1,679 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
Freeze the BCI until the user is ready: a pilot study of a BCI inhibitor
In this paper we introduce the concept of Brain-Computer Interface (BCI)
inhibitor, which is meant to standby the BCI until the user is ready, in order
to improve the overall performance and usability of the system. BCI inhibitor
can be defined as a system that monitors user's state and inhibits BCI
interaction until specific requirements (e.g. brain activity pattern, user
attention level) are met. In this pilot study, a hybrid BCI is designed and
composed of a classic synchronous BCI system based on motor imagery and a BCI
inhibitor. The BCI inhibitor initiates the control period of the BCI when
requirements in terms of brain activity are reached (i.e. stability in the beta
band). Preliminary results with four participants suggest that BCI inhibitor
system can improve BCI performance.Comment: 5th International Brain-Computer Interface Workshop (2011
Using Scalp Electrical Biosignals to Control an Object by Concentration and Relaxation Tasks: Design and Evaluation
In this paper we explore the use of electrical biosignals measured on scalp
and corresponding to mental relaxation and concentration tasks in order to
control an object in a video game. To evaluate the requirements of such a
system in terms of sensors and signal processing we compare two designs. The
first one uses only one scalp electroencephalographic (EEG) electrode and the
power in the alpha frequency band. The second one uses sixteen scalp EEG
electrodes and machine learning methods. The role of muscular activity is also
evaluated using five electrodes positioned on the face and the neck. Results
show that the first design enabled 70% of the participants to successfully
control the game, whereas 100% of the participants managed to do it with the
second design based on machine learning. Subjective questionnaires confirm
these results: users globally felt to have control in both designs, with an
increased feeling of control in the second one. Offline analysis of face and
neck muscle activity shows that this activity could also be used to distinguish
between relaxation and concentration tasks. Results suggest that the
combination of muscular and brain activity could improve performance of this
kind of system. They also suggest that muscular activity has probably been
recorded by EEG electrodes.Comment: International Conference of the IEEE EMBS (2011
Régimes des libertés et droits fondamentaux : les points clés juridique, historique, politique et idéologique de chaque liberté et droit
Edition 2015-2016</p
- …