Paper16.doc.doc.doc.doc

Using Spectral Analysis to Extract Frequency Components from Electroencephalography: Application for Fatigue Countermeasure in Train Drivers

Budi Thomas Jap*, Sara Lal*, Peter Fischer+, Evangelos Bekiaris^

University of Technology, Sydney (UTS), Broadway NSW 2007, Australia

+ Signal Network Technology, Lane Cove NSW 2066, Australia

^ Center for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thermi, Greece

[email protected], [email protected], [email protected], [email protected]

AbstractTrain accidents can have a massive impact towards the surrounding area as well as the general community. Most train

accidents can be attributed to fatigue, and hence, development of fatigue countermeasure devices that can warn drivers of fatigue status and prevent accidents can greatly benefit train drivers, passengers, society and general community. Electroencephalography (EEG) has been proven to be one of the most reliable indicators of fatigue. This study investigated the change of brain activity during fatigue-instigating monotonous driving session, by extracting the four frequency components (delta, theta, alpha, and beta) using FFT spectral analysis at different brain sites (frontal, central, temporal, parietal, and occipital). Results identified some statistically significant differences between early and later stages of driving in delta, theta and beta activities at different brain sites. The results of the current study may be used for future development of fatigue countermeasure by targeting specific frequency component and brain sites

Keywords: Spectral analysis, fatigue, countermeasure, electroencephalography

1. IntroductionRailway is the backbone of most industries to transport goods from a factory or warehouse to the

consumers in other states, and it is one of the cheapest methods for people to travel interstate and

intrastate [1]. The number of train accidents and injuries in Australia is relatively low, and compared to road

and aviation industries, rail is a relatively safer transportation means [2]. However, given the fact that the

travelling speed of a train is relatively faster than a car, a train carriage is much heavier than a car, and that

trains’ braking time is slower than a car (about 1.1 m/s2) [3], the impact that it creates in an accident can

greatly affect the surrounding area and the community as a whole. Bureau of Transport and Regional

Economics (BTRE) [4] reported that not only the people directly involved in the accidents, such as the

drivers and the passengers, but also the whole community is affected by the accident.

Fatigue is found to be one of the three biggest killers on Australian roads, along with speeding and drink

driving, and research indicates that fatigue is four times more likely the cause of an accident than drugs or

alcohol [5]. National Transport Commission (NTC) [2] has acknowledged fatigue as a significant causal

factor in industrial accidents, especially in those that operate on a continuous basis and require workers to

work long hours, night shifts or rotating shifts. Transportation industries, such as rail, road, and so on, all

face similar challenges to minimize the degree of fatigue in its workforce [2].

Approximately 75% of all train accidents can be attributed to human errors, which are mostly caused by

train crew members when they are in a fatigued state [6, 7]. Train driving task is a complex task and relies

heavily on several aspects of neuron-cognitive functioning, including sustained attention, object detection

and recognition, memory, planning, decision-making and workload management [8]. Fatigue affects both

the physical capacity and the cognitive and other mental processes to perform work [2], and is associated

with lapses in attention, longer response times and more frequent errors, and also have increased difficulty

identifying and processing important information from the surrounding environment [8]. For example, it has

been noted that train drivers in a fatigued state tend to use the brake less and travel at faster speeds [8].

Therefore, there is a need for a robust fatigue countermeasure device to detect when the driver starts

showing early signs of fatigue. There are many developments of fatigue countermeasures, such as

development of electroencephalography (EEG) algorithms to detect fatigue [9], facial movement detector

[10], and PERCLOS, which detects the percentage of eye closure [11]. However, Artaud et al. [12] found

that EEG is one of the most reliable indicators of fatigue. A study by Lal and Craig [13] found a good test

and retest reliability and high reproducibility for the delta and theta bands, and another study by Gasser,

Bacher and Steinberg [14] found the frequency of alpha rhythm also showed a good reliability. Acceptable

test and retest reliability has also been found in resting alpha activity [15], and others found acceptable

reliability for theta, alpha, and beta frequency bands [16].

Four frequency components in EEG exist, which are delta (δ) (0 – 4 Hz), theta (θ) (4 – 8 Hz), alpha (α)

(8 – 13 Hz), and beta (β) (13 – 35 Hz), which can be measured to detect the current state of a driver [17].

