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High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays

Overview of attention for article published in Frontiers in Neural Circuits, April 2017
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Title
High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays
Published in
Frontiers in Neural Circuits, April 2017
DOI 10.3389/fncir.2017.00020
Pubmed ID
Authors

Taro Kaiju, Keiichi Doi, Masashi Yokota, Kei Watanabe, Masato Inoue, Hiroshi Ando, Kazutaka Takahashi, Fumiaki Yoshida, Masayuki Hirata, Takafumi Suzuki

Abstract

Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels. However, few studies have discussed the actual validity of high-density ECoG arrays. In this study, we developed novel high-density flexible ECoG arrays and conducted decoding analyses with monkey somatosensory evoked potentials (SEPs). Using MEMS technology, we made 96-channel Parylene electrode arrays with an inter-electrode distance of 700 μm and recording site area of 350 μm(2). The arrays were mainly placed onto the finger representation area in the somatosensory cortex of the macaque, and partially inserted into the central sulcus. With electrical finger stimulation, we successfully recorded and visualized finger SEPs with a high spatiotemporal resolution. We conducted offline analyses in which the stimulated fingers and intensity were predicted from recorded SEPs using a support vector machine. We obtained the following results: (1) Very high accuracy (~98%) was achieved with just a short segment of data (~15 ms from stimulus onset). (2) High accuracy (~96%) was achieved even when only a single channel was used. This result indicated placement optimality for decoding. (3) Higher channel counts generally improved prediction accuracy, but the efficacy was small for predictions with feature vectors that included time-series information. These results suggest that ECoG signals with high spatiotemporal resolution could enable greater decoding precision or external device control.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 161 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 23%
Researcher 27 17%
Student > Master 19 12%
Student > Bachelor 16 10%
Student > Doctoral Student 10 6%
Other 12 7%
Unknown 40 25%
Readers by discipline Count As %
Engineering 40 25%
Neuroscience 33 20%
Agricultural and Biological Sciences 9 6%
Physics and Astronomy 8 5%
Medicine and Dentistry 8 5%
Other 20 12%
Unknown 43 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 December 2019.
All research outputs
#7,805,085
of 24,226,848 outputs
Outputs from Frontiers in Neural Circuits
#461
of 1,268 outputs
Outputs of similar age
#118,815
of 314,037 outputs
Outputs of similar age from Frontiers in Neural Circuits
#13
of 26 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 314,037 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.