324 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
Would Motor-Imagery based BCI user training benefit from more women experimenters?
Mental Imagery based Brain-Computer Interfaces (MI-BCI) are a mean to control
digital technologies by performing MI tasks alone. Throughout MI-BCI use, human
supervision (e.g., experimenter or caregiver) plays a central role. While
providing emotional and social feedback, people present BCIs to users and
ensure smooth users' progress with BCI use. Though, very little is known about
the influence experimenters might have on the results obtained. Such influence
is to be expected as social and emotional feedback were shown to influence
MI-BCI performances. Furthermore, literature from different fields showed an
experimenter effect, and specifically of their gender, on experimental outcome.
We assessed the impact of the interaction between experi-menter and participant
gender on MI-BCI performances and progress throughout a session. Our results
revealed an interaction between participants gender, experimenter gender and
progress over runs. It seems to suggest that women experimenters may positively
influence partici-pants' progress compared to men experimenters
Assessing the Zone of Comfort in Stereoscopic Displays using EEG
The conflict between vergence (eye movement) and accommodation (crystalline
lens deformation) occurs in every stereoscopic display. It could cause
important stress outside the "zone of comfort", when stereoscopic effect is too
strong. This conflict has already been studied using questionnaires, during
viewing sessions of several minutes. The present pilot study describes an
experimental protocol which compares two different comfort conditions using
electroencephalography (EEG) over short viewing sequences. Analyses showed
significant differences both in event-related potentials (ERP) and in frequency
bands power. An uncomfortable stereoscopy correlates with a weaker negative
component and a delayed positive component in ERP. It also induces a power
decrease in the alpha band and increases in theta and beta bands. With fast
responses to stimuli, EEG is likely to enable the conception of adaptive
systems, which could tune the stereoscopic experience according to each viewer
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
TOBE: Tangible Out-of-Body Experience
We propose a toolkit for creating Tangible Out-of-Body Experiences: exposing
the inner states of users using physiological signals such as heart rate or
brain activity. Tobe can take the form of a tangible avatar displaying live
physiological readings to reflect on ourselves and others. Such a toolkit could
be used by researchers and designers to create a multitude of potential
tangible applications, including (but not limited to) educational tools about
Science Technologies Engineering and Mathematics (STEM) and cognitive science,
medical applications or entertainment and social experiences with one or
several users or Tobes involved. Through a co-design approach, we investigated
how everyday people picture their physiology and we validated the acceptability
of Tobe in a scientific museum. We also give a practical example where two
users relax together, with insights on how Tobe helped them to synchronize
their signals and share a moment
The Impact of Flow in an EEG-based Brain Computer Interface
Major issues in Brain Computer Interfaces (BCIs) include low usability and
poor user performance. This paper tackles them by ensuring the users to be in a
state of immersion, control and motivation, called state of flow. Indeed, in
various disciplines, being in the state of flow was shown to improve
performances and learning. Hence, we intended to draw BCI users in a flow state
to improve both their subjective experience and their performances. In a Motor
Imagery BCI game, we manipulated flow in two ways: 1) by adapting the task
difficulty and 2) by using background music. Results showed that the difficulty
adaptation induced a higher flow state, however music had no effect. There was
a positive correlation between subjective flow scores and offline performance,
although the flow factors had no effect (adaptation) or negative effect (music)
on online performance. Overall, favouring the flow state seems a promising
approach for enhancing users' satisfaction, although its complexity requires
more thorough investigations
The Use of Fuzzy Inference Systems for Classification in EEG-based Brain-Computer Interfaces
International audienceThis paper introduces the use of a Fuzzy Inference System (FIS) for classification in EEG-based Brain-Computer Interfaces (BCI) systems. We present our FIS algorithm and compare it, on motor imagery signals, with three other popular classifiers, widely used in the BCI community. Our results show that FIS outperformed a Linear Classifier and reached the same level of accuracy as Support Vector Machine and neural networks. Thus, FIS-based classification is suitable for BCI design. Furthermore, FIS algorithms have two additionnal advantages: they are readable and easily extensible
Les Interfaces Cerveau-Ordinateur: Conception et Utilisation en Réalité Virtuelle
International audienceBrain-Computer Interfaces (BCI) are emerging interfaces that enable their users to send commands to a computer by means of brain activity only. In this paper, we first propose a brief overview of BCI, focused on BCI principles and applications. In a second part, we present our recent contributions to BCI research. More precisely, we present 1) our contributions in brain signal processing and classification to design an efficient BCI, able to accurately identify the user's mental state and 2) our work related to the design of concrete BCI-based virtual reality applications. Finally, this paper proposes some promising perspectives for BCI, notably in the fields of assistive technologies, video games and mental state monitoring.Les interfaces cerveau-ordinateur ou BCI ("Brain-Computer Interfaces") sont une forme émergente d'interfaces permettant à un utilisateur d'envoyer des commandes à un ordinateur uniquement grâce à son activité cérébrale. Dans cet article, nous proposons tout d'abord un bref tour d'horizon des BCI s'intéressant à leur fonctionnement et à leurs applications. Dans une deuxième partie, nous présentons nos récents travaux et plus particulièrement 1) nos contributions en traitement et classification de signaux cérébraux afin de concevoir des BCI efficaces, capables de reconnaitre précisément l'état mental de l'utilisateur et 2) nos recherches visant à concevoir des applications concrètes de réalité virtuelle contrôlée à l'aide d'une BCI. Enfin, cet article propose quelques perspectives prometteuses pour les BCI notamment dans les domaines du handicap, des jeux vidéos ou encore du suivi temps réel d'état mental
A new feature and associated optimal spatial filter for EEG signal classification: Waveform Length
International audienceIn this paper, we introduce Waveform Length (WL), a new feature for ElectroEncephaloGraphy (EEG) signal classification which measures the signal complexity. We also propose the Waveformlength Optimal Spatial Filter (WOSF), an optimal spatial filter to classify EEG signals based on WL features. Evaluations on 15 subjects suggested that WOSF with WL features provide performances that are competitive with that of Common Spatial Patterns (CSP) with Band Power (BP) features, CSP being the optimal spatial filter for BP features. More interestingly, our results suggested that combining WOSF with CSP features leads to classification performances that are significantly better than that of CSP alone (80% versus 77% average accuracy respectively)
On the need for alternative feedback training approaches for BCI
International audienceWhile recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used in practice, outside laboratories. The main reason is their lack of reliability and robustness. Indeed, with current BCI, mental state recognition is usually slow and too often incorrect. These poor performances are due in part to the EEG signal processing algorithms used since they are not yet able to deal appropriately with their noisy, complex and non-stationary nature. However, there is another component of the BCI loop that may also be deficient: the user him/herself who may not be able to produce reliable EEG patterns. Indeed, it is widely acknowledged that "BCI use is a skill" [1], which means the user must be properly trained to be able to successfully use the BCI. If the BCI user is indeed unable to correctly perform the desired mental commands, whatever the signal processing algorithms used, there would be no way to properly identify them. Despite this, the BCI community has focused the majority of its research effort on signal processing and machine learning, mostly neglecting the human in the loop. In this work, we argue that the user is one of the most critical components of the BCI loop that may explain the limited reliability of current BCI. It does not mean that BCI users are per se poor performers or incompetent. It means that the way current BCI training protocols are designed is inappropriate, making BCI users unable to properly learn and use the BCI skill
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