Interface neuronale directe

Résumé

thumb|250px|Schéma d'une interface neuronale directe. Une interface neuronale directe - abrégée IND ou BCI ou encore ICM (interface cerveau-machine, ou encore interface cerveau-ordinateur) est une interface de communication directe entre un cerveau et un dispositif externe (un ordinateur, un système électronique…). Ces systèmes peuvent être conçus dans le but d'étudier le cerveau, d'assister, améliorer ou réparer des fonctions humaines de cognition ou d'action défaillantes. L'IND peut être unidirectionnelle ou bidirectionnelle. Ce type de périphérique est fondamentalement différent de toute autre interface homme-machine : une telle liaison ne requiert en effet aucune transformation préalable du signal électrique émis par l’activité cérébrale en activité musculaire (psychomotrice), cette dernière étant usuellement traduite en signal d’entrée pour la machine. En s’affranchissant de la chaîne de réaction « cerveau, nerfs, muscles, interface conventionnelle homme-machine », les temps d

Source officielle

https://fr.wikipedia.org/wiki/Interface_neuronale_directe

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L'électroencéphalographie (EEG) est une méthode d'exploration cérébrale qui mesure l'activité électrique du cerveau par des électrodes placées sur le cuir chevelu souvent représentée sous la forme d'

Un rythme cérébral (appelé aussi activité neuro-électrique) désigne l'oscillation électromagnétique émise par le cerveau des êtres humains, mais également de tout être vivant. Le cortex frontal qui

vignette|Cerveau d'un chimpanzé. Le cerveau est le principal organe du système nerveux des animaux bilatériens. Ce terme tient du langage courant (non scientifique) et chez les chordés, comme les hum

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Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes

Anwesha Khasnobish

Tactile feedback has the capability of reducing the workload on the visual channel, during visual feedback in brain-computer interfaces (BCIs). It is requisite to analyse the brain signals corresponding to the tactile stimulations. This work is aimed at analysing the brain signals while the users are vibrotactually stimulated. The brain signals are acquired non-invasively by electroencephalography (EEG), while brushless coin-type vibration motors are actuated in particular patterns to convey the object shape information on subjects' skin surface in form of vibrations. The acquired EEG signals are pre-processed to eliminate the effect of various types of noises and to extract the EEG signals corresponding to relevant frequency bands. Adaptive autoregressive (AAR) parameters are extracted from the pre-processed EEG signals and are finally classified by Naive Bayesian (NB) approach, in order to recognize the vibratotactually stimulated object shapes from brain signals. In addition to the classifier output, subjects' verbal responses about the object shape they perceived are also noted for validation. Three successive sessions of shape recognition from vibrotactile pattern show an improvement in EEG classification accuracy from 63.75% to 74.37%, and also depicted learning of the stimulus from subjects' psychological response which is observed to increase from 75% to 95%. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increases the system efficacy.

IEEE2018

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Brain-computer interfaces (BCIs), as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists of a verification procedure directly based on the presence of error-related potentials (ErrP) in the EEG recorded right after the occurrence of an error. Two healthy volunteer subjects with little prior BCI experience participated in a real-time human-robot interaction experiment where they were asked to mentally move a cursor towards a target that can be reached within a few steps using motor imagery. These experiments confirm the previously reported presence of a new kind of ErrP. These Interaction ErrP exhibit a first sharp negative peak followed by a positive peak and a second broader negative peak (~270, ~330 and ~430 ms after the feedback, respectively). The objective of the present study was to simultaneously detect erroneous responses of the interface and classifying motor imagery at the level of single trials in a real-time system. We have achieved online an average recognition rate of correct and erroneous single trials of 84.7% and 78.8%, respectively. The off-line post-analysis showed that the BCI error rate without the integration of ErrP detection is around 30% for both subjects. However, when integrating ErrP detection, the average online error rate drops to 7%, multiplying the bit rate by more than 3. These results show that it's possible to simultaneously extract in real-time useful information for mental control to operate a brain-actuated device as well as correlates of cognitive states such as error-related potentials to improve the quality of the brain-computer interaction.

2008

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You Are Wrong!---Automatic Detection of Interaction Errors from Brain Waves

Pierre Ferrez

Brain-computer interfaces, as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. In this paper we exploit a unique feature of the ``brain channel'', namely that it carries information about cognitive states that are crucial for a purposeful interaction. One of these states is the awareness of erroneous responses. Different physiological studies have shown the presence of error-related potentials (ErrP) in the EEG recorded right after people get aware they have made an error. However, for human-computer interaction, the central question is whether ErrP are also elicited when the error is made by the interface during the recognition of the subject's intent and no longer by errors of the subject himself. In this paper we report experimental results with three volunteer subjects during a simple human-robot interaction (i.e., bringing the robot to either the left or right side of a room) that seem to reveal a new kind of ErrP, which is satisfactorily recognized in single trials. These recognition rates significantly improve the performance of the brain interface.

2005

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