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
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 kΩ .We use the Scan NuAmps Express system (Compumedics
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 . Statistical reports  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.
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.