A SQUID System for Measurement of Spinal Cord Evoked Field of Supine Subjects

Adachi, Yoshiaki; Kawai, Jun; Miyamoto, Masatoshi; Ogata, H.; Tomori, Masaki; Kawabata, Shigenori; Sato, Tomoya; Uehara, Gen

doi:10.1109/tasc.2009.2019199

Cited by 26 publications

(15 citation statements)

References 16 publications

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“…We first simulated spinal cord evoked field (SCEF) measurements distorted by large stimulus-induced artifacts. For data generation, we assumed the sensor array of a 120-channel biomagnetometer [5][7], which has been specifically developed for SCEF measurements. The biomagnetometer is equipped with 40 vector sensors 6 , which are arranged at 8 × 5 measurement locations covering a 14 cm×9 cm area.…”

Section: Computer Simulationmentioning

confidence: 99%

Dual signal subspace projection (DSSP): a novel algorithm for removing large interference in biomagnetic measurements

et al. 2016

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Objective In functional electrophysiological imaging, signals are often contaminated by interference that can be of considerable magnitude compared to the signals of interest. This paper proposes a novel algorithm for removing such interferences that does not require separate noise measurements. Approach The algorithm is based on a dual definition of the signal subspace in the spatial- and time-domains. Since the algorithm makes use of this duality, it is named the dual signal subspace projection (DSSP). The DSSP algorithm first projects the columns of the measured data matrix onto the inside and outside of the spatial-domain signal subspace, creating a set of two preprocessed data matrices. The intersection of the row spans of these two matrices is estimated as the time-domain interference subspace. The original data matrix is projected onto the subspace that is orthogonal to the interference subspace Main results The DSSP algorithm is validated first by using the computer generated data, and then by using two sets of real biomagnetic data: SCEF data measured from a healthy volunteer and MEG data from a patient with a vagus nerve stimulator. Significance The proposed DSSP algorithm is effective for removing overlapped interference in a wide variety of biomagnetic measurements.

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Section: Computer Simulationmentioning

confidence: 99%

Dual signal subspace projection (DSSP): a novel algorithm for removing large interference in biomagnetic measurements

et al. 2016

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“…There has been growing interests in developing biomagnetometers optimized for measuring the spinal cord–evoked magnetic field (SCEF). 2,3 Dynamic (spatiotemporal) source imaging of the spinal cord electrophysiological activity from its evoked magnetic field has also been investigated, aiming at developing a novel, diagnostic imaging tool for cervical spinal cord disorders. 4…”

Section: Poster Presentationsmentioning

confidence: 99%

Abstracts of Presentations at the International Conference on Basic and Clinical Multimodal Imaging (BaCI), a Joint Conference of the International Society for Neuroimaging in Psychiatry (ISNIP), the International Society for Functional Source Imaging (ISFSI), the International Society for Bioelectromagnetism (ISBEM), the International Society for Brain Electromagnetic Topography (ISBET), and the EEG and Clinical Neuroscience Society (ECNS), in Geneva, Swit

2013

Clin EEG Neurosci

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A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance. However, BCI systems that are applicable in real-time and outside clinical settings require high performance. Therefore, we have to improve the current methods for feature extraction. In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a linearly constrained minimum variance (LCMV) beamformer 1 . Beamformers allow for easy incorporation of anatomical data and are applicable in real-time. A 32-channel EEG-BCI system was designed for a two-class motor imagery (MI) paradigm. We optimized a synchronous system for two untrained subjects and investigated two aspects. First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method 2 (FDM) head model including white and gray matter, CSF, scalp and skull tissue. Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed. On the one hand, they were chosen around electrodes C3 and C4, as hand MI activity theoretically is expected here. On the other hand, they were constructed based on the actual activated regions identified by an fMRI scan. Subsequently, an asynchronous system was derived for one of the subjects and an optimal balance between speed and accuracy was found. Lastly, a real-time application was made. These systems were evaluated by their accuracy, defined as the percentage of correct left and right classifications. From the real-time application, the information transfer rate 3 (ITR) was also determined. An accuracy of 86.60 ± 4.40% was achieved for subject 1 and 78.71 ± 0.73% for subject 2. This gives an average accuracy of 82.66 ± 2.57%. We found that the use of a personalized FDM model improved the accuracy of the system, on average 24.22% with respect to the template BEM model and on average 5.15% with respect to the personalized BEM model. Including fMRI spatial priors did not improve accuracy. Personal finetuning largely resolved the robustness problems arising due to the differences in head geometry and neurophysiology between subjects. A real-time average accuracy of 64.26% was reached and the maximum ITR was 6.71 bits/min. We conclude that beamformers calculated with a personalized FDM model have great potential to ameliorate feature extraction and, as a consequence, to improve the performance of real-time BCI systems.

