Measurement of Frontal Cortex Brain Activity Attributable to the Driving Workload and Increased Attention

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1 Copyright 2 SAE International Measurement of Frontal Cortex Brain Activity Attributable to the Driving Workload and Increased Attention Toshiyuki Shimizu, Satoru Hirose, Hideo Obara Nissan Motor Co., Ltd. Kazuki Yanagisawa, Hitoshi Tsunashima, Yoshitaka Marumo College of Industrial Technology, Nihon University Tomoki Haji, Masato Taira School of medicine, Nihon University ABSTRACT Functional magnetic resonance imaging (fmri) and functional near-infrared spectroscopy (fnirs) were used to measure subjects' cerebral blood flow in order to investigate higher-order human brain function activity associated with cognition and attention while operating a vehicle. As a first step, the effects of the fundamental driving environment on brain activity was investigated on the basis of fmri measurements, with simultaneous measurement of the region by fnirs. The experiments involved the presentation of visual stimuli by video clips and the execution of simple individual tasks corresponding to steering wheel and pedal operations. As a second step, a driving simulator was used to reproduce narrow road driving and car-following driving situations requiring cognition and attention. Drivers' mental activity under these conditions involving different levels of attention was measured by fnirs. The results showed that the level of activity in the lateral region rose as the relative difficulty of the tasks increased based on subjective evaluations. In addition, under the narrow road driving condition, greater activation in the pre region attributable to increased attention was found in a comparison with the results for ordinary driving tasks. INTRODUCTION Drivers of motor vehicles obtain visual information on the surrounding environment, recognize and judge that information suitably and then control their vehicle through steering wheel, accelerator and brake pedal operations. Human brain activity functions to control all of these processes. In situations where it is necessary to predict unexpected danger, it is thought that a driver's brain activity strengthens the cognition function by spontaneously raising the level of attention. In the course of developing driver support systems, it is important to have a clear understanding of human brain activity in such driving situations. Toward that end, drivers' brain activity must be measured. One method of measuring brain activity is functional magnetic resonance imaging (fmri), a noninvasive technique that uses a magnetic field to monitor an increase in cerebral blood flow based on a reduction in the concentration of deoxygenated hemoglobin. Because measurements must be made with a subject in a supine position, fmri is a highly restrictive method that can also impose limitations on the experimental conditions. Although fmri is not well suited to the observation of changes, its high spatial resolution allows activated areas of the brain to be identified with good accuracy. The Engineering Meetings Board has approved this paper for publication. It has successfully completed SAE s peer review process under the supervision of the session organizer. This process requires a minimum of three () reviews by industry experts. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of SAE. ISSN 4-7 Positions and opinions advanced in this paper are those of the author(s) and not necessarily those of SAE. The author is solely responsible for the content of the paper. SAE Customer Service: Tel: (inside USA and Canada) Tel: (outside USA) Fax: CustomerService@sae.org SAE Web Address: Printed in USA

2 In contrast to fmri, functional near-infrared spectroscopy (fnirs) has attracted attention in recent years as a technique that uses near-infrared light to monitor changes in the levels of oxygenated and deoxygenated hemoglobin in the cerebral cortex. While fnirs has low spatial resolution for identifying activated areas of the brain, it has the advantage of enabling continuous monitoring of changes in cerebral blood flow because of its higher resolution. It also involves few restrictions because monitoring can be done by simply attaching to the subject's head the optical fibers for emitting and receiving NIR light. Additionally, it is not influenced by muscle electric activity and imposes relatively few restrictions on a subject's posture and movement. () For these reasons, it is expected to be used in medical rehabilitation and medical welfare (2) as well as in engineering applications such as for measuring the brain activity of train drivers () and drivers of motor vehicles. drivers also perceive and pay attention to other vehicles and pedestrians which might pose a potential risk. In order to investigate drivers' brain activity with respect to fundamental visual stimuli, the subjects in this study were shown video clips of actual driving scenes to which visual tasks were assigned and measurements were made of their brain activity. Brain activity was measured by fmri (Symphony.5T MRI Scanner, Siemens) and especially brain activity was also measured by fnirs (OMM, Shimadzu). Six male subjects, aged 22-55, took part in the experiments. The subjects were put in the fmri scanner in a supine position and performed the assigned tasks while watching video images projected on a small screen over their head. As shown in Fig., the video images shown were actual scenes of the forward view photographed during driving on public roads. The authors used an fnirs device to measure brain activity while subjects performed mental arithmetic and observed