Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146546
C. Herff, Ole Fortmann, C. Tse, Xiaoqin Cheng, F. Putze, D. Heger, Tanja Schultz
In this study, we show that both electroencephalograhy (EEG) and functional Near-Infrared Spectroscopy (fNIRS) can be used to discriminate between 5 levels of memory load. We induce memory load with the memory updating task, which is known to robustly generate memory load and allows us to define 5 different levels of load. Typical experiments only discriminate between low and high workload or up to a maximum of three classes. To the best of our knowledge, the memory updating task has not been used in combination with brain activity measurements before. Here, accuracies of up to 93% are achieved for the binary classification between very high and very low workload. On average, two levels of workload could be discriminated with 74% accuracy. Classification between the full five classes yielded 44% accuracy on average. Despite the fact that EEG results consistently outperformed the results obtained with fNIRS, we could show that the feature-level fusion of both modalities increased robustness of classification results. A reliable discrimination between different levels of memory load could be used to adapt user interfaces or present the right amount of information to a learner.
{"title":"Hybrid fNIRS-EEG based discrimination of 5 levels of memory load","authors":"C. Herff, Ole Fortmann, C. Tse, Xiaoqin Cheng, F. Putze, D. Heger, Tanja Schultz","doi":"10.1109/NER.2015.7146546","DOIUrl":"https://doi.org/10.1109/NER.2015.7146546","url":null,"abstract":"In this study, we show that both electroencephalograhy (EEG) and functional Near-Infrared Spectroscopy (fNIRS) can be used to discriminate between 5 levels of memory load. We induce memory load with the memory updating task, which is known to robustly generate memory load and allows us to define 5 different levels of load. Typical experiments only discriminate between low and high workload or up to a maximum of three classes. To the best of our knowledge, the memory updating task has not been used in combination with brain activity measurements before. Here, accuracies of up to 93% are achieved for the binary classification between very high and very low workload. On average, two levels of workload could be discriminated with 74% accuracy. Classification between the full five classes yielded 44% accuracy on average. Despite the fact that EEG results consistently outperformed the results obtained with fNIRS, we could show that the feature-level fusion of both modalities increased robustness of classification results. A reliable discrimination between different levels of memory load could be used to adapt user interfaces or present the right amount of information to a learner.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127374007","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146818
Miguel Rodrigues Mendes, N. Subramaniyam, Katrina Wendel-Mitoraj
This paper studies the effect of subdermal EEG lead placement on measurement sensitivity distributions, and compares them with the sensitivity distributions obtained using surface EEG leads. A five-layered isotropic head model was constructed based on magnetic resonance imaging (MRI) data. The surface electrodes were placed on the scalp of the model according to the traditional 10-20 EEG system. The subdermal electrodes were arranged in 5 × 5 grids and placed on the skull in seven reference locations: FZ, CZ, OZ, T3, T4, P3, and P4. The effects on the measurement sensitivity were studied by means of the half-sensitivity volume (HSV). For the surface measurements, the size of the HSV varies around 1 cm3, while the subdermal leads can concentrate the measurement in regions ten times smaller. The results indicate that the EEG measurement may benefit from subdermal implantation since the subdermal measurements are more accurate and specific than the surface measurements. Nevertheless, the improvement was registered only for the subdermal grids centred on CZ, T3 and T4 locations. This suggests that the subdermal electrode performance highly depends on the thickness of the underlying matter, such as the skull and cerebrospinal fluid (CSF).
