arXiv subjects

Fabien Lotte

Publications and source records attributed to Fabien Lotte.

23 records · Page 2Linked to original sources

arXiv2014

Teegi: Tangible EEG Interface

We introduce Teegi, a Tangible ElectroEncephaloGraphy (EEG) Interface that enables novice users to get to know more about something as complex as brain signals, in an easy, en- gaging and informative way. To this end, we have designed a new system based on a unique combination of spatial aug- mented reality, tangible interaction and real-time neurotech- nologies. With Teegi, a user can visualize and analyze his or her own brain activity in real-time, on a tangible character that can be easily manipulated, and with which it is possible to interact. An exploration study has shown that interacting with Teegi seems to be easy, motivating, reliable and infor- mative. Overall, this suggests that Teegi is a promising and relevant training and mediation tool for the general public.

cs.HC↗

arXiv2014

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.

cs.HC↗

arXiv2013

Review of the Use of Electroencephalography as an Evaluation Method for Human-Computer Interaction

Evaluating human-computer interaction is essential as a broadening population uses machines, sometimes in sensitive contexts. However, traditional evaluation methods may fail to combine real-time measures, an "objective" approach and data contextualization. In this review we look at how adding neuroimaging techniques can respond to such needs. We focus on electroencephalography (EEG), as it could be handled effectively during a dedicated evaluation phase. We identify workload, attention, vigilance, fatigue, error recognition, emotions, engagement, flow and immersion as being recognizable by EEG. We find that workload, attention and emotions assessments would benefit the most from EEG. Moreover, we advocate to study further error recognition through neuroimaging to enhance usability and increase user experience.

cs.HC↗

arXiv2011

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.

cs.OH↗

arXiv2010

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.

q-bio.NC↗