High-resolution EEG techniques for brain-computer interface applications
Open accessAbstract
High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.
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Source features were generally more correlated with cued motor-related mental states than sensor features.
“Similar to these previous works, we found that, in general, source features were more correlated with cued motor-related mental states than sensor features (Fig. S9) (44, 45).”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [44]
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- Authors
- Cincotti, F., Mattia, D., Aloise, F., Bufalari, S., Astolfi, L., De Vico Fallani, F., Tocci, A., Bianchi, L., Marciani, M. G., Gao, S., Millan, J., & Babiloni, F.
- Affiliations
- Fondazione Santa Lucia (IT)
- University of Rome Tor Vergata (IT)
- Tsinghua University (CN)
- Corresponding author
- Babiloni, Fabio
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2008-01
- Volume
- 167
- Issue
- 1
- Pages
- 31-42
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2098951185
- Cited by
- 115
- References
- 36
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 5 · 2022: 2 · 2021: 7 · 2020: 5 · 2019: 6 · 2018: 12 · 2017: 9 · 2016: 9 · 2015: 8 · 2014: 9 · 2013: 9 · 2012: 4
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- European Commission (BM0601)
- Tsinghua University
- National Institutes of Health (GUP03562)
- National Institute of Biomedical Imaging and Bioengineering (R01EB006356, EB006356)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- high-resolution EEG, brain-computer interface, source localization, individual head model, Tikhonov regularization, motor imagery, sensorimotor rhythm, spectral properties, online BCI, distributed source model, current dipoles, Brodmann areas
- MeSH terms
- Adult, Biofeedback, Psychology, Brain, Brain Mapping, Communication Devices for People with Disabilities, Electrodes, Electroencephalography, Evoked Potentials, Somatosensory, Female, Humans, Male, Online Systems, Signal Processing, Computer-Assisted, User-Computer Interface, Evoked Potentials, Motor
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