Dynamic oscillations of local extracellular field potentials between 30 to 100 Hz have been associated with sensory perception, motor task planning, selective attention and working memory in cortical regions of the mammalian brain (Wang, 2003; Whittington et al., 1995). They have also been observed in the hippocampus. However, the degree of correlation with these tasks, as well as their cellular and network mechanisms is still the subject of study and debate. This is especially true with regard to rhythms observed in the hippocampus, which have been difficult to directly correlate with behavior (Wang, 2003). A minimal mathematical model was developed for a preliminary study of long-range neural transmission of gamma oscillation from the CA3 to the entorhinal cortex via the CA1 region of the hippocampus, a subset within a larger complex set of pathways. A module was created for each local population of neurons with common intrinsic properties and connectivity to simplify the connection process and make the model more flexible. Three modules were created using MATLAB Simulinkreg and tested to confirm that they transmit gamma through the system. The model also revealed that a portion of the signal from CA1 to the entorhinal cortex may be lost in transmission under certain conditions
{"title":"Model of long-range transmission of gamma oscillation","authors":"T. Murray","doi":"10.1109/CNE.2007.369757","DOIUrl":"https://doi.org/10.1109/CNE.2007.369757","url":null,"abstract":"Dynamic oscillations of local extracellular field potentials between 30 to 100 Hz have been associated with sensory perception, motor task planning, selective attention and working memory in cortical regions of the mammalian brain (Wang, 2003; Whittington et al., 1995). They have also been observed in the hippocampus. However, the degree of correlation with these tasks, as well as their cellular and network mechanisms is still the subject of study and debate. This is especially true with regard to rhythms observed in the hippocampus, which have been difficult to directly correlate with behavior (Wang, 2003). A minimal mathematical model was developed for a preliminary study of long-range neural transmission of gamma oscillation from the CA3 to the entorhinal cortex via the CA1 region of the hippocampus, a subset within a larger complex set of pathways. A module was created for each local population of neurons with common intrinsic properties and connectivity to simplify the connection process and make the model more flexible. Three modules were created using MATLAB Simulinkreg and tested to confirm that they transmit gamma through the system. The model also revealed that a portion of the signal from CA1 to the entorhinal cortex may be lost in transmission under certain conditions","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":"116455018","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}
C. M. Chin, M. Popovic, T. Cameron, A. Lozano, R. Chen
The purpose of this study was to explore the possibility of using electrocorticographic (ECoG) recordings to identify the upper limb motion performed by a human subject. More specifically, we were trying to identify features in the ECoG signals that could help us determine the type of movement performed by an individual. Two subjects with subdural electrodes implanted over the primary motor cortex were asked to perform various motor tasks with the upper limb contralateral to the site of electrode implantation. ECoG signals and upper limb kinematics were recorded simultaneously while the participants were performing the movements. ECoG frequency components were identified that correlated well with the performed movements measured along 3D coordinates (X, Y, and Z). These frequencies were grouped using histograms. The resulting histograms had consistent and unique shapes that were representative of specific upper limb movements performed by the participants. Thus, it was possible to identify which movement was performed. To confirm these findings a nearest neighbour classifier was applied to identify the specific movement that each individual had performed. The achieved classification accuracy was 89%.
{"title":"Identification of Arm Movements Using Electrocorticographic Signals","authors":"C. M. Chin, M. Popovic, T. Cameron, A. Lozano, R. Chen","doi":"10.1109/CNE.2007.369645","DOIUrl":"https://doi.org/10.1109/CNE.2007.369645","url":null,"abstract":"The purpose of this study was to explore the possibility of using electrocorticographic (ECoG) recordings to identify the upper limb motion performed by a human subject. More specifically, we were trying to identify features in the ECoG signals that could help us determine the type of movement performed by an individual. Two subjects with subdural electrodes implanted over the primary motor cortex were asked to perform various motor tasks with the upper limb contralateral to the site of electrode implantation. ECoG signals and upper limb kinematics were recorded simultaneously while the participants were performing the movements. ECoG frequency components were identified that correlated well with the performed movements measured along 3D coordinates (X, Y, and Z). These frequencies were grouped using histograms. The resulting histograms had consistent and unique shapes that were representative of specific upper limb movements performed by the participants. Thus, it was possible to identify which movement was performed. To confirm these findings a nearest neighbour classifier was applied to identify the specific movement that each individual had performed. The achieved classification accuracy was 89%.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"365 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":"125828975","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}
E. Felton, N. Lewis, S. Wills, R. Radwin, J.C. Williams
Integration of the Dasher text-entry program with a brain-computer interface (BCI) system may give individuals with severe motor disabilities the ability to write using their neural signals. Five able-bodied participants previously trained to control their neural signals using motor imagery in an electroencephalogram-based BCI study were trained to control the Dasher program using similar methods. The time to write simple phrases in Dasher using BCI and standard mouse inputs were compared. To compare with existing technology, four disabled participants wrote the same phrases using their own augmentative communication input. The time to input phrases with Dasher-BCI was greater than that for Dasher-mouse and other alternative inputs. However, as Dasher is optimized for BCI control, it will become increasingly useful for people with severe motor and speech disabilities.
