Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis

Abstract

Driving anger, called "road rage", has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG) spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving/cutting in line, jaywalking/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of β band (β%) and relative energy spectrum of θ band (θ%) for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC) curve analysis, the optimal thresholds (best cut-off points) of β% and θ% for identifying none anger state (i.e., neutral) were determined to be 0.2183 ≤ θ% < 1, 0 < β% < 0.2586; low anger state is 0.1539 ≤ θ% < 0.2183, 0.2586 ≤ β% < 0.3269; moderate anger state is 0.1216 ≤ θ% < 0.1539, 0.3269 ≤ β% < 0.3674; high anger state is 0 < θ% < 0.1216, 0.3674 ≤ β% < 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc) of the optimal thresholds of β% for discriminating the four driving anger states is 80.21%, while 75.20% for that of θ%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.

Driving Anger States Detection Based on Incremental Association Markov Blanket and Least Square Support Vector Machine

“…The greater AUC is, the higher accuracy the classifier has. Here, the AUC can be calculated by means of integration of trapezoid URST (see Figure 5) [36], namely:…”

“…(a) The test route (shown as blue line) (b) Elicitation events in real traffic environment The test route and traffic environment of field experimental system[36].…”

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

“…In the literature, there are different types of stimuli that have been utilized by the researchers to induce emotions. In [7], different genres of music were chosen as stimuli for inducing EEG-based emotions. In [46], a hypermedia system, namely MetaTutor, was utilized as stimuli for students learning about a complicated science topic.…”

“…From between the modalities that tend to be in the literature, speech is an intuitive measure for computers to comprehend human emotion, whereas EEG [7] is an internal measurement from the brain that makes an intriguing alternative for recognizing multi-modal emotion. Up until now, there are no proposals that have endeavored to consider speech and EEG simultaneously for recognizing spontaneous emotion.…”

“…Only 60 s of every song was employed to avoid inducing anxiety and boredom in a participant's brain, as depicted in Figure 2. The subjects annotated their emotional status by the end of each music track picking one of the seven available emotional keywords: Fear, surprise, happy, disgust, neutral, anxiety, and sadness [7,45]. To avoid the participant's subjectivity and exaggeration while expressing emotions, the experiment was also noticed by an expert of psychology who judged through listening to the participant's emotional speech and did the final labeling of emotions to exclude the spoofed labeled emotion.…”