Detection of braking intention in diverse situations during simulated driving based on EEG feature combination

Kim, Il Hwa; Kim, Jeong Woo; Haufe, Stefan; Lee, Seong Whan

doi:10.1088/1741-2560/12/1/016001

Cited by 135 publications

(103 citation statements)

References 33 publications

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“…3,4 . Along this line, the possibility of decoding brain activity, measured with electroencephalography (EEG), to improve driving assistance systems has recently attracted considerable interest 11,13,14,29 . In this approach, the objective is not to 'control a car with a BCI' but rather to predict the driver's intentions 11,13,14,29 .…”

Section: Introductionmentioning

confidence: 99%

Real-time Detection of Driver’s Movement Intention in Response to Traffic Lights

Khaliliardali

Chavarriaga

Zhang

et al. 2019

Preprint

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Movements are preceded by certain brain states that can be captured through various neuroimaging techniques. Brain-Computer Interfaces can be designed to detect the movement intention brain state during driving, which could be beneficial in improving the interaction between a smart car and its driver, by providing assistance in-line with the driver's intention. In this paper, we present an Electroencephalogram based decoder of such brain states preceding movements performed in response to traffic lights in two experiments: in a car simulator and a real car. The results of both experiments (N=10: car simulator, N=8: real car) confirm the presence of anticipatory Slow Cortical Potentials in response to traffic lights for accelerating and braking actions. Single-trial classification performance exhibits an Area Under the Curve (AUC) of 0.71±0.14 for accelerating and 0.75±0.13 for braking. The AUC for the real car experiment are 0.63±0.07 and 0.64±0.13 for accelerating and braking respectively. Moreover, we evaluated the performance of real-time decoding of the intention to brake during online experiments only in the car simulator, yielding an average accuracy of 0.64±0.1. This paper confirm the existence of the anticipatory slow cortical potentials and the feasibility of single-trial detection these potentials in driving scenarios.

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Section: Introductionmentioning

confidence: 99%

Real-time Detection of Driver’s Movement Intention in Response to Traffic Lights

Khaliliardali

Chavarriaga

Zhang

et al. 2019

Preprint

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“…These BCI systems would decode driver's brain activity to estimate his/her cognitive states or action intentions. For instance, the system can verify whether the driver is paying attention to the driving behavior (Simon et al, 2011), estimate mental workload (Dijksterhuis et al, 2013), or predict driver's intention of action (e.g., braking, traffic lights, and lane changes) (Haufe et al, 2011;Gheorghe et al, 2013;Haufe et al, 2014;Sonnleitner et al, 2014;Kim et al, 2015;Khaliliardali et al, 2015).…”

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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“…Their laboratory results were reproduced during real-world driving (Haufe et al, 2014a), demonstrating that well-designed simulator studies can be a useful proxy for real world studies. The results have also been confirmed under more diversified traffic conditions (Kim et al, 2014; Khaliliardali et al, 2015). …”

Section: Detection Of Emergency Braking Intention During Drivingmentioning

confidence: 54%

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

et al. 2016

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The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

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Detection of braking intention in diverse situations during simulated driving based on EEG feature combination

Cited by 135 publications

References 33 publications

Real-time Detection of Driver’s Movement Intention in Response to Traffic Lights

Real-time Detection of Driver’s Movement Intention in Response to Traffic Lights

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

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

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