Discriminant brain connectivity patterns of performance monitoring at average and single-trial levels

Zhang, Huaijian; Chavarriaga, Ricardo; Millán, José del R.

doi:10.1016/j.neuroimage.2015.07.012

Cited by 20 publications

(11 citation statements)

References 62 publications

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“…A number of previous reports demonstrated modulation of beta-band activity in the prefrontal areas during action planning (Siegel et al, 2011), implementation of cognitive control (Zhang et al, 2015), working memory load (Babiloni et al, 2004) and feedback processing (Cohen et al, 2007; Marco-Pallares et al, 2008; van de Vijver et al, 2011; Cunillera et al, 2012). Using combined EEG-fMRI analysis, Mas-Herrero et al (2015) revealed that beta oscillations reflect involvement of frontal, striatal and hippocampal structures related to memory during reward processing.…”

Section: Discussionmentioning

confidence: 95%

Slow and Fast Responses: Two Mechanisms of Trial Outcome Processing Revealed by EEG Oscillations

Novikov

Nurislamova

Zhozhikashvili

et al. 2017

Front. Hum. Neurosci.

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Cognitive control includes maintenance of task-specific processes related to attention, and non-specific regulation of motor threshold. Depending upon the nature of the behavioral tasks, these mechanisms may predispose to different kinds of errors, with either increased or decreased response time (RT) of erroneous responses relative to correct responses. Specifically, slow responses are related to attentional lapses and decision uncertainty, these conditions tending to delay RTs of both erroneous and correct responses. Here we studied if RT may be a valid approximation distinguishing trials with high and low levels of sustained attention and decision uncertainty. We analyzed response-related and feedback-related modulations in theta, alpha and beta band activity in the auditory version of the two-choice condensation task, which is highly demanding for sustained attention while involves no inhibition of prepotent responses. Depending upon response speed and accuracy, trials were divided into slow correct, slow erroneous, fast correct and fast erroneous. We found that error-related frontal midline theta (FMT) was present only on fast erroneous trials. The feedback-related FMT was equally strong on slow erroneous and fast erroneous trials. Late post-response posterior alpha suppression was stronger on erroneous slow trials. Feedback-related frontal beta was present only on slow correct trials. The data obtained cumulatively suggests that RT allows distinguishing the two types of trials, with fast trials related to higher levels of attention and low uncertainty, and slow trials related to lower levels of attention and higher uncertainty.

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Section: Discussionmentioning

confidence: 95%

Slow and Fast Responses: Two Mechanisms of Trial Outcome Processing Revealed by EEG Oscillations

Novikov

Nurislamova

Zhozhikashvili

et al. 2017

Front. Hum. Neurosci.

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“…Additionally, the current study only uses the discriminative information from ERP patterns, or temporal waveforms, as classification features. Alternative features, e.g., spectral information and causal influences between electrodes, are likely to boost classification performance according to some reported studies on BCI (Wang et al, 2006;Billinger et al, 2013;Omedes et al, 2014;Zhang et al, 2015). Moreover, the online results in the car simulator data indicate that the prompt updating of the classifier seems to contribute to performance improvement, which might be further evaluated through the adaption of the classifier parameters, e.g., weights in an LDA classifier (Hsu, 2011).…”

Section: Discussionmentioning

confidence: 95%

“…This ERP pattern has been used in BCI for detecting error activity while human subjects either control moving objects (Parra et al, 2003;Ferrez and Millán, 2008) or monitor an external system (Chavarriaga and Millán, 2010;Iturrate et al, 2015). This information can then be used to correct user's erroneous decision (Parra et al, 2003), improve the information transfer rate of BCI system (Ferrez and Millán, 2008), or detect subject's intentional preferred target (Chavarriaga and Millán, 2010;Zhang et al, 2015;Iturrate et al, 2015). See Chavarriaga et al (2014) for a review.…”

Section: Introductionmentioning

confidence: 99%

EEG-based decoding of error-related brain activity in a real-world driving task

et al. 2015

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Abstract.Objectives. Recent studies have started to explore the implementation of braincomputer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver's intended turning direction before reaching road intersections. Approach. We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject's intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyze ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests.Results. An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver's intention coincides with the advice provided by the driving assistant in a real car. Significance. The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver's error-related brain activity. Given the encouraging results, the paradigm could be further improved by use of more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.

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“…models robust to temporal variability of neural correlates, increased noise, interaction of neural processes. Some promising approaches include the use of novel features based on the connectivity across brain areas [6], [7], [15], [53]- [55] or the covariance across channels [56], deep learning [10], [57], as well as techniques for robust decoder training using limited samples such as transfer learning or semi-supervised approaches [9], [40], [58]- [61]. A recent review on current trends for EEG decoding in BMI applications can be found in reference [62].…”

Section: Discussionmentioning

confidence: 99%

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Chavarriaga

Uscumlic

Zhang

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

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Modern cars can support their drivers by assessing and performing autonomously different driving maneuvers, based on information gathered by in-car sensors. We propose that brain machine interfaces (BMIs) can provide complementary information that can ease the interaction with intelligent cars in order to enhance the driving experience. In our approach, the human remains in control, while a BMI is used to monitor the driver's cognitive state and use that information to modulate the assistance provided by the intelligent car. In this review, we gather our proof-of-concept studies demonstrating the feasibility of decoding electroencephalography (EEG) correlates of upcoming actions and those reflecting whether the decisions of driving assistant systems are in-line with the driver intentions. Experimental results while driving both simulated and real cars consistently showed neural signatures of anticipation, movement preparation and error processing. Remarkably, despite the increased noise inherent to real scenarios, these signals can be decoded on a single-trial basis, reflecting some of the cognitive processes that take place while driving. However, moderate decoding performance compared to the controlled experimental BMI paradigms indicate there exists room for improvement of the machine learning methods typically used in the state-ofthe-art BMIs. We foresee that fusion of neural correlates with information extracted from other physiological measures; e.g. eye movements or electromyography (EMG) as well as contextual information gathered by in-car sensors will allow intelligent cars to provide timely and tailored assistance only if it is required; thus keeping the user in the loop and allowing him to fully enjoy the driving experience.

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Discriminant brain connectivity patterns of performance monitoring at average and single-trial levels

Cited by 20 publications

References 62 publications

Slow and Fast Responses: Two Mechanisms of Trial Outcome Processing Revealed by EEG Oscillations

Slow and Fast Responses: Two Mechanisms of Trial Outcome Processing Revealed by EEG Oscillations

EEG-based decoding of error-related brain activity in a real-world driving task

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

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