Movement related activity in the high gamma range of the human EEG
Open accessAbstract
Electrocorticographic (ECoG) recordings obtained using intracranially implanted electrodes in epilepsy patients indicate that high gamma band (HGB) activity of sensorimotor cortex is focally increased during voluntary movement. These movement related HGB modulations may play an important role in sensorimotor cortex function. It is however currently not clear to what extent this type of neural activity can be detected using non-invasive electroencephalography (EEG) and how similar HGB responses in healthy human subjects are to those observed in epilepsy patients. Using the same arm reaching task, we have investigated spectral power changes both in intracranial ECoG recordings in epilepsy patients and in non-invasive EEG recordings optimized for detecting HGB activity in healthy subjects. Our results show a common HGB response pattern both in ECoG and EEG recorded above the sensorimotor cortex contralateral to the side of arm movement. In both cases, HGB activity increased around movement onset in the 60-90 Hz range and became most pronounced at reaching movement end. Additionally, we found EEG HGB activity above the frontal midline possibly generated by the anterior supplementary motor area (SMA), a region that was however not covered by the intracranial electrodes used in the present study. In summary, our findings show that HGB activity from human sensorimotor cortex can be non-invasively detected in healthy subjects using EEG, opening a new perspective for investigating the role of high gamma range neuronal activity both in function and dysfunction of the human cortical sensorimotor network.
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Amplitudes of alpha, beta and gamma bands provide class-discriminative information for motor tasks.
“It is known that the amplitudes, for example of the alpha, beta and gamma bands, provide class‐discriminative information for motor tasks [Ball et al., 2008; Pfurtscheller, 1981; Pfurtscheller and Aranibar, 1979].”
From Deep learning with convolutional neural networks for EEG decoding and visualization (2017) · cites this paper as Ball et al., 2008
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- Authors
- Ball, T., Demandt, E., Mutschler, I., Neitzel, E., Mehring, C., Vogt, K., Aertsen, A., & Schulze-Bonhage, A.
- Affiliations
- University of Freiburg (DE)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-06
- Volume
- 41
- Issue
- 2
- Pages
- 302-310
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2118872857
- Cited by
- 274
- References
- 53
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 9 · 2023: 10 · 2022: 19 · 2021: 11 · 2020: 24 · 2019: 13 · 2018: 25 · 2017: 16 · 2016: 18 · 2015: 17 · 2014: 17 · 2013: 17 · 2012: 14
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- edoc (University of Basel) (acceptedVersion)
- edoc (University of Basel)
- Funders
- Bundesministerium für Bildung und Forschung (01GQ0420)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- sensorimotor cortex, supplementary motor area, intracranial electrocorticography, epilepsy patients
- MeSH terms
- Adult, Brain, Brain Mapping, Electroencephalography, Epilepsy, Female, Humans, Male, Movement
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