An EEG-Based Multi-Classification Method of Braking Intentions for Driver-Vehicle Interaction

Wang, Hui‐Kang; Bi, Luzheng; Fei, Weijie; Wang, Ling

doi:10.1109/rcar47638.2019.9044151

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Some studies using traditional EEG features, such as PSD, often obtain reduced accuracy. Wang proposed a classification method based on PSD features to distinguish emergency and soft braking intentions from normal driving intentions using SVM [32]. Li's study shows that, compared with modeling using raw EEG signals, CSP + SVM can better identify user preferences [42].…”

Section: Discussionmentioning

confidence: 99%

“…At present, research on the decision intention of interactive behavior is widely used in the field of complex systems, mainly to solve the problem of intelligent system takeover in time in the case of the high possibility of human error, such as fatigue and high load, so as to ensure the safe operation of the system. Taking the intelligent assisted driving system ADAS as an example, Wang et al studied the neural characteristics under different driving intentions and provided a braking intention detection method for the driving assistance system from a neuroscience perspective [32]. In HCI, the behavioral decision intention is reflected as the user click decision situation.…”

Section: Research On Implicit Intent In Hcimentioning

confidence: 99%

“…This study found that the theta bands in the frontal, parietal, and occipital regions were most associated with preference decisions, including alpha in the frontal and temporal regions, and they also found clear and significant changes in brain waves in the frontal, temporal, and occipital regions when consumers make preference decisions. What also uses EEG frequency domain analysis is Wang et al (2019). Their team, combined with the EEG time domain and frequency domain PSD characteristics, explored the driver's emergency braking, flexible braking, and normal driving intentions and the differences between the three intentions.…”

Section: Research On Algorithm Model Constructionmentioning

confidence: 99%

“…They found that braking intention can cause a 40-60 Hz PSD enhancement significantly, and PSD emergency braking increased more, with a shorter duration. Using the SVM classifier for brake intention recognition, identification accuracy was more than 74% [32]. Slanzi (2017) studied the physiological changes when users make the click decision from the perspective of multimodal physiological signal fusion and modeled the click decision based on the physiological signals.…”

Section: Research On Algorithm Model Constructionmentioning

confidence: 99%

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Human–Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG Decoding

Miao,

Hou

2023

Applied Sciences

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In the short term, a fully autonomous level of machine intelligence cannot be achieved. Humans are still an important part of HCI systems, and intelligent systems should be able to “feel” and “predict” human intentions in order to achieve dynamic coordination between humans and machines. Intent recognition is very important to improve the accuracy and efficiency of the HCI system. However, it is far from enough to focus only on explicit intent. There is a lot of vague and hidden implicit intent in the process of human–computer interaction. Based on passive brain–computer interface (pBCI) technology, this paper proposes a method to integrate humans into HCI systems naturally, which is to establish an intent-based HCI model and automatically recognize the implicit intent according to human EEG signals. In view of the existing problems of few divisible patterns and low efficiency of implicit intent recognition, this paper finally proves that EEG can be used as the basis for judging human implicit intent through extracting multi-task intention, carrying out experiments, and constructing algorithmic models. The CSP + SVM algorithm model can effectively improve the EEG decoding performance of implicit intent in HCI, and the effectiveness of the CSP algorithm on intention feature extraction is further verified by combining 3D space visualization. The translation of implicit intent information is of significance for the study of intent-based HCI models, the development of HCI systems, and the improvement of human–machine collaboration efficiency.

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Section: Discussionmentioning

confidence: 99%

Section: Research On Implicit Intent In Hcimentioning

confidence: 99%

Section: Research On Algorithm Model Constructionmentioning

confidence: 99%

Section: Research On Algorithm Model Constructionmentioning

confidence: 99%

See 2 more Smart Citations

Human–Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG Decoding

Miao,

Hou

2023

Applied Sciences

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“…An emergency braking intention detection model was proposed using the driver's EEG signals by applying spatial-frequency features with regularized linear discriminant analysis [6]. Three support vector machine-based classifiers were used to distinguish between 3 driving situations (no braking, soft braking, and emergency braking) using the driver's EEG signals [7]. The driver's EEG signals were integrated with surrounding data to better anticipate the driver's intention to brake [8].…”

Section: Related Workmentioning

confidence: 99%