. Ultimately, this aims at the clinical recording of ECoG signals from awake patients undergoing tumor resection. For that purpose stereo-video camera frames are used to detect and triangulate hand and finger positions with high precision while at the same time a DSP-based 32-channel board acquires wideband biosignals. We validated the synchronous acquisition of both space coordinates and bioelectrical signals (EMG) by performing simple grasp experiments. Further research will be dedicated to the system's clinical application","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130795966","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}
{"title":"Dynamic Simulation and Testing of the Electrode-Electrolyte Interface of 3-D Stimulating Microelectrodes","authors":"A. Hung, D. Zhou, R. Greenberg, J. Judy","doi":"10.1109/CNE.2005.1419584","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419584","url":null,"abstract":"Traditional simulations of current distribution for neuralelectrodes have been based on steady-state, resistive-only models that predict severe current crowding at sharp electrode topologies. In contrast, this work presents time-stepping calculations performed with SPICE and ANSYS that can accurately reflect the capacitive behavior of neuralelectrodes. The simulations display that at the electrode-electrolyte interface, the current distribution exhibits a variation of <10%. While current crowding is observed in the solution adjacent to sharp convex edges, the current at these sites is found to be parallel to the electrode surface, and is not expected to contribute to electrode corrosion. Preliminary dissolution studies at 50 muC/cm 2 shows an isometric dissolution pattern, confirming the predicted uniform current density","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117284973","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}
Hongxuan Zhang, Y. Oweis, H. Mozaffari-Naeini, S. Venkatesha, N. Thakor, A. Natarajan
{"title":"Continuous Quantitative Motor Evoked Potentials for Spinal Cord Injury Detection","authors":"Hongxuan Zhang, Y. Oweis, H. Mozaffari-Naeini, S. Venkatesha, N. Thakor, A. Natarajan","doi":"10.1109/CNE.2005.1419651","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419651","url":null,"abstract":"Motor evoked potential (MEP) monitoring is a complementary approach to directly monitor the integrity of the motor pathway and is another modality designed to ensure the integrity of the spinal cord during surgery. However, conventional interpretation of MEP signal based on amplitude and latency tracking is subjective and not always accurate, and usually a significant learning curve has been demonstrated. Hence, we proposed a quantitative time frequency analysis method, window energy index, for monitoring spinal cord surgery. The preliminary results suggest the proposed analysis has better sensitivity than conventional amplitude analysis","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132537124","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}
{"title":"Bioelectronic Artificial Nose Using Four-Channel Moth Antenna Biopotential Recordings","authors":"A. Myrick, T. Baker, K. Park, J. Hetling","doi":"10.1109/CNE.2005.1419620","DOIUrl":"https://doi.org/10.1109/CNE.2005.1419620","url":null,"abstract":"The use of insect antennae as an odor sensor array was evaluated as a means to advance the current capabilities of \" artificial nose\" technology. A given species is highly sensitive to odors of survival interest (e.g. species-specific pheromones), but also to a broad range of other natural and anthropogenic compounds. The sensitivity of the antennae to some odors extends to the parts per billion range. In contrast, the best current artificial nose technology is able to detect compounds in the parts per million range. Here, a system designed to utilize four antenna biopotential signals suitable for field use and a computational analysis strategy which allows discrimination between specific odors, and between odor and background or unknown compounds, with high fidelity and in real time, is described. The automated analysis measures three parameters per odor response. Following a training period, a K nearest-neighbor (KNN) approach is used to classify an unknown odor, assuming equal prior probabilities. The algorithm can also declare an odor as \"unknown\". System responses to single filaments in an odor plume can be analyzed and classified in less than one second","PeriodicalId":113815,"journal":{"name":"Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005.","volume":"11 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132874624","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}