{"title":"Evaluating the electrode measurement sensitivity of subdermal electroencephalography electrodes","authors":"Miguel Rodrigues Mendes, N. Subramaniyam, Katrina Wendel-Mitoraj","doi":"10.1109/NER.2015.7146818","DOIUrl":"https://doi.org/10.1109/NER.2015.7146818","url":null,"abstract":"This paper studies the effect of subdermal EEG lead placement on measurement sensitivity distributions, and compares them with the sensitivity distributions obtained using surface EEG leads. A five-layered isotropic head model was constructed based on magnetic resonance imaging (MRI) data. The surface electrodes were placed on the scalp of the model according to the traditional 10-20 EEG system. The subdermal electrodes were arranged in 5 × 5 grids and placed on the skull in seven reference locations: FZ, CZ, OZ, T3, T4, P3, and P4. The effects on the measurement sensitivity were studied by means of the half-sensitivity volume (HSV). For the surface measurements, the size of the HSV varies around 1 cm3, while the subdermal leads can concentrate the measurement in regions ten times smaller. The results indicate that the EEG measurement may benefit from subdermal implantation since the subdermal measurements are more accurate and specific than the surface measurements. Nevertheless, the improvement was registered only for the subdermal grids centred on CZ, T3 and T4 locations. This suggests that the subdermal electrode performance highly depends on the thickness of the underlying matter, such as the skull and cerebrospinal fluid (CSF).","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124834779","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146794
P. Reddy, Saurabh Shukla, A. Karunarathne, S. Jana, L. Giri
The variability and complex dynamics of cell morphology make the automated segmentation of neurons in microscopic images a rather difficult task. To fully leverage modern computational power in large-scale analysis of such biological images, automation is necessary. In this paper, we present an automated approach to segmenting individual cells from their surroundings, and test it on time-lapse images of hipppocampal neurons during neurite initiation and extension. Noting that active contour based methods are usually accurate, but computationally expensive and slow, we propose a fast hybrid approach that combines Chan-Vese active contour segmentation with Bayesian thresholding for segmentation of neuron and measurement of neurite growth dynamics. Our approach demonstrated upto two-hundred-fold faster quantification of growth dynamics compared to the pure Chan-Vese segmentation.
{"title":"Segmentation of neuron and measurement of optically programed neurite growth: Fast automation via Bayesian thresholding","authors":"P. Reddy, Saurabh Shukla, A. Karunarathne, S. Jana, L. Giri","doi":"10.1109/NER.2015.7146794","DOIUrl":"https://doi.org/10.1109/NER.2015.7146794","url":null,"abstract":"The variability and complex dynamics of cell morphology make the automated segmentation of neurons in microscopic images a rather difficult task. To fully leverage modern computational power in large-scale analysis of such biological images, automation is necessary. In this paper, we present an automated approach to segmenting individual cells from their surroundings, and test it on time-lapse images of hipppocampal neurons during neurite initiation and extension. Noting that active contour based methods are usually accurate, but computationally expensive and slow, we propose a fast hybrid approach that combines Chan-Vese active contour segmentation with Bayesian thresholding for segmentation of neuron and measurement of neurite growth dynamics. Our approach demonstrated upto two-hundred-fold faster quantification of growth dynamics compared to the pure Chan-Vese segmentation.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123262966","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146805
Utkarsh Jindal, Mehak Sood, Abhijit Das, S. R. Chowdhury, Anirban Dutta
Cerebral vascular status can be evaluated with cerebrovascular reactivity (CVR) that reflects the capacity of blood vessels to dilate, and is an important marker for brain vascular reserve. Here, transcranial direct current stimulation (tDCS) can up- and down- regulate cortical excitability depending on current direction, and anodal tDCS can increase regional cerebral blood flow during stimulation. Impairments in CVR have been associated with increased risk of ischemic events. Here, near-infrared spectroscopy (NIRS) is a cerebral monitoring method that can be used for non-invasive and continuous measurement of cerebral vascular status under various clinical conditions. This paper describes the development of a 4-channel continuous wave NIRS combined with tDCS in an FPGA-based hardware that captured the hemodynamic changes in the frontal cortex of the brain, as a measure of CVR, before and after anodal tDCS. We recruited 14 patients with established and acute ischemic stroke (<;1 month) localized to a single hemisphere (10 male and 4 females from age 42 to 73). The affected hemisphere with impaired circulation showed significantly less (0.26 +/- 0.28), p<;0.01, change in cerebral hemoglobin oxygenation than the healthy side (3.43+/- 0.86) in response to anodal tDCS. Thus, combining NIRS with tDCS can lend to low-cost point of care testing of cerebral vascular status so we present a NIRS-tDCS based adaptive neuro-fuzzy inference system implemented in a FPGA-based hardware.
