Fronto-Parietal Gamma-Oscillations are a Cause of Performance Variation in Brain-Computer Interfacing
In recent work, we have provided evidence that fronto-parietal γ-oscillations of the electromagnetic field of the brain modulate the sensorimotor-rhythm. It is unclear, however, what impact this effect may have on explaining and addressing within-subject performance variations of brain-computer interfaces (BCIs). In this paper, we provide evidence that on a group-average classification accuracies in a two-class motor-imagery paradigm differ by up to 22.2% depending on the state of fronto-parietal γ-power. As such, this effect may have a large impact on the design of future BCI-systems. We further investigate whether adapting classification procedures to the current state of γ-power improves classification accuracy, and discuss other approaches to exploiting this effect.
| Author(s): | Grosse-Wentrup, M. |
| Pages: | 384-387 |
| Year: | 2011 |
| Month: | May |
| Day: | 0 |
| Publisher: | IEEE |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Piscataway, NJ, USA |
| DOI: | 10.1109/NER.2011.5910567 |
| Event Name: | 5th International IEEE/EMBS Conference on Neural Engineering (NER 2011) |
| Event Place: | Cancun, Mexico |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 978-1-4244-4140-2 |
| Links: | |
BibTex
@inproceedings{GrosseWentrup2011,
title = {Fronto-Parietal Gamma-Oscillations are a Cause of Performance Variation in Brain-Computer Interfacing},
abstract = {In recent work, we have provided evidence that fronto-parietal γ-oscillations of the electromagnetic field of the brain modulate the sensorimotor-rhythm. It is unclear, however, what impact this effect may have on explaining and addressing within-subject performance variations of brain-computer interfaces (BCIs). In this paper, we provide evidence that on a group-average classification accuracies in a two-class motor-imagery paradigm differ by up to 22.2% depending on the state of fronto-parietal γ-power. As such, this effect may have a large impact on the design of future BCI-systems. We further investigate whether adapting classification procedures to the current state of γ-power improves classification accuracy, and discuss other approaches to exploiting this effect.},
pages = {384-387},
publisher = {IEEE},
address = {Piscataway, NJ, USA},
month = may,
year = {2011},
slug = {grossewentrup2011},
author = {Grosse-Wentrup, M.},
month_numeric = {5}
}