that activation in the lateral region corresponded to the relative difficulty of the calculations. The fnirs results also coincided with fmri measurements. (4-5) Ebe et al. () used fnirs to reveal that changes in the oxy-hb concentration in the region were larger when subjects searched for an unfamiliar place on a map than when they searched for a familiar place. Although they showed that fnirs can be applied to measure brain functions, they did not touch on the measurement of the higher-order brain activity involved in driving a vehicle, which is associated with attention, learning and other processes. In this study, fmri was first used to measure brain activity when operations equivalent to those of basic driving actions were applied and while varying the relative difficulty of assigned tasks that involved finding specified targets under a condition where visual stimuli corresponding to the driving environment were presented. Simultaneous with the fmri measurement, fnirs was used to confirm the activation state of the region. A driving simulator was then used to generate an experimental protocol that created a condition requiring spontaneous attention while driving. An attempt was made to use fnirs to identify drivers' brain activity attributable to the driving workload level and increased attention. BRAIN ACTIVITY RELATED TO FUNDAMENTAL DRIVING OPERATIONS AND STIMULI BRAIN ACTIVITY RELATED TO VISUAL STIMULI - Drivers direct their eyes toward various objects in the external environment while driving, though they may not be so conscious of doing so. They are constantly inputting visual information from outside the vehicle, and external visual stimuli constitute the fundamental environment for driving. In addition to these visual stimuli, Fig. Video image of actual driving scene Three different levels of difficulty were defined for the assigned visual stimulus tasks and the subjects' brain activity was compared for each of the various conditions. One task was simply to look at the driving scene. As a second task that was designed to increase the subjects' visual attention, they were instructed to count the number of oncoming vehicles that were seen in the video clip. As a third task that was designed to increase their visual attention further, the subjects were told to count both the number of oncoming vehicles and the number of pedestrians that they saw. The subjects were instructed not to move their arms and legs as they watched the screen in the resting supine position. In addition, they were told not to count the number of oncoming vehicles or pedestrians aloud. The six subjects' cerebral blood flow was measured by fmri under each of the visual stimulus conditions. The measured results were averaged and images were made of the state of brain activation related to each task as shown in Figs. 2-4.

3 osscipital (right) Fig. 2 Results when looking at the driving scene osscipital (left) ordinary visual stimuli which are constantly being input during driving do not induce pronounced brain function activity in the region. In other words, the visual stimuli in simple driving scenes do not influence brain activity. BRAIN ACTIVITY INFLUENCED BY BASIC DRIVING OPERATIONS - Cerebral blood flow was also investigated in the same way when the subjects operated a pedal located near their feet in the supine position and a lever controller located near their hands. Simultaneously with the fmri measurement, fnirs probes were attached to the region of the subjects' head so that their cerebral blood flow could be measured by fnirs. Brain activity was observed in the motor area of the region in the results measured by fmri, but pre blood flow was not influenced (Figs. 5 and ). (right) (left) osscipital (right) (left) Fig. Results when counting oncoming vehicles while looking at the driving scene osscipital Fig. 5 Blood flow when turning the steering wheel osscipital (right) (left) (right) (left) Fig. 4 Results when counting oncoming vehicles and pedestrians while looking at the driving scene Activation of the brain in the visual cortex of the occipital region can be observed in the fmri images. It is also seen that the size of the activated area in the occipital region differed according to the conditions of the assigned visual tasks. However, virtual no activation of brain function is seen in the pre region regardless of the visual task conditions. Although not shown in the figures, the simultaneous fnirs measurements showed the same result, with only sight changes seen in the concentration of oxygenated hemoglobin in the region. Presumably, the reason for this result is that Fig. Blood flow when operating a pedal NOTE - The foregoing results confirmed that ordinary basic driving behavior such as visual confirmation of the surrounding environment and the execution of fundamental driving operations like turning the steering wheel and operating pedals does not influence drivers' pre brain activity. Although not shown in the figures here, fmri measurements of cerebral blood flow were also made when the subjects performed secondary actions such as holding their breath. No activation of pre blood flow was seen in the results. Simultaneous fnirs measurements revealed the same