{"title":"Near infra-red spectroscopy combined with transcranial direct current stimulation in FPGA-based hardware for point of care testing of cerebral vascular status - A stroke study","authors":"Utkarsh Jindal, Mehak Sood, Abhijit Das, S. R. Chowdhury, Anirban Dutta","doi":"10.1109/NER.2015.7146805","DOIUrl":"https://doi.org/10.1109/NER.2015.7146805","url":null,"abstract":"Cerebral vascular status can be evaluated with cerebrovascular reactivity (CVR) that reflects the capacity of blood vessels to dilate, and is an important marker for brain vascular reserve. Here, transcranial direct current stimulation (tDCS) can up- and down- regulate cortical excitability depending on current direction, and anodal tDCS can increase regional cerebral blood flow during stimulation. Impairments in CVR have been associated with increased risk of ischemic events. Here, near-infrared spectroscopy (NIRS) is a cerebral monitoring method that can be used for non-invasive and continuous measurement of cerebral vascular status under various clinical conditions. This paper describes the development of a 4-channel continuous wave NIRS combined with tDCS in an FPGA-based hardware that captured the hemodynamic changes in the frontal cortex of the brain, as a measure of CVR, before and after anodal tDCS. We recruited 14 patients with established and acute ischemic stroke (<;1 month) localized to a single hemisphere (10 male and 4 females from age 42 to 73). The affected hemisphere with impaired circulation showed significantly less (0.26 +/- 0.28), p<;0.01, change in cerebral hemoglobin oxygenation than the healthy side (3.43+/- 0.86) in response to anodal tDCS. Thus, combining NIRS with tDCS can lend to low-cost point of care testing of cerebral vascular status so we present a NIRS-tDCS based adaptive neuro-fuzzy inference system implemented in a FPGA-based hardware.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116421928","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146627
A. Gibaldi, A. Canessa, S. Sabatini
A neural network architecture able to autonomously learn effective disparity-vergence responses and drive the vergence eye movements of a simulated binocular active vision system is proposed. The proposed approach, instead of exploiting purposely designed resources, relies on the direct use of a set of real disparity tuning curves, measured in the primary visual cortex of two macaque monkeys and courteously made available by (Prince et al., 2002), that provides a distributed representation of binocular disparity. The network evolves following a differential Hebbian rule that exploits the overall population activity to measure the state of the system, i.e. the deviation from the desired vergence position, so as its modification as a consequence of the action performed. Accordingly, the signal provides an effective intrinsic reward to develop a stable and accurate vergence behaviour. Emerging from a direct interaction of the sensing system with the environment, the resulting control provides a precise and accurate control for small disparities, as well as a raw control on a broader working range when large disparities are experienced. The developed control converges to a stable state that intrinsically and continuously adapts to the characteristics of the ongoing stimulation. The results proved how actually naturally distributed resources allows for robust and flexible learning capabilities in changeable situations.
提出了一种能够自主学习有效视差-收敛响应并驱动双目主动视觉系统收敛眼运动的神经网络结构。所提出的方法，而不是利用故意设计的资源，依赖于直接使用一组真实的视差调节曲线，在两只猕猴的初级视觉皮层中测量，并由(Prince et al.， 2002)提供，它提供了双眼视差的分布表示。网络遵循微分Hebbian规则发展，该规则利用总体人口活动来测量系统的状态，即偏离期望的收敛位置，因此它的修改是执行动作的结果。因此，信号提供了一个有效的内在奖励，以发展稳定和准确的收敛行为。从传感系统与环境的直接相互作用中产生，由此产生的控制提供了对小差异的精确控制，以及在经历大差异时对更广泛工作范围的原始控制。所开发的控制收敛到稳定状态，该状态本质上连续地适应正在进行的刺激的特征。结果证明了自然分布的资源如何在多变的情况下提供强大而灵活的学习能力。
{"title":"Vergence control learning through real V1 disparity tuning curves","authors":"A. Gibaldi, A. Canessa, S. Sabatini","doi":"10.1109/NER.2015.7146627","DOIUrl":"https://doi.org/10.1109/NER.2015.7146627","url":null,"abstract":"A neural network architecture able to autonomously learn effective disparity-vergence responses and drive the vergence eye movements of a simulated binocular active vision system is proposed. The proposed approach, instead of exploiting purposely designed resources, relies on the direct use of a set of real disparity tuning curves, measured in the primary visual cortex of two macaque monkeys and courteously made available by (Prince et al., 2002), that provides a distributed representation of binocular disparity. The network evolves following a differential Hebbian rule that exploits the overall population activity to measure the state of the system, i.e. the deviation from the desired vergence position, so as its modification as a consequence of the action performed. Accordingly, the signal provides an effective intrinsic reward to develop a stable and accurate vergence behaviour. Emerging from a direct interaction of the sensing system with the environment, the resulting control provides a precise and accurate control for small disparities, as well as a raw control on a broader working range when large disparities are experienced. The developed control converges to a stable state that intrinsically and continuously adapts to the characteristics of the ongoing stimulation. The results proved how actually naturally distributed resources allows for robust and flexible learning capabilities in changeable situations.