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)

Le Neurofeedback

résumé dans les actes de la journée d'étude du projet auto-guérison, publié dans la revue HEGELNational audienc