Focusing on human factors while designing a BMI room

International audienceThe research in Brain Machine Interfaces (BMIs), although in rapid expansion, must still be considered at the experimental level since no widely available BMI system exists for helping people with motor disabilities in everyday life. Transferring BMI applications from laboratories to dedicated clinical services - and later to patient homes - implies, first of all, the specification of perfectly adapted experimental conditions including all the human factors. Our paper surveys various criteria that must be taken into account while designing a room dedicated to BMI experimentation from the ergonomic point of view, as well as adapted experimental protocols. This related work emphasizes the need and the complexity of a global and multidisciplinary approach which places human factors at the centre of the concerns

Hybrid BCI Coupling EEG and EMG for Severe Motor Disabilities

AbstractIn this paper, we are studying hybrid Brain-Computer Interfaces (BCI) coupling joystick data, electroencephalogram (EEG – electrical activity of the brain) and electromyogram (EMG – electrical activity of muscles) activities for severe motor disabilities. We are focusing our study on muscular activity as a control modality to interact with an application. We present our data processing and classification technique to detect right and left hand movements. EMG modality is well adapted for DMD patients, because less strength is needed to detect movements in contrast to conventional interfaces like joysticks. Across virtual reality tools, we believe that users will be more able to understand how to interact with such kind of interactive systems. This first part of our study report some very good results concerning the detection of hand movements, according to muscular channel, on healthy subjects

L'analyse de donnees multidimensionnlles par transformations morphologiques binaires

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 81278 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Fuzzy Mode Enhancement and Detection for Color Image Segmentation

This work lies within the scope of color image segmentation by pixel classification. The classes of pixels are constructed by detecting the modes of the spatial-color compactness function, which characterizes the image by taking into account both the distribution of colors in the color space and their spatial location in the image plane. A fuzzy transformation of this function is performed, based on fuzzy morphological operators specifically designed for mode detection. Experimental segmentation results, using several synthetic and benchmark images, show the interest of the proposed method

Color image segmentation by compacigram analysis

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The BCI group in LAGIS at Lille university

id:166Since 2005, when BCI research activities started in our lab -- dealing with classification methods for the P300 Speller [1] -- we have mostly focused our attention on the development of palliative communication interfaces. Like in many labs our BCIs are based on EEG signal analysis, as much from the experimental point of view as from the scientific one. First, we will present the experimental goals of our group in terms of palliation of motor handicap. Then, we will present our scientific contribution to the development of BCI interfaces. These studies have been started thanks to several partners involved in different projects that we will finally clarify

Color image segmentation by fuzzy morphological transformation of the 3d color histogram

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Color image segmentation by analysis of 3D histogram with fuzzy morphological filters

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Les interfaces Cerveau-Machine pour la palliation du handicap moteur sévère

National audienceLes interfaces cerveau-machine (BMI: Brain-Machine Interface) sont des systèmes de communication directe entre un individu et une machine ne reposant pas sur les canaux de communication standard que sont nos nerfs périphériques et nos muscles. Dans une BMI, l'activité cérébrale de l'utilisateur est enregistrée, analysée et traduite en commandes destinées à la machine. Nous présentons quelques caractéristiques de l'activité cérébrale qui peuvent être exploitées comme source d'information dans une BMI. Ensuite, nous décrivons les principales approches de traitement et de classification des signaux mises en oeuvre dans les BMIs. Nous présentons enfin un état de l'art des différentes interfaces BMI développées jusqu'alors, en nous attachant plus particulièrement à celles dédiées à l'aide aux personnes atteintes d'un handicap moteur sévère dans leur tâche de communication ou de contrôle de machines