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114572956","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146719
Anne M. Leijsen, Alejandro Lopez-Valdes, Myles McLaughlin, Jaclyn Smith, L. Viani, P. Walshe, R. Reilly
Recent evidence suggests that cortical auditory evoked potentials recorded by EEG may be used to obtain an objective measure of spectral sound processing abilities in cochlear implant (CI) users. As speech perception depends on both spectral and temporal processing abilities, developing an objective measure of sound processing in the temporal domain is necessary for a complete evaluation of CI speech performance. This study explored the feasibility to objectively assess sound processing in the temporal domain employing a method based on EEG and complex temporal stimuli such as the Schroeder-phase harmonic complexes. Psychoacoustic discrimination abilities were measured employing a four-interval two-alternative forced choice paradigm. Neural discrimination abilities were measured by recording single-channel EEG during an unattended oddball paradigm. Psychoacoustic and neural discrimination abilities were analyzed for correlation. A strong, but non-significant, correlation was found in three out of six subjects. Schroeder-phase harmonic complexes may have utility as stimuli in the development of an objective measure of temporal processing in CI users. Furthermore, they provide new insights on temporal processing in CI users that may benefit the development of the CI.
{"title":"An approach to develop an objective measure of temporal processing in cochlear implant users based on Schroeder-phase harmonic complexes","authors":"Anne M. Leijsen, Alejandro Lopez-Valdes, Myles McLaughlin, Jaclyn Smith, L. Viani, P. Walshe, R. Reilly","doi":"10.1109/NER.2015.7146719","DOIUrl":"https://doi.org/10.1109/NER.2015.7146719","url":null,"abstract":"Recent evidence suggests that cortical auditory evoked potentials recorded by EEG may be used to obtain an objective measure of spectral sound processing abilities in cochlear implant (CI) users. As speech perception depends on both spectral and temporal processing abilities, developing an objective measure of sound processing in the temporal domain is necessary for a complete evaluation of CI speech performance. This study explored the feasibility to objectively assess sound processing in the temporal domain employing a method based on EEG and complex temporal stimuli such as the Schroeder-phase harmonic complexes. Psychoacoustic discrimination abilities were measured employing a four-interval two-alternative forced choice paradigm. Neural discrimination abilities were measured by recording single-channel EEG during an unattended oddball paradigm. Psychoacoustic and neural discrimination abilities were analyzed for correlation. A strong, but non-significant, correlation was found in three out of six subjects. Schroeder-phase harmonic complexes may have utility as stimuli in the development of an objective measure of temporal processing in CI users. Furthermore, they provide new insights on temporal processing in CI users that may benefit the development of the CI.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122215619","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146637
Daniele Poli, Vito Paolo Pastore, P. Massobrio, S. Martinoia
Goal of this work is to present a general approach to estimate functional connectivity in in vitro cortical networks coupled to Micro-Electrode Array (MEAs). Specifically, we developed and optimized a Partial Correlation (PC) based algorithm and we compared it to Cross Correlation (CC) and Transfer Entropy (TE) methods. First, we applied the algorithms to simulated networks with different average connectivity degrees. Second, we used a specific validation procedure based on the accuracy coefficient (ACC) to evaluate the algorithm's performances and we found Partial Correlation to be the best method to infer functional connections from spiking activity of in vitro cortical networks. Finally, we used PC to estimate connectivity during development (i.e., from 2nd to 4th week) from recordings of cortical networks coupled to MEAs.