4 result, as oxygenated hemoglobin in the region changed only slightly. This confirmed that the fmrimeasured results supported the results of the cerebral blood flow measurements made by fnirs. were used, 47 channels for the and 4 channels for the occipital. This facilitated simultaneous measurement of brain activity throughout roughly the driver's entire head, excluding the sides. MEASUREMENT OF BRAIN ACTIVITY UNDER ACTUAL DRIVING BEHAVIOR REQUIRING ATTENTION The preceding section explained the fmri measurements of the subjects' cerebral blood flow under conditions in which they performed visual tasks and simulated basic driving operations such as turning the steering wheel and operating pedals. The results revealed that those actions were not artifacts that influenced pre brain activity, as was also indicated by the results of fnirs measurements. Frontal Channel 4 Therefore, situations involving car-following and driving on a narrow road were reproduced on a driving simulator as typical driving conditions involving the higher-order brain function of cognition. Using fnirs, drivers' brain activity was measured under these dynamic conditions for the following two purposes: to validate whether fnirs can be used to measure brain activity associated with driving, and to attempt to measure higher-order brain activity with fnirs during car-following and narrow road driving, representing two situations requiring spontaneous attention by drivers in different ways. Occipital Fig. Positions of optical probes Figure shows the measurement condition with the probes attached to the subject's head. The optical fibers to the probes were secured to the ceiling of the vehicle positioned on the driving simulator, thereby reducing the weight of the optical fibers so that the driver could shake his head easily. EXPERIMENTAL ENVIRONMENT AND EQUIPMENT - The driving simulator used to reproduce the two driving situations is shown in Fig. 7. Simulator Screen fnirs Fig. Subject s condition during mesurement Fig. 7 Apparatus of driving-simulator experiment The fnirs device was the same Shimadzu OMM unit that was used in the simultaneous measurements with fmri described above. As shown by the positions of the fnirs optical probes in Fig., a total of 5 channels EXPERIMENTAL CONDITIONS - The subjects were 2 males, aged 22-5 and who possessed a driver's license. To familiarize the subjects with the characteristics and operation of the driving simulator, they first practiced driving on a straight road before the fnirs device was attached. Along with receiving a full explanation of the purpose of the experiments, the subjects were told that they could stop the experiment at anytime if they felt sick or for other reasons. Their informed consent was obtained before starting the experiments.

5 Driving on a narrow road and car-following were reproduced on the driving simulator as the experimental driving situations. In addition, stop-and-go driving, involving standing-start acceleration, steady-speed driving and deceleration to a stop on a straight road, was also reproduced as a situation that often occurs when driving from one set of traffic lights to another. Figure shows typical examples of the stop-and-go driving, narrow road driving and car-following scenes that were shown on the driving simulator screen. The block design of the driving experiment for the fnirs measurement is shown in Fig.. During stop-and-go driving, the rest and task times were both set at 2 s, although the rest times at the start and end of the experiment were 4 s each (Tasks, 2 and ). The subjects performed three tasks in succession and the total experiment time was s. The block design for narrow road driving was the same as that for stop-andgo driving (Tasks 4, 5 and ). The task time in carfollowing driving was 2 s and the rest time was s, with only the initial rest time set at 2 s (Tasks 7, and ). Following a practice period, the subjects performed three tasks in succession and the total experiment time was 244 s. However, because the preceding vehicle braked suddenly after about 5 s of driving in the Task 7 period, the subsequent rest time was slightly longer than that of the other rest periods. The order of the experiments was stop-and-go-driving for reference, followed by narrow road driving and car-following driving. Stop-and-go Narrow road driving Stop-and-go driving Car-following Fig. Block design of driving experiment Narrow road driving Car-following Fig. Example views of driving-simulator experiment Three experimental conditions were defined for narrow road driving in order to vary the level of attention. Task 4 simply involved driving on a narrow road. In Task 5, the subjects were instructed beforehand that a pedestrian might suddenly dart into the road from either the right or left side, which was intended to evoke their attention (in actuality, no pedestrian darted into the road). In Task, the subjects were told beforehand that no pedestrian would dart into the road, thereby relieving the need to exercise attention. Task 5 in particular was a driving situation that required a high level of driver attention to avoid striking a pedestrian if one should suddenly dart into the road from either side. Three experimental conditions were also defined for the car-following situation in order to vary the level of attention, after the subjects initially practiced driving and stopping at a target position without any preceding vehicle. Task 7 simply involved following a preceding vehicle, which, however, braked suddenly so as to create a situation requiring the subjects' spontaneous attention, as would be required in the next Task. In Task, the subjects followed a preceding vehicle while being alert and paying attention to possible sudden braking by the vehicle ahead. In Task, once again, the subjects were not required to exercise attention as there was no preceding vehicle or target stopping position. Task in particular was a situation that required the subjects to drive cautiously and pay attention to the behavior of the preceding vehicle so as not to cause a rear-end collision.