{"title":"Neural Signal Based Control of the Dasher Writing System","authors":"E. Felton, N. Lewis, S. Wills, R. Radwin, J.C. Williams","doi":"10.1109/CNE.2007.369686","DOIUrl":"https://doi.org/10.1109/CNE.2007.369686","url":null,"abstract":"Integration of the Dasher text-entry program with a brain-computer interface (BCI) system may give individuals with severe motor disabilities the ability to write using their neural signals. Five able-bodied participants previously trained to control their neural signals using motor imagery in an electroencephalogram-based BCI study were trained to control the Dasher program using similar methods. The time to write simple phrases in Dasher using BCI and standard mouse inputs were compared. To compare with existing technology, four disabled participants wrote the same phrases using their own augmentative communication input. The time to input phrases with Dasher-BCI was greater than that for Dasher-mouse and other alternative inputs. However, as Dasher is optimized for BCI control, it will become increasingly useful for people with severe motor and speech disabilities.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"2 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":"129460834","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 new technique for designing EMG spatial filters with optimized spatial selectivity is described. Simulations were used to model an action potential (AP) as a tripole source within the muscle, and to calculate the voltage induced at an array of surface monopolar electrodes. Next, a map of desired spatial filter output voltages was created as a function of the location of an AP beneath the array. Linear least squares was used to solve for the filter weights which optimally matched the desired filter outputs to those resulting from the tripole model. The optimized filters were found to be consistently superior to the conventional normal double differentiating filter. Selectivity was a function of the simulated inter-electrode spacing and the number of electrodes in the array.
{"title":"Spatially Selective Filter Design for High-Resolution EMG Arrays","authors":"J. D. Quartararo, Edward A. Clancy","doi":"10.1109/CNE.2007.369727","DOIUrl":"https://doi.org/10.1109/CNE.2007.369727","url":null,"abstract":"A new technique for designing EMG spatial filters with optimized spatial selectivity is described. Simulations were used to model an action potential (AP) as a tripole source within the muscle, and to calculate the voltage induced at an array of surface monopolar electrodes. Next, a map of desired spatial filter output voltages was created as a function of the location of an AP beneath the array. Linear least squares was used to solve for the filter weights which optimally matched the desired filter outputs to those resulting from the tripole model. The optimized filters were found to be consistently superior to the conventional normal double differentiating filter. Selectivity was a function of the simulated inter-electrode spacing and the number of electrodes in the array.","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":"129692731","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}
Current pharmacological, electrophysiological, and surgical treatments are not always effective for all epileptic syndromes. In analyzing the clinical utility, traditional EEG analysis provides a coarse representation of the neuronal activity and we hypothesize that for chronic, in vivo epilepsy research more specific electrophysiological techniques are necessary. In order to increase our understanding of single unit behavior in an epileptic network, this study recorded pre-seizure single unit firing rates from pyramidal cells and interneurons in an epileptic rat model of temporal lobe epilepsy. The information gained from this study seeks to aid in the development of new seizure warning and control methods.
{"title":"Are the Spatio-Temporal Firings of Pyramidal Cells and Interneurons Markers of Impending Seizures?","authors":"J. Mitzelfelt, J. Sánchez","doi":"10.1109/CNE.2007.369662","DOIUrl":"https://doi.org/10.1109/CNE.2007.369662","url":null,"abstract":"Current pharmacological, electrophysiological, and surgical treatments are not always effective for all epileptic syndromes. In analyzing the clinical utility, traditional EEG analysis provides a coarse representation of the neuronal activity and we hypothesize that for chronic, in vivo epilepsy research more specific electrophysiological techniques are necessary. In order to increase our understanding of single unit behavior in an epileptic network, this study recorded pre-seizure single unit firing rates from pyramidal cells and interneurons in an epileptic rat model of temporal lobe epilepsy. The information gained from this study seeks to aid in the development of new seizure warning and control methods.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"7 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":"129833142","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}
Early studies on the modeling of electromagnetic (EM) field interactions with the human head have shown that induced current densities in the brain depend on both tissue geometry and its conductive properties. However, no head model of sufficient complexity for studying the physics of induced brain activation has been developed which provides well-defined smooth boundaries between tissues of different conductivities and orientations. In our study, we generated a detailed finite element model of the head that includes structural details to the level of cerebral gyri and sulci as well as axonal fiber tracts by combining different imaging modalities, namely computed tomography, magnetic resonance and diffusion tensor imaging. The anisotropic properties of brain tissues accompanying these details have also been included.
