用于识别紧急制动意图的同步混合脑机接口

Brain-X Pub Date : 2024-03-21 DOI:10.1002/brx2.56
Jiawei Ju, Aberham Genetu Feleke, Hongqi Li, Haiyang Li
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引用次数: 0

摘要

混合神经生理信号,例如脑电图(EEG)和肌电图(EMG)的组合,可用于提前获取驾驶员的意图并相应地应用适当的辅助控制,从而减少道路交通事故。然而,在从正常驾驶和软制动中检测紧急制动的情况下,这两种方法是否可以结合使用并取得更好的效果,还没有进行过探讨。本研究采用了一个特征级(混合 BCI-FL)和三个分类器级(混合 BCIs-CLs)的混合策略、谱带和谱点特征来构建识别模型。我们进行了离线和伪离线实验。使用光谱点特征的识别效果优于使用光谱带特征的识别效果。在所有实验中,所提出的两种混合 BCI 策略都能达到接近或高于 95% 的检测准确率,而检测提前时间小于 300 毫秒。其中,对于所开发的混合BCI识别模型,使用频谱点特征的混合BCI-FL和混合BCI-CL2识别模型的系统准确率分别比目前较好的基于单一肌电图的识别模型高出4.25%(p <0.015)和4.69%(p <0.006)。这项研究推动了脑电图和肌电信号混合信号在智能驾驶辅助系统中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Synchronous hybrid brain–computer interfaces for recognizing emergency braking intention

Synchronous hybrid brain–computer interfaces for recognizing emergency braking intention

Hybrid neurophysiological signals, such as the combination of electroencephalography (EEG) and electromyography (EMG), can be used to reduce road traffic accidents by obtaining the driver's intentions in advance and accordingly applying appropriate auxiliary controls. However, whether they can be used in combination and can achieve better results in situations of detecting emergency braking from normal driving and soft braking has not been explored. This study used one feature-level (hybrid BCI-FL) and three classifier-level (hybrid BCIs-CLs) hybrid strategies, the spectral band, and spectral point features to construct recognition models. Offline and pseudo-online experiments were conducted. The recognition performance with the spectral point features showed a better result than that with spectral band features. In all experiments, the two proposed hybrid BCI strategies could achieve a detection accuracy close to or above 95%, while the detection advanced time is less than 300 ms. In particular, for the developed hybrid BCI recognition models, the hybrid BCI-FL and hybrid BCI-CL2 recognition models with spectral point features achieved 4.25% (p < 0.015) and 4.69% (p < 0.006) higher system accuracies, respectively, than that of the current better single EMG-based recognition model. This research promotes the application of hybrid EEG and EMG signals in intelligent driving assistance systems.

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