{"title":"Functional connectivity in cultured cortical networks during development: Comparison between correlation and information theory-based algorithms","authors":"Daniele Poli, Vito Paolo Pastore, P. Massobrio, S. Martinoia","doi":"10.1109/NER.2015.7146637","DOIUrl":"https://doi.org/10.1109/NER.2015.7146637","url":null,"abstract":"Goal of this work is to present a general approach to estimate functional connectivity in in vitro cortical networks coupled to Micro-Electrode Array (MEAs). Specifically, we developed and optimized a Partial Correlation (PC) based algorithm and we compared it to Cross Correlation (CC) and Transfer Entropy (TE) methods. First, we applied the algorithms to simulated networks with different average connectivity degrees. Second, we used a specific validation procedure based on the accuracy coefficient (ACC) to evaluate the algorithm's performances and we found Partial Correlation to be the best method to infer functional connections from spiking activity of in vitro cortical networks. Finally, we used PC to estimate connectivity during development (i.e., from 2nd to 4th week) from recordings of cortical networks coupled to MEAs.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129523923","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146733
Y. Aung, K. Anam, Adel Al-Jumaily
Continuous prediction of dynamic joint angle from surface electromyography (sEMG) signal is one of the most important applications in rehabilitation area for stroke survivors as these can directly reflect the user motor intention. In this study, new shoulder joint angle prediction method in real-time based on the biosignal: sEMG is proposed. Firstly, sEMG to muscle activation model is built up to extract the user intention from contracted muscles and then feed into the extreme learning machine (ELM) to estimate the angle in real-time continuously. The estimated joint angle is then compare with the webcam captured joint angle to analyze the effectiveness of the proposed method. The result reveals that correlation coefficient between actual angle and estimated angle is as high as 0.96 in offline and 0.93 in online mode. In addition, the processing time for the estimation is less than 32ms in both cases which is within the semblance of human natural movements. Therefore, the proposed method is able to predict the user intended movement very well and naturally and hence, it is suitable for real-time applications.
{"title":"Continuous prediction of shoulder joint angle in real-time","authors":"Y. Aung, K. Anam, Adel Al-Jumaily","doi":"10.1109/NER.2015.7146733","DOIUrl":"https://doi.org/10.1109/NER.2015.7146733","url":null,"abstract":"Continuous prediction of dynamic joint angle from surface electromyography (sEMG) signal is one of the most important applications in rehabilitation area for stroke survivors as these can directly reflect the user motor intention. In this study, new shoulder joint angle prediction method in real-time based on the biosignal: sEMG is proposed. Firstly, sEMG to muscle activation model is built up to extract the user intention from contracted muscles and then feed into the extreme learning machine (ELM) to estimate the angle in real-time continuously. The estimated joint angle is then compare with the webcam captured joint angle to analyze the effectiveness of the proposed method. The result reveals that correlation coefficient between actual angle and estimated angle is as high as 0.96 in offline and 0.93 in online mode. In addition, the processing time for the estimation is less than 32ms in both cases which is within the semblance of human natural movements. Therefore, the proposed method is able to predict the user intended movement very well and naturally and hence, it is suitable for real-time applications.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129439902","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146678
Matthias Müller, M. Ulloa, M. Schuettler, T. Stieglitz
As cleanroom fabricated polyimide based electrodes are commonly used in clinical trials due to their small dimensions and high flexibility we want to translate these advantages to a maskless manufacturing technology and another substrate material with FDA approval: Parlyene C. Using a picosecond laser (355 nm Nd:YVO4) an established laser fabrication process (1064 nm Nd:YAG nansecond laser) for silicone rubber electrodes was modified to allow the fabrication of thin parylene C electrodes for intrafascicular application. The process utilizes a 25 μm thick platinum iridium foil that is placed between two 10 μm thick parylene C substrate layers. Using the laser for thinning down the metal, increasing the active surface and cutting the complete electrode array a new fabrication process is developed. Adhesion parameters of the involved materials are measured and tailored to fit each other. The single electrode arrays feature 4 intrafascicular contacts as well as a ground electrode and fixation openings outside the nerve. Functionality of the electrode array was measured and a first assessment of its usability has been performed. The mechanical and electrochemical parameters are promising for intrafascicular implantation, successful stimulation and recording application in a peripheral nerve.