6 NIRS SIGNAL PROCESSING AND ANALYSIS PROCESSING - The fnirs device uses the infrared region of the spectrum between 75 nm and nm, which is called the "optical window" in medical physics because there is relatively little absorption of infrared light by human skin and bones at these wavelengths. (7) This technique measures blood activity on the brain surface using differences in the spectral characteristics of the optical absorption coefficients of oxygenated hemoglobin (oxy-hb) and deoxygenated hemoglobin (deoxy-hb) in the blood. () The detected signal is converted to a corresponding value of the change in the hemoglobin concentration in the blood using the revised Lambert-Beer law. (2) Fig. 2 Examples of original fnirs singals other methods. Because this study dealt with successive signals measured during driving, an attempt was made to analyze the data using the differential of the fnirs signal, representing the change per unit of time in the relative value of the change in the hemoglobin concentration. Judgment of brain activation from the fnirs signal is generally done by performing a statistical analysis with the General Linear Model (GLM). () However, it has been pointed out that brain activation gradually declines when one subject repeats the same task multiple times. (2) Therefore, it was decided to find the standard score of the data of the multiple subjects under the same condition so as to be able to apply this GLM statistical analysis technique. MEASURED RESULTS - Figure 4 shows typical results for the brain activity measured for approximately the entire head during the driving experiments. In the case of a subject having a high signal-to-noise (S/N) ratio of the fnirs signal from the occipital, the results show activation in the visual area of the occipital and the lateral region and suppression in the midline region. As explained earlier, activation in the visual area of the occipital was also seen in the fmri results under the application of visual stimuli. Since the number of subjects is rather limited for making a comparison of the activation condition of the entire head, only the results of the analysis of the data are presented here. Parietal Parietal Fig. Example of reconstructed fnirs signals However, the fnirs signal also contains noise, i.e., artifacts, in addition to brain activity, making it necessary to extract only the signal related to the task that is the target of the measurement (Fig. 2). One general signal processing method that can be used in this regard is signal averaging, whereby multiple data records are summed to find one averaged signal, similar to the event-related potential (ERP) technique. () However, in the case of driving an automobile which constantly involves successive processing of different tasks, an effective method for extracting only the signal related to the target of the measurement is to perform a multiresolution analysis of the discrete wavelet transform of the original signal without doing any signal averaging. () In this study, multiresolution analysis was used to remove noise from the fnirs signal (Fig. ). This technique was selected because the aim was to measure higher-order brain functions while driving and, in particular, to extract brain activity under conditions requiring spontaneous attention on the part of the subjects. The fnirs signal is typically analyzed by comparing the relative values of the change in the hemoglobin concentration in the blood or by evaluating the area ratios of the activated regions of the brain, () among Frontal Temporal Occipital Fig. 4 Frontal & occipital cortex activity during narrow road driving Figures 5, and show the fnirs measured results for stop-and-go driving, narrow road driving and carfollowing driving, respectively. The graph in each figure shows the time-history data for channel 4 in the right lateral pre region. The vertical axis shows the z- value (standard score) of the fnirs signals measured for the 2 subjects (only subjects in the case of carfollowing driving). The experiment from start to finish is divided at nine points, labeled from () to (), and mapping images of brain activity at each point are shown below each graph. For stop-and-go driving, increases and decreases in oxy- Hb are seen in tandem with the execution of the three tasks, and the level of brain activation decreases as the number of tasks increases. A spatial feature of brain

