Data acquisition

 A Virtual Reality (VR)-based Driving Environment

In this study we use a virtual-reality based highway-driving environment to generate the required data.  Some of our previous studies to investigate changes in drivers’ cognitive states during a long-term monotonous driving have also used the same VR-based environment [19-20].  In this system, a real car mounted on a 6-degree-of-freedom Stewart platform is used for the driving and seven projectors are used to generate 3-D surrounded scenes. During the driving experiments, all scenes move according to  the displacement of the car and the subject’s maneuvering of the wheel which make the subject feel like driving the car on a real road. In all our experiments we have kept the driving speed fixed at 100 km/hr and system automatically and randomly drifts the car away from the center of the cruising lane to mimic the effects of a non ideal road surface. The driver is asked to maintain the car along the center of the cruising lane. All subjects involved in this study have good driving skill and hence when the subject is alert, his/her response time to the random drift is short and the deviation of the car from the center of the lane is small. But, when the subject is not alert/ drowsy, both the response time and the car’s deviation are high. Note that, in all our experiments, the subject’s car is the only car cruising on the VR-based freeway. Although, both response time and the deviation from the central line are related to the subject’s driving performance, in this study, we use the car’s deviation from the central line as a measure of performance of the subjects.

 

The EEG Recording System:

The data acquisition system uses 32 sintered Ag/AgCl EEG/EOG electrodes with a unipolar reference at right earlobe and 2 ECG channels in bipolar connection which are placed on the chest. All EEG/EOG electrodes were placed following a modified International 10–20 system and refer to right ear lobe as depicted in Figure. 1. In Fig. 1, we use the following notations: F: Frontal lobe. T: Temporal lobe. C: Central lobe. P: Parietal lobe. O: Occipital lobe. "Z" refers to an electrode placed on the mid-line. In Fig. 1, A1 and A2 are two reference channels.  The two channels FP1 and FP2 are found to be quite noisy and hence we do not use the signals obtained from them. Thus we use data from 28 channels. Before the data acquisition, the contact impedance between EEG electrodes and cortex was calibrated to be less than 5 .We use the Scan NuAmps Express system (Compumedics Ltd., VIC, Australia) to simultaneously record the EEG/EOG data and the deviation between the center of the vehicle and the center of the cruising lane. The EEG data are recorded with 16-bit quantization level at the sampling rate of 500 Hz. To reduce the burden of computation, the data are then down-sampled to sampling rate of 250 Hz.  Since the objective is to develop methodologies that can be used in real time, we do not use sophisticated noise cleaning techniques such as ICA but we preprocess the EEG signals using a simple low-pass filter with a cutoff frequency of 50 Hz to remove the line noise (60 Hz and its harmonics) and other high-frequency noise.

Figure 1.                           Electrodes placement of 10–20 system. The letters used are: F: Frontal lobe. T: Temporal lobe. C: Central lobe. P: Parietal lobe. O: Occipital lobe. "Z" refers to an electrode placed on the mid-line.

The Subjects

Here we provide a brief description of the EEG recording system as well as of the subjects involved in this study.  We have used a set of thirteen subjects (ages varying from 20 to 40 years old) to generate data for the investigation. Of this thirteen, ten subjects are the same as used in [18].  Statistical reports [21] suggest that people often get drowsy within one hour of continuous driving in the early afternoon hours. Moreover, after a good sleep in the night, people are not likely to fall sleep easily during the first half of the day. And hence, we have conducted all our experiments in the early afternoon after lunch so that we can generate more useful data. We have explained the participants about the goal of these experiments and the general features of the driving task. We have also completed the necessary formalities to get their consent for these experiments. Each subject was asked to drive the car for 60-minutes with a view to keeping the car at the center of the cruising lane by maneuvering it with the steering wheel. Of the thirteen subjects, four struggled with mild drowsiness, while the remaining nine exhibited mild and deep drowsy episodes during the 1-hour driving session.

 

Indirect Measurement of Alertness

To investigate the relationship between the measured EEG signals and subject’s cognitive state, and to quantify the level of the subject’s alertness, in our previous studies [19-20], we have define an indirect index of the subject’s alertness level (driving performance) as the deviation between the center of the vehicle and the center of the cruising lane.  Typically the drowsiness level fluctuates with cycle lengths longer than 4 minutes [22-25], and hence we smooth the indirect alertness level index using a causal 90-sec moving window advancing at 2-ses steps. This helps us to eliminate variance with cycle lengths shorter than 1-2 minutes. We emphasize that this index is used only to validate our approach, and it is not as an input to develop the model for the alert state of the subject.