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Cited by 49EEG and Brain-Computer InterfacesNeural dynamics and brain functionMuscle activation and electromyography studies

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The role of ECoG magnitude and phase in decoding position, velocity, and acceleration during continuous motor behavior

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Hammer, Jiri, Fischer, Jörg, Ruescher, Johanna, Schulze-Bonhage, Andreas, Aertsen, Ad, Ball, Tonio·2013·Frontiers in Neuroscience
DOI 10.3389/fnins.2013.00200PMC PMC3814578
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Abstract

In neuronal population signals, including the electroencephalogram (EEG) and electrocorticogram (ECoG), the low-frequency component (LFC) is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI) applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy) "copy" of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.

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  • The deep ConvNets as used here may have learned features different from FBCSP, which could explain their higher accuracies in the lower frequencies where band power features may be less important.

    “However, the deep ConvNets as used here may have learned features different from FBCSP, which could explain their higher accuracies in the lower frequencies where band power features may be less important [Hammer et al., 2013].”

    From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Hammer et al., 2013

  • Phase information is less important in the higher than the lower frequencies.

    “which is less important in the higher than the lower frequencies [Hammer et al., 2013]”

    From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Hammer et al., 2013

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Metadata

Authors
Hammer, J., Fischer, J., Ruescher, J., Schulze-Bonhage, A., Aertsen, A., & Ball, T.
Affiliations
University of Freiburg (DE)
ORCID
Aertsen, Ad
Venue
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Published
2013
Volume
7
Pages
200-200
ISSN
1662-453X (ISSN-L)
DOI
10.3389/fnins.2013.00200
PMC
PMC3814578
OpenAlex
W1972999291
Cited by
49
References
5
Citations by year
2026: 1 · 2025: 4 · 2024: 1 · 2023: 2 · 2022: 8 · 2021: 4 · 2020: 6 · 2019: 6 · 2018: 5 · 2017: 6 · 2016: 3 · 2015: 1 · 2014: 1 · 2013: 1
FWCI
1.10
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
Funders
  • Deutsche Forschungsgemeinschaft (EXC-1086 BrainLinks-BrainTools, EXC‐1086)
  • Bundesministerium für Bildung und Forschung (0313891, 0316064C)
  • Albert-Ludwigs-Universität Freiburg (EXC 1086)
  • BrainLinks-BrainTools
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Keywords
electrocorticography, low-frequency components, brain-computer interface, hand position, hand velocity, hand acceleration

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