1.1. The importance of drowsiness detection
Studies reported that fatigue, which, in turn, caused drivers inattention or drowsiness, was the major risk factor for serious injury and death in car accidents [1-4] National Sleep Foundation (NSF) reported that 60% of drivers had felt drowsy during driving, and 37% of the drivers had actually fallen asleep. The National Highway Traffic Safety Administration (NHTSA) also reported that at least 100,000 police-reported crashes were directly caused by drowsy driving in 2006 and leaded to 1,500 deaths, 71,000 injuries and $12.5 billion in monetary losses (National Sleep Foundation 2007 State of the States Report on Drowsy Driving). Therefore, to early detect the drivers’ drowsiness and to help to keep the drivers’ alertness for avoiding the car accidents that caused by drowsiness are important to protect living safeties of people.
Drowsiness detection changes of the subject’s alertness have been widely investigated by different measurements [5, 6] including the monitoring subject’s behavior and image based techniques and physiological signal-based system. The advantage and limitation of these methods were described in the following paragraphs.
1.2. The behavioral monitoring
Previous studies had shown that subject’s response performance is deteriorated along with the drowsiness. The response performances were
of drivers’ moving handle wheel [11, 12]. The limitation of behavioral monitory system is highly depended on driving behavior, experiences, road conditions, and all other environmental variables. Therefore, it is difficult to be generalized for regular use. However, it can be used as an auxiliary method in the image-based techniques or physiological signal based system to define or verify the subject’s alertness according to the car deviation from the cruising lane and the response time (RT) to specific driving conditions. Such methods have difficulties to apply in the real driving since it is easily affect by the sounded environment and it is still unclear to what extend the behavioral responses can fully reflect the real cognitive status. But, previous have showed that behavioral performance is opposite correlated with the driver’s alertness.
Specifically, the subject’s response performances, which index by response time, are decreased along with the increases of drivers’ drowsiness [13, 14].
1.3. The image-based technique
The image-based technique uses the video camera to record the eye gaze position, eye closure or the head position [15] to derive the duration of eye gaze fixation and the eye closure or frequency of eye movement, eye blinking [16-18] or head movement [19] for correlating the subject’s drowsiness level. The advantage of the image based detecting system is nearly no need for preparation before the experiment, which is contrast to the long preparation time in the EEG based monitoring system. However, the quality of recorded image is easily influenced by the environment [20], with which is necessary for the camera needed to interact. Furthermore, it is difficult to get enough space to mount two cameras inside the cabin and without blocking the driver’s
viewing angle and therefore reducing the driver’s visual field [21]. Second, the response time for detecting driver’s drowsiness was too long to feedback to the driver in real time [22].
1.4. The physiological signal based system
Abundance of studies used the physiological signals, including the electrocardiograph (ECG), electro-oculograph (EOG), or electroencephalograph (EEG), to monitor the subject’s alertness. The heart rate or heart rate variability [23] which derived from the ECG signals has been known easily affected by the subject’s psychological and physiological conditions and therefore the ECG signals is not a good index for monitoring the driver’s alertness. Some laboratories tried to use the EOG signals to index the driver’s alertness. For example, they found that the rate of eye blinking [24]
was declined along with the decreases of subject’s alertness. However, the time window for analyzing the EOG signals to assess the driver’s drowsiness was around 240 sec, which is too long to use in the drowsiness warning system in the real driving. Hence, the EEG signals se the limitation of long average windows to detect drowsiness. Therefore, EEG remains the most popular modality and the better methods used to monitor drowsiness state in real-time.
1.5. Drowsiness related EEG features
Studies had shown that the brain activities are changed with the subject’s drowsiness level, especially the neural activities generated from the occipital
(4-7 Hz, [27-30]) were incremented along with the decreases of subject’s performances. The similar brain dynamic changes are also observed in the simulated driving condition. Lin et al. [31] reported that the power of occipital alpha band was linearly increased from alertness to mild drowsy and then the alpha power was maintain at the same level or slightly decreased from mild drowsiness. In addition, the occipital theta power was also found increased monotonically from alert to deep drowsy. The above results suggested that occipital alpha and theta bands would be as good EEG features for indexing the subject’s drowsiness.
1.6. Effects of warning signals under drowsy condition
Many studies had tried to use the warning signals to keep driver’s attention [32-34]. They delivered the warning stimulations mainly via the acoustic [35], visual [36] or vibrated stimuli [37, 38]. Furthermore, some studies also tried to simultaneously present the warning signals via the multiple modalities [39]. Belz et al. compared the above warning modalities in terms of the reaction time (RT) to each warning modality [40]. Results showed that subject responded to the visual alarms with the longest RT since the driver needed to pay attention to the road condition and the dashboard. Therefore, the visual alarms are adequate as the warning stimulus. The multiple-warning modality significantly improved the driver’s performances by accelerating the RT. The acoustic stimuli also greatly improve the driver’s RT while the characteristics of the warning signal would significantly affect the results of the warning.
The warning sounds could be classified into two types, the conventional warning signals and the auditory icon [41, 42]. The conventional sounds were generated with specific acoustic parameters, such as pure tones, bells, buzzers and sirens. The auditory icons were sounds with specific stereotypical meanings defined by the objects or actions. For example, the horn or tire-skid, imply the emergency braking or car accident. Graham assessed these two types of sounds by measuring the driver’s RT [41]. Though results revealed that auditory icons significantly reduced the RT compared to the responses to conventional warnings, the auditory icons are also known to cause the driver to respond alarms improperly and increasing the risk of car accidents. Therefore, the auditory icons would not be safe to widely apply on real driving. Our previous studies evaluated effects of the spectrum and delivering patterns of conventional sounds on keeping the driver’s attention [43]. We delivered two types of sound patterns (continuous tone and tone bursts) and each pattern was tested by three different carrier frequencies (500, 1750, and 3000 Hz).
Results showed that tone bursts with the carrier frequencies at 1750 Hz significantly improved the driver’s performances and without side effects on driver’s driving behavior.
1.7. Aims of this study
Effects of alarms on maintaining driver’s attention and alertness were assessed in terms of the behavioral responses. To what extent the behavioral performance can reflect on the subject’s cognitive status and neural activities remains unclear. Some studies have observed that the behavioral performance might not be sufficient to fully mirror the real cognitive state
though lots of results showed that the behavioral performance was highly correlated with the brain dynamic [44, 45].
The first aim of this study was to determine the effects of the auditory alarm on the brain dynamics, which explored by the EEG. The second aim of this study was tried to elucidate whether the brain activities could fully mirror the behavioral indexes.