1,105 research outputs found
Exploring Two Novel Features for EEG-based Brain-Computer Interfaces: Multifractal Cumulants and Predictive Complexity
In this paper, we introduce two new features for the design of
electroencephalography (EEG) based Brain-Computer Interfaces (BCI): one feature
based on multifractal cumulants, and one feature based on the predictive
complexity of the EEG time series. The multifractal cumulants feature measures
the signal regularity, while the predictive complexity measures the difficulty
to predict the future of the signal based on its past, hence a degree of how
complex it is. We have conducted an evaluation of the performance of these two
novel features on EEG data corresponding to motor-imagery. We also compared
them to the most successful features used in the BCI field, namely the
Band-Power features. We evaluated these three kinds of features and their
combinations on EEG signals from 13 subjects. Results obtained show that our
novel features can lead to BCI designs with improved classification
performance, notably when using and combining the three kinds of feature
(band-power, multifractal cumulants, predictive complexity) together.Comment: Updated with more subjects. Separated out the band-power comparisons
in a companion article after reviewer feedback. Source code and companion
article are available at
http://nicolas.brodu.numerimoire.net/en/recherche/publication
Unimanual versus bimanual motor imagery classifiers for assistive and rehabilitative brain computer interfaces
Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this study we present a classifier which discriminates between uni-and bimanual MI. Ten able bodied participants took part in cue based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32 channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L-B, B-R and B--L hands were created, with features based on wide band Common Spatial Patterns (CSP) 8-30 Hz, and band specifics Common Spatial Patterns (CSPb). Event related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for L-R task (73±9% and 75±10% respectively) and for L-B task (73±11% and 70±9% respectively). However, for R-B task (67±3% and 72±6% respectively) it was significantly higher for CSPb (p=0.0351). Six participants whose L-R classification accuracy exceeded 70% were included in an on-line task a week later, using the unmodified offline CSPb classifier, achieving 69±3% and 66±3% accuracy for the L-R and R-B tasks respectively. Combined uni and bimanual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits
Single-trial analysis of EEG during rapid visual discrimination: enabling cortically-coupled computer vision
We describe our work using linear discrimination of multi-channel electroencephalography
for single-trial detection of neural signatures of visual recognition events. We demonstrate
the approach as a methodology for relating neural variability to response variability, describing
studies for response accuracy and response latency during visual target detection.
We then show how the approach can be utilized to construct a novel type of brain-computer
interface, which we term cortically-coupled computer vision. In this application, a large
database of images is triaged using the detected neural signatures. We show how ‘corticaltriaging’
improves image search over a strictly behavioral response
Is implicit motor imagery a reliable strategy for a brain computer interface?
Explicit motor imagery (eMI) is a widely used brain computer interface (BCI) paradigm, but not everybody can accomplish this task. Here we propose a BCI based on implicit motor imagery (iMI). We compared classification accuracy between eMI and iMI of hands. Fifteen able bodied people were asked to judge the laterality of hand images presented on a computer screen in a lateral or medial orientation. This judgement task is known to require mental rotation of a person’s own hands which in turn is thought to involve iMI. The subjects were also asked to perform eMI of the hands. Their electroencephalography (EEG) was recorded. Linear classifiers were designed based on common spatial patterns. For discrimination between left and right hand the classifier achieved maximum of 81 ± 8% accuracy for eMI and 83 ± 3% for iMI. These results show that iMI can be used to achieve similar classification accuracy as eMI. Additional classification was performed between iMI in medial and lateral orientations of a single hand; the classifier achieved 81 ± 7% for the left and 78 ± 7% for the right hand which indicate distinctive spatial patterns of cortical activity for iMI of a single hand in different directions. These results suggest that a special brain computer interface based on iMI may be constructed, for people who cannot perform explicit imagination, for rehabilitation of movement or for treatment of bodily spatial neglect
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