7 activity seen in the mapping images is that activity increases in both lateral regions but decreases in the midline region when a task is performed. e lu a V - Z oxyhb deoxyhb.5 R T R T R T R Time(sec) Task Task 2 Task Channel (Channel 4) For narrow road driving as well, it is seen that oxy-hb increases and decreases coincident with all the tasks. However, Task 5 that had the highest level of concentration shows a lower level of oxy-hb than Task 4, and no difference is seen in the spatial features either. These results imply that brain activity under conditions requiring attention cannot be extracted with a simple measurement method. In addition to analyzing brain activity in rest-task periods, a method has also been proposed for analyzing the differential between tasks. () Since there are examples of analyses between tasks, the fnirs signal for stop-and-go driving, which is a general driving pattern having the same block design as that for narrow road driving, was used here in an effort to extract the change in the level of attention during narrow road driving, based on the differential between the two driving situations. The results obtained are shown in Fig. 7. Task 5, for which the subjects were instructed beforehand that a pedestrian might dart into the road, showed the highest z-value for oxy-hb in the lateral region for all the tasks. This confirmed the correlation between the attention level during a task and the measured fnirs signal. Fig. 5 Reconstructed fnirs signal during stop-and-go driving e lu a V - Z oxyhb deoxyhb (Channel 4) R T R T R T R Time(sec) e lu a V - Z oxyhb deoxyhb (Channel 4) R T R T R T R Time(sec) Task 4- Task 5-2 Task Task 4 Task 5 Task (s) (s) 2(s) 2 5 Fig. Reconstructed fnirs signal during narrow road driving - Fig. 7 Differential between narrow road driving and stop-and-go driving Similar to narrow road driving, the experimental results for car-following driving in Fig. show a lower level of brain activity for Task, for which the highest level of attention was defined, than for Task 7. Since there is no reference condition with the same experimental block design, the level of attention cannot be extracted from the difference between the tasks. However, the results of subjective evaluations conducted after the driving experiments concerning the relative difficulty of the tasks

8 and perceived workload. Each subject rated the tasks on a 5-point scale, with a score of indicating no workload and a score of 5 an extremely difficult workload. e lu a V - Z oxyhb deoxyhb.5 R T R T R T R T R Time (sec) Task 7 Task Task 4 7 (Channel 4) advance instruction about sudden braking by the vehicle ahead. Accordingly, these results show the possibility of using fnirs to measure driver workload, similar to the results seen for the relative difficulty of mental arithmetic. Subjective score Z score Fig. 2 Relationship between subjective score and Z- value.5.5 * * - *:p<.5 (Channel ) Fig. Reconstructed fnirs signal during car-following Subjective score Task Fig. Subjective workload scores during car-following The results of subjective evaluations show the highest scores for Task 7, followed by Task and Task in that order (Fig. ). A correlation was seen between the subjective evaluation results for car-following driving and the z-value of the differential of the fnirs signal (Fig. 2). A t-test between the tasks showed a level of confidence of less than 5% between Task 7 and Task, both with a preceding vehicle, and between Task 7 and Task, indicating a significant difference (Fig. 2). This can be attributed to the possibility of an increased psychological workload under the Task 7 condition. It can also be understood from the fact that nearly all of the subjects ran into the rear of the preceding vehicle when it braked suddenly, inasmuch as they did not receive any Z score Z-value Task Fig. 2 Z-value of fnirs signal differential car-following NOTE - Attention is also related to the driving workload and it is difficult to separate the two. Yet, as shown in this study, the subjects' state of attention while driving on a narrow road was extracted by using the difference from a reference condition for the same experimental block design. Brain activity associated with that attention was successfully measured. However, in future work it will be necessary to validate the possibility of obtaining brain activity measurements with and without an attention task under the same driving experiment conditions by applying a method in which the subject group or other factors are varied. Although a correlation between the driving workload and certain brain activity was measured for car-following driving, it was not possible to extract differences in the level of attention. Presumably, the reason for that is related to the influence of the experimental protocol that evoked spontaneous attention and learning by the subjects. The experimental protocol will require further innovation in future work.