{"title":"Development of a single-sided Parylene C based intrafascicular multichannel electrode for peripheral nerves","authors":"Matthias Müller, M. Ulloa, M. Schuettler, T. Stieglitz","doi":"10.1109/NER.2015.7146678","DOIUrl":"https://doi.org/10.1109/NER.2015.7146678","url":null,"abstract":"As cleanroom fabricated polyimide based electrodes are commonly used in clinical trials due to their small dimensions and high flexibility we want to translate these advantages to a maskless manufacturing technology and another substrate material with FDA approval: Parlyene C. Using a picosecond laser (355 nm Nd:YVO4) an established laser fabrication process (1064 nm Nd:YAG nansecond laser) for silicone rubber electrodes was modified to allow the fabrication of thin parylene C electrodes for intrafascicular application. The process utilizes a 25 μm thick platinum iridium foil that is placed between two 10 μm thick parylene C substrate layers. Using the laser for thinning down the metal, increasing the active surface and cutting the complete electrode array a new fabrication process is developed. Adhesion parameters of the involved materials are measured and tailored to fit each other. The single electrode arrays feature 4 intrafascicular contacts as well as a ground electrode and fixation openings outside the nerve. Functionality of the electrode array was measured and a first assessment of its usability has been performed. The mechanical and electrochemical parameters are promising for intrafascicular implantation, successful stimulation and recording application in a peripheral nerve.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130283466","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}
Pub Date : 2015-04-22DOI: 10.1109/NER.2015.7146775
S. Hsu, T. Mullen, T. Jung, G. Cauwenberghs
The needs for online Independent Component Analysis (ICA) algorithms arise in a range of fields such as continuous clinical assessment and brain-computer interface (BCI). Among the online ICA methods, online recursive ICA algorithm (ORICA) has attractive properties of fast convergence and low computational complexity. However, there hasn't been a systematic comparison between an online ICA method such as ORICA and other offline (batch-mode) ICA algorithms on real EEG data. This study compared ORICA with ten ICA algorithms in terms of their decomposition quality, validity of source characteristics, and computational complexity on the thirteen experimental 71-ch EEG datasets. Empirical results showed that ORICA achieved higher mutual information reduction (MIR) and extracted more near-dipolar sources than algorithms such as FastICA, JADE, and SOBI did while the performance of ORICA approached that of the best-performed Infomax-based algorithms. Furthermore, ORICA outperforms most of ICA methods in terms of the computational complexity. The properties of fast convergence and low computational complexity of ORICA enable the realization of real-time online ICA process, which has further applications such as real-time functional neuroimaging, artifact reduction, and adaptive BCI.
{"title":"Validating online recursive independent component analysis on EEG data","authors":"S. Hsu, T. Mullen, T. Jung, G. Cauwenberghs","doi":"10.1109/NER.2015.7146775","DOIUrl":"https://doi.org/10.1109/NER.2015.7146775","url":null,"abstract":"The needs for online Independent Component Analysis (ICA) algorithms arise in a range of fields such as continuous clinical assessment and brain-computer interface (BCI). Among the online ICA methods, online recursive ICA algorithm (ORICA) has attractive properties of fast convergence and low computational complexity. However, there hasn't been a systematic comparison between an online ICA method such as ORICA and other offline (batch-mode) ICA algorithms on real EEG data. This study compared ORICA with ten ICA algorithms in terms of their decomposition quality, validity of source characteristics, and computational complexity on the thirteen experimental 71-ch EEG datasets. Empirical results showed that ORICA achieved higher mutual information reduction (MIR) and extracted more near-dipolar sources than algorithms such as FastICA, JADE, and SOBI did while the performance of ORICA approached that of the best-performed Infomax-based algorithms. Furthermore, ORICA outperforms most of ICA methods in terms of the computational complexity. The properties of fast convergence and low computational complexity of ORICA enable the realization of real-time online ICA process, which has further applications such as real-time functional neuroimaging, artifact reduction, and adaptive BCI.","PeriodicalId":137451,"journal":{"name":"2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130548668","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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