Performance estimation of a cooperative brain-computer interface based on the detection of steady-state visual evoked potentials

International audienceTo be better suited for the expectations of healthy people, new brain-computer interface (BCI) paradigms should be proposed. To tackle this problem, we investigate the emerg- ing field of cooperative BCIs, which involves several users in a single BCI system. Because combining trials over time improves performance, combining trials across subjects can significantly improve performance compared to a single user. However, cooperative BCIs can only be used in particular settings, and new paradigms must be proposed to efficiently use this approach. To show the advantages of a cooperative BCI based on steady-state visual evoked potentials (SSVEP), we evaluate and compare the performance of combining deci- sions across subjects, and over time. By considering a reliable brain response such as SSVEP responses, this study repre- sents a first evolution for the combination, and the choice of the combination method for creating a cooperative SSVEP based BCI. The results suggest that six people would be enough to obtain a perfect accuracy within one second of EEG signal

Modified k-mean clustering method of HMM states for initialization of Baum-Welch training algorithm

International audienceHidden Markov models are widely used for recognition algorithms (speech, writing, gesture, ...). In this paper, a classical set of models is considered: state space of hid- den variable is discrete and observation probabilities are modeled as Gaussian distributions. The models parame- ters are generally estimated with training sequences and the Baum-Welch algorithm, i.e. an expectation maxi- mization algorithm. However this kind of algorithm is well known to be sensitive to its initialization point. The problem of this initialization point choice is addressed in this paper: a model with a very large number of states which describe training sequences with accuracy is first constructed. The number of states is then reduced using a k-mean algorithm on the state. This algorithm is com- pared to other methods based on a k-mean algorithm on the data with numerical simulations

Beyond 2D for brain-computer interfaces: two 3D extensions of the the P300-speller

International audienceThis paper, investigates the use of a 3D setting for Brain- Computer Interface (BCI) by implementing the 3D interface for the P300-Speller device. The 3D configurations were im- plemented using two different approaches which are called Natural 3D and Parallel 2D. The theoretical analyses con- cerning these two approaches are provided considering the modifications in speed, accuracy, and capacity. The experi- mental results on subjects who tested the 3D interfaces are then provided to validate the theoretical analyses

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

An Application of Gaussian Processes on Ocular Artifact Removal from EEG

International audienceConsequences of eye movements are one of the main inferences that distort the brain EEG recordings. In this paper, a multi-modal approach is used to estimate the ocular artifacts in the EEG: both vertical and horizontal eye movement signals recoded by an eye tracker are used as a reference to denoise the EEG. A Gaussian process, i.e. a second order statistics method, is assumed to model the link between the eye tracker signals and the EEG signals. The proposed method is thus a non-linear extension of the well-known adaptive filtering and can be applied with a single EEG signal contrary to independent component analysis (ICA) which is extensively used. The results show the applicability and the efficiency of this model on the ocular artifact removal

Estimation of overlapped Eye Fixation Related Potentials: The General Linear Model, a more flexible framework than the ADJAR algorithm

The Eye Fixation Related Potential (EFRP) estimation is the average of EEG signals across epochs at ocular fixation onset. Its main limitation is the overlapping issue. Inter Fixation Intervals (IFI) - typically around 300 ms in the case of unrestricted eye movement- depend on participants’ oculomotor patterns, and can be shorter than the latency of the components of the evoked potential. If the duration of an epoch is longer than the IFI value, more than one fixation can occur, and some overlapping between adjacent neural responses ensues. The classical average does not take into account either the presence of several fixations during an epoch or overlapping. The Adjacent Response algorithm (ADJAR), which is popular for event-related potential estimation, was compared to the General Linear Model (GLM) on a real dataset from a conjoint EEG and eye-tracking experiment to address the overlapping issue. The results showed that the ADJAR algorithm was based on assumptions that were too restrictive for EFRP estimation. The General Linear Model appeared to be more robust and efficient. Different configurations of this model were compared to estimate the potential elicited at image onset, as well as EFRP at the beginning of exploration. These configurations took into account the overlap between the event-related potential at stimulus presentation and the following EFRP, and the distinction between the potential elicited by the first fixation onset and subsequent ones. The choice of the General Linear Model configuration was a tradeoff between assumptions about expected behavior and the quality of the EFRP estimation: the number of different potentials estimated by a given model must be controlled to avoid erroneous estimations with large variances

On the Suppression of Noise from a Fast Moving Acoustic Source using Multimodality

International audienceThe problem of cancelling the noise from a moving acoustic source in outdoor environment is investigated in this paper. By making use of the known instantaneous location of the moving source (provided by a second modality), we propose a time-domain method for removing the noise from a moving source in a mixture of acoustic sources. The proposed method consists in resampling the mixed data recorded at a reference sensor, and by linearly combining the resampled data and the non-resampled data of the others sensor to cancel the undesired source. Simulation on synthetic data show the effectiveness and the usefulness of the proposed method

xDAWN algorithm to enhance evoked potentials: application to brain-computer interface.

International audienceA brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier , show that the proposed method is efficient and accurate

Blind Source Separation Approaches to Remove Imaging Artefacts in EEG Signals Recorded Simultaneously with fMRI

International audienceUsing jointly functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) is a growing field in human brain mapping. However, EEG signals are contaminated during acquisition by imaging artefacts which are stronger by several orders of magnitude than the brain activity. In this paper, we propose three methods to remove the imaging artefacts based on the temporal and/or the spatial structures of these specific artefacts. Moreover, we propose a new objective criterion to measure the performance of the proposed algorithm on real data. Finally, we show the efficiency of the proposed methods applied to a real dataset