SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in Brain–Computer Interfaces

Gysels, E.; Renevey, P.; Celka, P.

doi:10.1016/j.sigpro.2005.07.008

Cited by 64 publications

(37 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, studies reported in the literature have used linear discriminant analysis (Babiloni et al, 2000); neural networks (Huan and Palaniappan, 2004;Pfurtscheller et al, 1997); support vector machines (Garrett et al, 2003;Gysels et al, 2005;Lal et al, 2004;Müller et al, 2003); linear regression (McFarland et al, 1993(McFarland et al, , 1997a. These procedures are summarized in a recent review article on BCI feature extraction and translation methods .…”

Section: The Signal Identification Problemmentioning

confidence: 99%

Brain–computer interfaces (BCIs): Detection instead of classification

Schalk

Brunner

Gerhardt

et al. 2008

Journal of Neuroscience Methods

View full text Add to dashboard Cite

Section: The Signal Identification Problemmentioning

confidence: 99%

Brain–computer interfaces (BCIs): Detection instead of classification

Schalk

Brunner

Gerhardt

et al. 2008

Journal of Neuroscience Methods

View full text Add to dashboard Cite

“…Additionally, in an intracerebral study (Le Van Quyen et al, 2005) we provided evidence for the use of phase synchrony to anticipate epileptic seizures. Moreover, recent findings in the field of Brain-Computer Interfaces suggest that combining spectral power measurements with phase synchrony improves accuracy when classifying EEG signals (Gysels et al, 2004(Gysels et al, ,2005Wei et al, 2007).…”

Section: Local Activity Measurementsmentioning

confidence: 99%

Classification methods for ongoing EEG and MEG signals

et al. 2007

View full text Add to dashboard Cite

Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97% classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states.

show abstract

“…Traditionally Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, it has been suggested that a measure of Phase Locking Value (PLV) might be better for this purpose as it is computationally faster and requires fewer data for a given time resolution (Gysels and Celka, 2004; Gysels et al, 2005). …”

Section: Introductionmentioning

confidence: 99%

Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain–computer interface

Krusienski

McFarland

Wolpaw

2012

Brain Research Bulletin

View full text Add to dashboard Cite

Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined Phase-Locking Value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of interchannel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.

show abstract

SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in Brain–Computer Interfaces

Cited by 64 publications

References 29 publications

Brain–computer interfaces (BCIs): Detection instead of classification

Brain–computer interfaces (BCIs): Detection instead of classification

Classification methods for ongoing EEG and MEG signals

Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain–computer interface

Contact Info

Product

Resources

About