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“…Proposed but as yet unproven techniques include electrical, magnetic, and optical recordings. 125,126 Below the transection, recordings from dorsal root ganglia or dorsal horns could provide invaluable feedback information for FES reanimation of paralyzed muscles. Systems have been proposed that could restore bipedal locomotion after spinal cord injury (SCI) using electronic central pattern generators (CPGs) that perform microstimulation of ventral horns, guided by appropriate sensory feedback.…”

Section: 3) Anatomic Target Regions For Neural Signals In Bmismentioning

confidence: 99%

Brain-Machine Interfaces for Motor Control: A Guide for Neuroscience Clinicians

Martín

Sankar²,

Lipsman³

et al. 2012

Can. J. Neurol. Sci.

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Brain-machine interfaces (BMIs) have been defined as devices that detect intent-typically intended movement-from brain activity, and translate it into an output action, such as control of a cursor on a screen or a robotic arm. There is no doubt that the potential of BMI technology to augment normal motor performance is vast, a fact which has attracted both attention and funding from a variety of sources including United States military. [1][2][3] For clinicians in the neurosciences, however, BMI technology could have the potential to assist in motor recovery or maintenance. At present, several forms of BMI devices, in varying stages of development, are being investigated and used to treat conditions that cause severe motor impairments, including amyotrophic lateral sclerosis (ALS), stroke, and spinal cord injury. [4][5][6][7][8][9] The preliminary experiences of several groups with motor BMI applications in clinical settings have generated justifiable enthusiasm about the promise of future BMI applications. Such enthusiasm, however, is tempered by a definite need for further advances in a number of specific aspects ABSTRACT: With the growing interdependence between medicine and technology, the prospect of connecting machines to the human brain is rapidly being realized. The field of neuroprosthetics is transitioning from the proof of concept stage to the development of advanced clinical treatments. In one area of brain-machine interfaces (BMIs) related to the motor system, also termed 'motor neuroprosthetics', research successes with implanted microelectrodes in animals have demonstrated immense potential for restoring motor deficits. Early human trials have also begun, with some success but also highlighting several technical challenges. Here we review the concepts and anatomy underlying motor BMI designs, review their early use in clinical applications, and offer a framework to evaluate these technologies in order to predict their eventual clinical utility. Ultimately, we hope to help neuroscience clinicians understand and participate in this burgeoning field.RÉSUMÉ: Guide sur les interface cerveau-machine pour le contrôle moteur destiné aux cliniciens en neurosciences. Étant donné l'interdépendance croissante de la médecine et de la technologie, la perspective de brancher des machines au cerveau humain se réalise rapidement. Le domaine de la neuroprothétique évolue maintenant de l'étape de la validation du principe à celui du développement de traitements cliniques de pointe. Dans le domaine des interfaces cerveau-machine (ICM) pour le système moteur, aussi connu sous le nom de "neuroprothétique motrice", les succès de la recherche sur les microélectrodes implantées chez des animaux ont démontré un potentiel immense pour pallier aux déficits moteurs. Des essais préliminaires chez les humains ont remporté un certain succès, mais ils ont aussi mis en évidence plusieurs défis techniques. Nous revoyons ici les concepts et l'anatomie pertinents à la conception des ICM motrices et leur utilisation préliminaire en cl...

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A SQUID System for Measurement of Spinal Cord Evoked Field of Supine Subjects

Cited by 26 publications

References 16 publications

Dual signal subspace projection (DSSP): a novel algorithm for removing large interference in biomagnetic measurements

Dual signal subspace projection (DSSP): a novel algorithm for removing large interference in biomagnetic measurements

Brain-Machine Interfaces for Motor Control: A Guide for Neuroscience Clinicians

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