{"title":"A Structurally-Detailed Finite Element Human Head Model for Brain-Electromagnetic Field Simulations","authors":"Ming Chen, D. Mogul","doi":"10.1109/CNE.2007.369668","DOIUrl":"https://doi.org/10.1109/CNE.2007.369668","url":null,"abstract":"Early studies on the modeling of electromagnetic (EM) field interactions with the human head have shown that induced current densities in the brain depend on both tissue geometry and its conductive properties. However, no head model of sufficient complexity for studying the physics of induced brain activation has been developed which provides well-defined smooth boundaries between tissues of different conductivities and orientations. In our study, we generated a detailed finite element model of the head that includes structural details to the level of cerebral gyri and sulci as well as axonal fiber tracts by combining different imaging modalities, namely computed tomography, magnetic resonance and diffusion tensor imaging. The anisotropic properties of brain tissues accompanying these details have also been included.","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":"129516197","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}
The ability to accurately detect action potentials in cortical in vivo recordings is a necessary first step in any multichannel wireless brain-machine interface (BMI) data acquisition system. This work presents a comparison of simple spike detection algorithms appropriate for implementation in an autonomous low power wireless BMI chip. The detectors were applied to pre-recorded cortical extracellular potentials. A simultaneously recorded intracellular transmembrane voltage gave the precise spike times of a local neuron; these times were then used as the "gold standard" against which to compare the output of the spike detectors under test. In contrast to earlier work on simulated data that showed the superiority of a simple absolute value-based spike detector, this work demonstrated that the non-linear energy operator provides an effective balance between correct detections and false alarms, and that the relative difference between detection algorithms diminishes as SNR increases
{"title":"Comparison of Spike Detectors based on Simultaneous Intracellular and Extracellular Recordings","authors":"I. Obeid","doi":"10.1109/CNE.2007.369696","DOIUrl":"https://doi.org/10.1109/CNE.2007.369696","url":null,"abstract":"The ability to accurately detect action potentials in cortical in vivo recordings is a necessary first step in any multichannel wireless brain-machine interface (BMI) data acquisition system. This work presents a comparison of simple spike detection algorithms appropriate for implementation in an autonomous low power wireless BMI chip. The detectors were applied to pre-recorded cortical extracellular potentials. A simultaneously recorded intracellular transmembrane voltage gave the precise spike times of a local neuron; these times were then used as the \"gold standard\" against which to compare the output of the spike detectors under test. In contrast to earlier work on simulated data that showed the superiority of a simple absolute value-based spike detector, this work demonstrated that the non-linear energy operator provides an effective balance between correct detections and false alarms, and that the relative difference between detection algorithms diminishes as SNR increases","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":"129791205","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. Funase, T. Yagi, A. Barros, A. Cichocki, I. Takumi
Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI. In order to process raw EEG data in real time, we performed saccade-related EEG experiments and processed data by using the non-conventional fast ICA with reference signal (FICAR). Visually guided saccade tasks and auditorily guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. As results, for single trail EEG data we have successfully extracted the desire ICs with recognition rate about 70%. In next steps, saccade-related EEG signals and saccade-related ICs in visually and auditorily guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value. As results, peak time when saccade-related ICs have maximum amplitude is earlier than peak time when saccade-related EEG signals have maximum amplitude. This is very important advantage for developing our BCI. However, S/N ratio in being processed by FICAR is not improved comparing S/N ratio in being processed by ensemble averaging.
