Sainte-Foy-lès-Lyon, France
Sainte-Foy-lès-Lyon, France

Time filter

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Rivet B.,CNRS GIPSA Laboratory | Cecotti H.,CNRS GIPSA Laboratory | Maby E.,French Institute of Health and Medical Research | Maby E.,Institute Federatif des Neurosciences | And 4 more authors.
Brain Topography | Year: 2012

A challenge in designing a Brain-Computer Interface (BCI) is the choice of the channels, e.g. the most relevant sensors. Although a setup with many sensors can be more efficient for the detection of Event-RelatedPotential (ERP) like the P300, it is relevant to consider only a low number of sensors for a commercial or clinical BCI application. Indeed, a reduced number of sensors can naturally increase the user comfort by reducing the time required for the installation of the EEG (electroencephalogram) cap and can decrease the price of the device. In this study, the influence of spatial filtering during the process of sensor selection is addressed. Two of them maximize the Signal to Signal-plus-Noise Ratio (SSNR) for the different sensor subsets while the third one maximizes the differences between the averaged P300 waveform and the non P300 waveform. We show that the locations of the most relevant sensors subsets for the detection of the P300 are highly dependent on the use of spatial filtering. Applied on data from 20 healthy subjects, this study proves that subsets obtained where sensors are suppressed in relation to their individual SSNR are less efficient than when sensors are suppressed in relation to their contribution once the different selected sensors are combined for enhancing the signal. In other words, it highlights the difference between estimating the P300 projection on the scalp and evaluating the more efficient sensor subsets for a P300-BCI. Finally, this study explores the issue of channel commonality across subjects. The results support the conclusion that spatial filters during the sensor selection procedure allow selecting better sensors for a visual P300 Brain-Computer Interface.© 2011 Springer Science+Business Media, LLC.


Cecotti H.,CNRS GIPSA Laboratory | Phlypo R.,CNRS GIPSA Laboratory | Rivet B.,CNRS GIPSA Laboratory | Congedo M.,CNRS GIPSA Laboratory | And 6 more authors.
2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010 | Year: 2010

A Brain-Computer Interface (BCI) allows the direct communication between humans and computers by analyzing brain activity. The oddball paradigm allows detecting event-related potentials (ERPs), like the P300 wave, on targets selected by the user. While this paradigm provides the location of the P300 wave in the signal, its exact location remains a hypothesis and depends on the subject. This paper deals with the choice of the time segment for the signal analysis and its impact on the classification. A method for selecting the relevant part of the signal that contains the P300 wave is proposed. First, spatial filters are estimated for enhancing the signal. Second, a part of the enhanced P300 wave is selected based on its magnitude. This selection aims at providing an optimal start for the time window representing the P300 wave. Three window lengths are compared. We show that a window length of 500ms provides on average the best results, but the optimal window length should be set individually. The proposed technique has been validated on data recorded on 20 healthy subjects. ©2010 IEEE.


Cecotti H.,CNRS GIPSA Laboratory | Rivet B.,CNRS GIPSA Laboratory | Congedo M.,CNRS GIPSA Laboratory | Jutten C.,CNRS GIPSA Laboratory | And 9 more authors.
Traitement du Signal | Year: 2010

A Brain-Computer Interface (BCI) is a new type of human-machine interface that allows direct communication between user and machine by decoding brain activity. The ERPs as the P300 can be obtained through the odd ball paradigm, where targets are selected by the user. A new method for reducing the number of sensors that record electroencephalography (EEG) signals is proposed. Reducing the number of sensors allows reducing the time required for the installation of sensors and therefore increases user's comfort. The proposed approach is based on a recursive elimination where the cost function is based on the signal to signal plus noise ratio (SSNR), after spatial filtering. We show that this cost function is more robust and less costly in computing time than other functions based on evaluation of the detection of P300 or targets, thus avoiding a step of classification. We also propose a decision function to better categorize the importance of a sensor based on the number of desired sensors. The proposed approach is tested and validated on 20 subjects over several sessions. © 2010 Lavoisier, Paris.


Rivet B.,CNRS GIPSA Laboratory | Cecotti H.,CNRS GIPSA Laboratory | Souloumiac A.,CEA Saclay Nuclear Research Center | Maby E.,French Institute of Health and Medical Research | And 5 more authors.
European Signal Processing Conference | Year: 2011

A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by a direct control from the decoding of brain activity. To improve the ergonomics and to minimize the cost of such a BCI, reducing the number of electrodes is mandatory. A theoretical analysis of the subjacent model induced by the BCI paradigm leads to derive a closed form theoretical expression of the spatial filters which maximize the signal to signal-plus-noise ratio. Moreover, this new formulation is useful to improve a previously introduced method to automatically select relevant sensors. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 8 sensors, the loss in classification accuracy is less than 2%. Furethermore, the computational time required to rank the 32 sensors is reduced by a 4.6 speed up factor allowing dynamical monitoring of sensor relevance as a marker of the user's mental state. © EURASIP, 2011.


Cecotti H.,CNRS GIPSA Laboratory | Rivet B.,CNRS GIPSA Laboratory | Congedo M.,CNRS GIPSA Laboratory | Jutten C.,CNRS GIPSA Laboratory | And 9 more authors.
Journal of Neural Engineering | Year: 2011

A brain-computer interface (BCI) is a specific type of human-computer interface that enables direct communication between human and computer through decoding of brain activity. As such, event-related potentials like the P300 can be obtained with an oddball paradigm whose targets are selected by the user. This paper deals with methods to reduce the needed set of EEG sensors in the P300 speller application. A reduced number of sensors yields more comfort for the user, decreases installation time duration, may substantially reduce the financial cost of the BCI setup and may reduce the power consumption for wireless EEG caps. Our new approach to select relevant sensors is based on backward elimination using a cost function based on the signal to signal-plus-noise ratio, after some spatial filtering. We show that this cost function selects sensors' subsets that provide a better accuracy in the speller recognition rate during the test sessions than selected subsets based on classification accuracy. We validate our selection strategy on data from 20 healthy subjects. © 2011 IOP Publishing Ltd.


Henaff M.-A.,French Institute of Health and Medical Research | Henaff M.-A.,Institute Federatif des Neurosciences | Henaff M.-A.,University of Lyon | Bayle D.,French Institute of Health and Medical Research | And 8 more authors.
Clinical Neurophysiology | Year: 2010

Objective: The aim of this study was to disclose the dynamics of the frontal processes involved in a task shifting between two attentional states. Methods: Magnetoencephalographic activities were recorded during a Wisconsin Card Sorting Test where subjects had to match card stimuli according to one of three possible dimensions ("maintained condition"). The matching dimension was intermittently changed and subjects, after feedback presentation, had to identify the new correct dimension ("shifted condition"). Results: Source activations following the feedback to the subject's response in these two attentional conditions did not differ before 350 ms post feedback. After 350 ms, in the shifted condition, a lateral/posterior frontal activation was maintained later, while a medial/anterior frontal activation appeared up to 450 ms. Conclusions: The dynamics of activities corresponding to the two conditions disclose a spread of activation from posterior lateral frontal in the "maintained condition" to anterior medial frontal in the "shifted condition". Significance: These results are consistent with fMRI results concerning the major involvement of medial frontal cortex in tasks involving reasoning and choice making. © 2009 International Federation of Clinical Neurophysiology.

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