9 CONCLUSION Simultaneous fmri and fnirs measurements were made for tasks requiring visual attention, which is the fundamental condition for driving, and for tasks simulating basic driving operations such as turning the steering wheel and operating pedals. Activation of the visual area and motor area of the brain corresponding to these tasks was confirmed by fmri. However, both the fmri and fnirs measurements coincided in showing no change in blood flow in relation to these tasks. This suggests that under the application of such basic visual stimuli and operations alone, it is difficult to measure distinctive pre brain activity while driving. A driving simulator was then used to reproduce the conditions of car-following and narrow road driving as typical driving situations involving the higher-order function of cognition. Using fnirs, drivers' brain activity was measured under dynamic conditions resembling those of actual driving. The results for car-following driving confirmed a correlation between activation in the lateral region and subjective workload evaluations, indicating the possibility of measuring the driving workload using fnirs. For narrow road driving under a condition where a pedestrian might possibly dart into the road, thus requiring spontaneous attention by the driver, a difference was extracted compared with ordinary stopand-go driving operations. It was found that the subjects' lateral region showed a higher level of activation when the level of attention was high. This result indicates the possibility of using fnirs to measure drivers' level of attention while driving. The reliability of the fnirs signal has already been validated by simultaneous measurements made by fmri while subjects did mental arithmetic. However, the reliability of the fnirs signal with respect to spatial resolution remains an issue. Research concerning the use of fmri to measure the higher-order brain functions of drivers while operating a vehicle has been reported. (4) In order to apply fnirs for measuring drivers' higherorder brain functions while driving, it will be necessary to accumulate more data measured simultaneously with fmri measurements and to identify the activated areas so as to enhance the reliability of the fnirs signal. REFERENCES. Y. Yamashita, A. Maki and H. Koizumi, Measurement system for Noninvasive dynamic optical topography, Journal of Biomedical Optics, October, Vol. 4, pp (). 2. T. Morikawa, H. Shinohara, Y. Matsuo, T. Nakamae, T. Yamamoto, M. Ohtaki, and H.Kajita, A study of brain hemodynamics in the writing task using near infrared spectroscopy, Kobe Gakuin Univ. Journal of Rehabilitation Vol. 2, No. 2, pp. 2- (27).. T. Kojima, H. Tsunashima, T. Shiozawa, H. Takada and T. Sakai, Measurement of train driver s Brain Activity by functional near-infrared spectroscopy (fnirs), Optical and Quantum Electronics, No. 7, pp. - (25). 4. T. Kojima, T. Yanaginuma, H. Tsunashima, S. Hirose, T. Shimizu, T. Shiozawa, M. Taira and T. Haji, Measurement of Higher Brain Function with Workload by Using Function Near-Infrared Spectroscopy (fnirs), JSAE Annual Congress Spring, No. -7, pp. -22 (27). 5. K. Yanagisawa, H. Tsunashima, Y. Marumo, S. Hirose, T. Shimizu, M. Taira and T. Haji, Measurement and Evaluation of Higher Brain Function by Using Functional Near-Infrared Spectroscopy (fnirs), JSAE annual congress Fall, No. 44-, pp.-4 (2).. K. Ebe, M. Okuwa and H. Inagaki, Evaluation of Driver s Mental Workload Due to Visual and Auditory Cognition, R&D Review of Toyota, Vol. 4, No., pp (). 7. I. Oda, Optical and electro-optical engineering contact, JOEM, Vol. 42, No. 7 pp (24).. H. Eda, Near infrared spectroscopic measurement of brain activation and its limitations, ITE Technical Report Vol. No. 5, pp. - (2).. Y. Yamashita, A. Maki, T. Yamamoto and H. Koizumi, Noninvasive Imaging of Human Brain Activity using Near-Infrared Light-Optical Topography- Journal of Spectroscopical Research of Japan, Vol. 4, No., pp (2).. T. Kozima, H. Tsunashima, S. Itoh and T. Shiozawa, Measurement of Brain Function of Train Driver Using Functional Near-infrared Spectroscopy (fnirs), Japanese Journal of Ergonomics, Vol. 4, No. 4, pp. -2 (27).. S. Kohno, A. Ishikawa, S. Tsuneishi, T. Amita, K. Shimizu and Y. Mukuta, Application development of functional near-infrared imaging system, Shimadzu Review, Vol., No..4 pp. 5-2 (2) 2. K. Takahashi, N. Watanabe and T. Harada, Preliminary experiment of the evaluation of a VR based training system using brain activity, Virtual Reality Society of Japan Annual Conference Vol. pp (2).. Y. Hoshi, I. Oda, Y. Wada, Y. Ito, Y. Yamashita, M. Oda, K. Ohta, Y. Yamada and M. Tamura, Visuospatial imagery is a fruitful strategy for the digit span backward task, a study with near-infrared optical tomography, Cognitive Brain Research,, pp. -42 (2). 4. H. J. Spiers and E. A. Maguire, Neural substrates of driving behaviour, Neuro Image, No., pp (27).