↓ Skip to main content

Coupling BCI and cortical stimulation for brain-state-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects

Overview of attention for article published in Frontiers in Neural Circuits, January 2012
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
2 X users

Citations

dimensions_citation
52 Dimensions

Readers on

mendeley
89 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Coupling BCI and cortical stimulation for brain-state-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects
Published in
Frontiers in Neural Circuits, January 2012
DOI 10.3389/fncir.2012.00087
Pubmed ID
Authors

Armin Walter, Ander R. Murguialday, Wolfgang Rosenstiel, Niels Birbaumer, Martin Bogdan

Abstract

Brain-state-dependent stimulation (BSDS) combines brain-computer interfaces (BCIs) and cortical stimulation into one paradigm that allows the online decoding for example of movement intention from brain signals while simultaneously applying stimulation. If the BCI decoding is performed by spectral features, stimulation after-effects such as artefacts and evoked activity present a challenge for a successful implementation of BSDS because they can impair the detection of targeted brain states. Therefore, efficient and robust methods are needed to minimize the influence of the stimulation-induced effects on spectral estimation without violating the real-time constraints of the BCI. In this work, we compared four methods for spectral estimation with autoregressive (AR) models in the presence of pulsed cortical stimulation. Using combined EEG-TMS (electroencephalography-transcranial magnetic stimulation) as well as combined electrocorticography (ECoG) and epidural electrical stimulation, three patients performed a motor task using a sensorimotor-rhythm BCI. Three stimulation paradigms were varied between sessions: (1) no stimulation, (2) single stimulation pulses applied independently (open-loop), or (3) coupled to the BCI output (closed-loop) such that stimulation was given only while an intention to move was detected using neural data. We found that removing the stimulation after-effects by linear interpolation can introduce a bias in the estimation of the spectral power of the sensorimotor rhythm, leading to an overestimation of decoding performance in the closed-loop setting. We propose the use of the Burg algorithm for segmented data to deal with stimulation after-effects. This work shows that the combination of BCIs controlled with spectral features and cortical stimulation in a closed-loop fashion is possible when the influence of stimulation after-effects on spectral estimation is minimized.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 1%
United States 1 1%
Russia 1 1%
Germany 1 1%
Unknown 85 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 24%
Researcher 15 17%
Student > Master 15 17%
Student > Bachelor 8 9%
Student > Doctoral Student 6 7%
Other 11 12%
Unknown 13 15%
Readers by discipline Count As %
Engineering 15 17%
Agricultural and Biological Sciences 13 15%
Computer Science 10 11%
Medicine and Dentistry 10 11%
Neuroscience 8 9%
Other 16 18%
Unknown 17 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 24 November 2012.
All research outputs
#15,256,901
of 22,687,320 outputs
Outputs from Frontiers in Neural Circuits
#775
of 1,209 outputs
Outputs of similar age
#163,202
of 244,125 outputs
Outputs of similar age from Frontiers in Neural Circuits
#25
of 73 outputs
Altmetric has tracked 22,687,320 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,209 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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 244,125 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 73 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 57% of its contemporaries.