{"title":"Single trial analysis on saccade-related EEG signal","authors":"A. Funase, T. Yagi, A. Barros, A. Cichocki, I. Takumi","doi":"10.1109/CNE.2007.369687","DOIUrl":"https://doi.org/10.1109/CNE.2007.369687","url":null,"abstract":"Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI. In order to process raw EEG data in real time, we performed saccade-related EEG experiments and processed data by using the non-conventional fast ICA with reference signal (FICAR). Visually guided saccade tasks and auditorily guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. As results, for single trail EEG data we have successfully extracted the desire ICs with recognition rate about 70%. In next steps, saccade-related EEG signals and saccade-related ICs in visually and auditorily guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value. As results, peak time when saccade-related ICs have maximum amplitude is earlier than peak time when saccade-related EEG signals have maximum amplitude. This is very important advantage for developing our BCI. However, S/N ratio in being processed by FICAR is not improved comparing S/N ratio in being processed by ensemble averaging.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"34 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":"126945861","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}
Unlike synchronous brain computer interfaces (BCI), self-paced (asynchronous) BCIs have the advantage of being operational at all times. A 3-state self-paced BCI is capable of detecting two different brain states (e.g. two movements) from the ongoing EEG, while a 2-state one can only detect one brain state. This study improves the performance of a 3-state self-paced BCI designed to detect right and left hand extension movements. Instead of using the values of features at each instant of time, the improved BCI uses all past features' values to detect the presence of a movement at any specific time. After detecting the presence of a movement, the system uses spectral features to determine whether the detected movement is a right or a left hand extension. Using data from two able-bodied individuals, it is shown that the correct detection of a right or a left hand movement, on average, increases from 44.3% to 55.9%, for a fixed false positive rate of 1%. In differentiating between right and left hand movements the average performance increases from 64% to 68.5%. At the false positive rate of 0.5%, the average true positive rate increases from 20.2% to 27.6% and the differentiation rate between right and left hand extensions increase from 71% to is 72.5%.
{"title":"Recent Advances in the Design of a 3-State Self-Paced (Asynchronous) Brain Computer Interface","authors":"A. Bashashati, R. Ward, G. Birch","doi":"10.1109/CNE.2007.369643","DOIUrl":"https://doi.org/10.1109/CNE.2007.369643","url":null,"abstract":"Unlike synchronous brain computer interfaces (BCI), self-paced (asynchronous) BCIs have the advantage of being operational at all times. A 3-state self-paced BCI is capable of detecting two different brain states (e.g. two movements) from the ongoing EEG, while a 2-state one can only detect one brain state. This study improves the performance of a 3-state self-paced BCI designed to detect right and left hand extension movements. Instead of using the values of features at each instant of time, the improved BCI uses all past features' values to detect the presence of a movement at any specific time. After detecting the presence of a movement, the system uses spectral features to determine whether the detected movement is a right or a left hand extension. Using data from two able-bodied individuals, it is shown that the correct detection of a right or a left hand movement, on average, increases from 44.3% to 55.9%, for a fixed false positive rate of 1%. In differentiating between right and left hand movements the average performance increases from 64% to 68.5%. At the false positive rate of 0.5%, the average true positive rate increases from 20.2% to 27.6% and the differentiation rate between right and left hand extensions increase from 71% to is 72.5%.","PeriodicalId":427054,"journal":{"name":"2007 3rd International IEEE/EMBS Conference on Neural Engineering","volume":"42 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":"130886434","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. Neves, Guy Orban, Milena Koudelka-Hep, Thomas Stieglitz, Patrick Ruther
Recordings from the brain have been used for decades to investigate the activity of individual neurons. However, the complex interaction between electrical and chemical signals with respect to short and long term changes of morphology and information transfer is still poorly understood. We introduce a new modular approach for multifunctional probe arrays for cerebral applications that will enable the addressing of fundamental questions in neuroscience. Our approach allows the individual assembly of multiple probes with customized architecture into three-dimensional arrays to address specific brain regions, including sulci of highly folded cortices such as those of humans. In this paper, we introduce the system approach that allows the integration of recording and stimulation electrodes, biosensors, microfluidics and integrated electronics, all sharing a common backbone. We present the first prototypes of multichannel electrodes, flexible ribbon cables, a backbone platform and the first telemetry unit.
{"title":"Development of Modular Multifunctional Probe Arrays for Cerebral Applications","authors":"H. Neves, Guy Orban, Milena Koudelka-Hep, Thomas Stieglitz, Patrick Ruther","doi":"10.1109/CNE.2007.369623","DOIUrl":"https://doi.org/10.1109/CNE.2007.369623","url":null,"abstract":"Recordings from the brain have been used for decades to investigate the activity of individual neurons. However, the complex interaction between electrical and chemical signals with respect to short and long term changes of morphology and information transfer is still poorly understood. We introduce a new modular approach for multifunctional probe arrays for cerebral applications that will enable the addressing of fundamental questions in neuroscience. Our approach allows the individual assembly of multiple probes with customized architecture into three-dimensional arrays to address specific brain regions, including sulci of highly folded cortices such as those of humans. In this paper, we introduce the system approach that allows the integration of recording and stimulation electrodes, biosensors, microfluidics and integrated electronics, all sharing a common backbone. We present the first prototypes of multichannel electrodes, flexible ribbon cables, a backbone platform and the first telemetry unit.","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":"130410054","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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