Brain-machine coadaptation for optimal interaction: application to P300-Speller
type: Dataset
description: Brain-computer interfaces (BCI) aim at enabling the brain to directly control an artificial device. In particular, the P300-Speller could offer patients who cannot speak and neither move, to communicate again. This work consisted in improving this communication by implementing and studying a coadaptation between the brain and the machine. First, on the user side, we showed that adaptation is reflected in real-time by modulations of the electrophysiological responses to the feedbacks from the machine. Then, on the computer side, we proposed, tested and evaluated the effect on the user, of several approaches that endow the machine with adaptive behavior, namely: - - Automatic correction of errors, based on real-time recognition of feedback responses; - - Dynamic stimulation to increase spelling accuracy as well as to reduce the discomfort associated with the traditional row/column stimulation paradigm; - - Adaptive decision making for optimal stopping, depending on the attentional state of the user. Our results show the presence of feedback responses which are error specific and modulated by attention as well as user's surprise with respect to the outcome of the interaction. Besides, while the interest of automatic correction is highly subject-dependant, the new stimulation mode and the adaptive decision method proved clearly beneficial and their use had a significant positive impact on subject's motivation. In the perspective of clinical studies to assess the usefulness of ICM for communication, this work highlights and quantifies the importance of developing adaptive interfaces that are tailored to each every individual.
issued: 2026-05-09T11:21:13.648514
language: fr
modified: 2026-05-09T11:21:13.648519
contact point: Organization
  fn: CCSD
keyword: apprentissage
keyword: attention
keyword: brain-computer-interfaces
keyword: coadaptation
keyword: erreurs
keyword: errors
keyword: feedback-responses
keyword: infoeu-reposemanticsdoctoralthesis
keyword: interface-cerveau-machine
keyword: learning
keyword: motivation
keyword: p300-speller
keyword: reponses-aux-feedbacks
keyword: scconeurcognitive-scienceneuroscience
keyword: theses
landing page: https://theses.hal.science/tel-00870395

Brain-machine coadaptation for optimal interaction: application to P300-Speller
type: Distribution
format: HTML
issued: 2026-05-09T11:21:13.653132
modified: 2026-05-09T11:21:13.631953
access URL: https://theses.hal.science/tel-00870395

test_moissonnage_selune
type: Agent