A New Generation of Brain-Computer Interface Based on Riemannian Geometry

Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.Comment: 33 pages, 9 Figures, 17 equations/algorithm

Brain Invaders : Données EEG d'une Expérience avec une Interface Cerveau-Ordinateur Adaptive et Non-Adaptive.

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.1494163 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 24 subjects doing a visual P300 Brain-Computer Interface experiment on PC. The visual P300 is an event-related potential elicited by visual stimulation, peaking 240-600 ms after stimulus onset. The experiment was designed in order to compare the use of a P300-based brain-computer interface on a PC with and without adaptive calibration using Riemannian geometry. The brain-computer interface is based on electroencephalography (EEG). EEG data were recorded thanks to 16 electrodes. Data were recorded during an experiment taking place in the GIPSA-lab, Grenoble, France, in 2013 (Congedo, 2013). Python code for manipulating the data is available at https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. The ID of this dataset is BI.EEG.2013-GIPSA.Dans ce document, nous décrivons une expérimentation dont les données ont été publiées sur https://doi.org/10.5281/zenodo.1494163 aux formats mat et csv. Ce jeu de donnée contient les enregistrements électroencéphalographiques (EEG) de 24 sujets durant une expérience sur les interfaces cerveau-ordinateur de type ‘P300 visuel’. Le P300 visuel est une perturbation du signal EEG apparaissant 240-600 ms après le début d'une stimulation visuelle. Le but de cette expérience était de comparer l'utilisation d'une interface cerveau-machine (ICM) basée sur le P300, sous PC, avec et sans calibration adaptive en utilisant la géométrie Riemannienne. L'EEG de chaque sujet a été enregistré grâce à 16 électrodes réparties sur la surface du scalp. L'expérience a été menée au GIPSA-lab (Université de Grenoble-Alpes, CNRS, Grenoble-INP) en 2013 (Congedo, 2013). Nous fournissons également une implémentation python pour manipuler les données à https://github.com/plcrodrigues/py.BI.EEG.2013-GIPSA. L’identifiant de cette base de données est BI.EEG.2013-GIPSA

Operationalization of Conceptual Imagery for BCIs

International audienceWe present a Brain Computer Interface (BCI) system in an asynchronous setting that allows classifying objects in their semantic categories (e.g. a hammer is a tool). For training, we use visual cues that are representative of the concepts (e.g. a hammer image for the concept of hammer). We evaluate the system in an offline synchronous setting and in an online asynchronous setting. We consider two scenarios: the first one, where concepts are in close semantic families (10 subjects) and the second where concepts are from distinctly different categories (10 subjects). We find that both have classification accuracies of 70% and above, although more distant conceptual categories lead to 5% more in classification accuracy

EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291

Recreational Applications of OpenViBE: Brain Invaders and Use-the-Force

International audienceThis chapter aims at providing the reader with two examples of open-source BCI-games that work with the OpenViBE platform. These two games are “Brain Invaders” and “Use-The-Force!” and are representative examples of two types of BCI: ERP-based BCI and oscillatory activity-based BCI. This chapter presents the principle, design and evaluation of these games, as well as how they are implemented in practice within OpenViBE. This aims at providing the interested readers with a practical basis to design their own BCI-based games

True zero-training brain-computer interfacing: an online study

Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model

Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%

A Separability Marker Based on High-Dimensional Statistics for Classification Confidence Assessment

International audienceThis work provides a theoretical analysis framework for features that belong to the high dimensional Riemannian manifold of symmetric positive definite matrices. In non-invasive EEG-based Brain Computer Interfaces, such as the P300 speller, these are sample covariance matrices of the epoched EEG signal that are classified into two classes. An analysis of the class shape on the manifold is performed, and the separability level of the two classes is evaluated. The main contribution is the Separability Marker (SM)-confidence method, a method that appends a confidence marker to the prediction of a binary classifier whose decision function is based on the comparison of Riemannian distances