Event-related potential

Publications associées (79)

Quantifying the time for accurate EEG decoding of single value-based decisions

Ricardo Andres Chavarriaga Lozano, Athina Tzovara

Background Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. New method Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects’ decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. Results We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ~500ms for ‘Easy’ vs. ~700ms for ‘Hard’ decisions, well before subjects’ response (~340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. Comparison with Existing Method(s) We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. Conclusions We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

Elsevier2015

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Contextual modulation predicted by the N1 component of event-related potentials

Vitaly Chicherov, Michael Herzog, Gijs Plomp

Performance on a target can be strongly modified by context. For example, vernier offset discrimination is strongly deteriorated by neighboring flankers. Performance is worst when the flankers have the same length as the vernier. Surprisingly, performance improves for longer and shorter flankers [Malania et al., 2007, Journal of Vision, 7(2):1, 1-7]. It was proposed that interference is strongest when the vernier and the flankers group, and weaker when they ungroup. Here, we used high density EEG to investigate the time course of this contextual modulation. A vernier was flanked on both sides by ten lines which were either shorter, longer, or of the same length as the vernier. Performance was worst for equal length flankers, and best for longer flankers. The P1 amplitude monotonically increased with flanker size, reflecting the stimulus layout. The N1 amplitude was highly correlated with performance and, hence, with the strength of grouping: longer flankers elicited the highest amplitude of the N1 wave, shorter flankers medium amplitude, and the equal length flankers elicited the lowest one. Our study shows that the N1 wave is a good predictor for perceptual grouping and hence contextual modulation. These processes seem to occur after the P1 wave, i.e. after basic feature extraction.

2012

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Inferring subjective preferences on robot trajectories using EEG signals

Iason Batzianoulis, Aude Billard, Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil, Fumiaki Iwane

Cognitive information has been exploited in non-invasive Brain Computer Interface (BCI) scenarios to provide autonomous external agents with additional information. In this context, Error-related potentials (ErrPs), temporal deflections in electroencephalogram (EEG) signals when humans perceive erroneous actions, have been exploited to teach correct policies to agents. However, previous works have shared same objective criteria to evaluate actions of agents across humans. Therefore, it is yet an open question whether ErrPs are elicited when humans assess actions based on individual subjective criteria, and if such neuronal activities can be exploited in BCIs to enhance personalized human computer interactions. In this work, we evaluate whether ErrPs are elicited while humans assess actions based on individual subjective criteria. For this purpose, we analyze EEG signals while humans evaluate trajectories performed by the robot to avoid an obstacle. We show that the ErrP can be generated even while the human evaluates the action based on his/ her subjective criteria, and that such neuronal activity reveals subjective aspects when assessing the appropriateness of the action. These findings open the door to the exploitation of neural correlates for the personalized human computer interactions, and thus to building adaptable, individualized neuroprosthetic devices.

IEEE2019

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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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Online BCI with Stable Sources

Ricardo Andres Chavarriaga Lozano, Mohit Kumar Goel

In this paper, we show that the estimated intra-cranial sources using source localization on EEG signals can be used for online Brain Computer Interation (BCI) and the discriminant sources remain stable over days. Classifiers are trained on discriminant sources obtained for Error-related Potential (ErrP) based BCI on day 1 and then tested online on day 2. The results for nine subjects show that the source localization with discriminant sources can be used for online detection of ErrPs. Furthermore, the number of common sources among the discriminant sources for different days is above chance which shows that the discriminant sources remain stable over days.

2012

Chargement

Cortical Current Density vs. Surface EEG for Event-Related Potential-based Brain-Computer Interface

Ricardo Andres Chavarriaga Lozano, Mohit Kumar Goel

In this paper we use the cortical current density based inverse solution to classify Event Related Potential, in particular for error-related potential elicited during Brain-Computer Interface experiment. We selected discriminant cortical sources for comparing classification performance with respect to surface EEG. We found that the data from cortical sources achieves higher classification accuracy for most of the subjects. In addition, the inverse method exhibits consistently discriminant activity for the sources located over the anterior cingulate cortex region for different time points. This level of neurophysiological interpretation in terms of localisation of selected cortical sources is enabled with the use of inverse solution.

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Chargement

One fundamental limitation of EEG-based brain- computer interfaces is the time needed to calibrate the system prior to the detection of signals, due to the wide variety of issues affecting the EEG measurements. For event-related potentials (ERP), one of these sources of variability is the application performed: Protocols with different cognitive workloads might yield to different latencies of the ERPs. In this sense, it is still not clear the effect that these latency variations have on the single-trial classification. This work studies the differences in the latencies of error potentials across three experiments with increasing cognitive workloads. A delay-correction algorithm based on the cross-correlation of the averaged signals is presented, and tested with a single-trial classification of the signals. The results showed that latency variations exist between different protocols, and that it is feasible to re-use data from previous experiments to calibrate a classifier able to detect the signals of a new experiment, thus reducing the calibration time.

2012

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Latency Correction of Event-Related Potentials Between Different Experimental Protocols

Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil

Objective: A fundamental issue in EEG event-related potentials (ERPs) studies is the amount of data required to have an accurate ERP model. This also impacts the time required to train a classifier for a brain-computer interface (BCI). This issue is mainly due to the poor signal-to-noise ratio, and to the large fluctuations of the EEG caused by several sources of variability. One of these sources is directly related to the experimental protocol or application designed, and may affect to amplitude or latency variations. This usually prevents BCI classifiers to generalize among different experimental protocols. In this work, we analyze the effect of the amplitude and the latency variations among different experimental protocols based on the same type of ERP. Approach: We present a method to analyze and compensate for the latency variations in BCI applications. The algorithm has been tested on two widely used ERPs (P300 and observation error potentials), in three experimental protocols in each case. We report the ERP analysis and single-trial classification. Results and significance: The results obtained show (i) how the experimental protocols significantly affect the latency of the recorded potentials but not the amplitudes, and (ii) how the use of latency-corrected data can be used to generalize the BCIs, reducing this way the calibration time when facing a new experimental protocol.

Institute of Physics2014

Chargement

One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions and users. The calibration limits the BCI systems to scenarios where the outputs are very controlled, and makes these systems non-friendly and exhausting for the users. Although it has been studied how to reduce calibration time for asynchronous signals, it is still an open issue for event- related potentials. Here, we analyze the differences between users for single-trial error-related potentials, and propose the design of classifiers based on inter-subject features to either remove or minimize the calibration time. The results show that it is possible to have a classifier with a high performance from the beginning of the experiment, which is able to adapt itself without the user noticing.

2011

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This paper presents a new filter for EEG Event-Related Potentials that relies on spatio-temporal features. The results are analyzed with error-related potentials, and compared to the results obtained with Independent Component Analysis. Additionally, the obtained features are used for single-trial classification, showing that it is possible to obtain high classification accuracies with a very low number of features.

2011

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