Delta activity shows higher activity during sleep. An increase in theta activity can indicate early stage of

drowsiness [18]. Alpha activity indicates a state of relaxed wakefulness, which decreases with

concentration, stimulation, or visual fixation [19]. An increase in alpha activity has also been found in train

drivers, who were really sleepy and fell asleep while driving [18, 20]. An increase in beta activity is related

to alertness level, and a decrease with drowsiness [21]. Torsvall and Åkerstedt [20] believed that alpha

activity was the most sensitive measure that could be used in detecting fatigue, followed by theta and delta

activities. However, delta activity is more related to occurrence of sleep proper [20].

Several studies have proposed methods of fatigue detection using EEG, such as a study by Tietze [22]

that proposed the detection of alpha spindles to detect fatigue. Other studies have proposed two

algorithms, which were (θ+α)/β and β/α, that can be used as a fatigue detection technique [21, 23]. Others

have proposed the combination of FFT and neural network to classify alertness and drowsiness [24], the

use of wavelet transform [25, 26], or the use of Independent Component Analysis (ICA) algorithm [27].

The current study investigated the changes of EEG activities, delta, theta, alpha, and beta, during

fatigue-inducing monotonous driving session for future development of fatigue countermeasure devices.

2. Methods

A total of 52 non-professional drivers (36 males and 16 females), aged 20 to 70 years (mean 26 9

years) were recruited to perform a monotonous driving simulator task. All participants provided informed

consent prior to participating in the study. For selection criteria, The lifestyle appraisal questionnaire was

used as a guideline that required all participants to have no medical contraindications, such as severe

concomitant disease, alcoholism, drug abuse, and psychological or intellectual problems that were likely to

limit compliance [28].

The study has approval from the Human Research Ethics Committee, and was conducted in a

temperature-controlled laboratory at around noon 1.5 hours. Participants were asked to refrain from

caffeine consumption (tea or coffee) and from smoking approximately 4 hours prior to the study. Alcohol

consumption was also refrained for approximately 24 hours before the study. All participants reported

compliance with these instructions.

Grand Prix 2 (version 1.0b, 1996, Microprose Software, Inc., USA) was used as the driving simulator

software for the study. The display from the software showed other cars, the driving environment, the

current speed, and other road stimuli. The simulator consisted of a car frame with an in-built steering

wheel, brakes, accelerator, and gear change buttons.

Two driving sessions were completed for the purpose of the current study. The first driving session was

the alert or active driving session for approximately 10 to 15 minutes, which would serve as a baseline

measure. During this driving session, the driving environment involved other cars and stimuli on the road.

The second driving session was the monotonous driving session for approximately 1 hour with speed limit

restricted to between 60 – 80 km/h. This session involved the participants driving with very few road stimuli.

Simultaneous physiological measurements were recorded during the driving sessions, using NeuroScan

system (Compumedics, Australia). A total of 30 channels of electroencephalography (EEG) were

measured simultaneously while participants were driving. The 10-20 international standard of electrode

placement was applied [29]. A referential montage was used with the reference point located at the center

of the head, between the midline central electrode (Cz), and the midline central parietal electrode (CPz).

The sample rate for the EEG recording was 1000 Hz. The current NeuroScan system was not a wireless

EEG system, and was only used for data collection and analysis purposes, prior to the development of

simple and wireless EEG system that was viable for use in the real driving environment. In addition to the

EEG recording, blood pressure and heart rate were collected before and after the driving task.

The literature specified that delta frequency range was from 0 – 4 Hz [17], and it is easily affected by DC

voltage (0 Hz), which might compromise the result of the experiment. Therefore, the EEG time-domain

recording was filtered with a 6th order Butterworth high-pass filter with cutoff frequency at 0.5 Hz.

Butterworth low-pass filter was also applied with cutoff frequency at 35 Hz to filter out frequencies higher

than beta frequency band, such as muscle artifacts.

All 30 channels of the EEG recording were then split into 1-second epochs, and were subjected to Fast

Fourier Transform (FFT) algorithm (Hanning Window) to derive spectral power for the four frequency

components, which were delta, theta, alpha, and beta. The total data for the monotonous driving session

was segmented into 10 equal time sections of approximately 6 minutes for each section. Three lots of thirty

1-second epochs were averaged in each of the alert and the monotonous driving sections to obtain on

value for each section. The result for the ten sections of monotonous driving session was then compared to

the alert baseline. The EEG channel values for each section (both in alert and monotonous driving

sessions) were then averaged according to the different sites of the brain, where the electrodes were

located, to obtain 5 site averages, which were frontal, central, temporal, parietal, and occipital sites.

For the current study, delta, theta, alpha, and beta activities were compared during the data analysis to

identify changes in those activities during fatigue-inducing driving session. Analysis of Variance (ANOVA)

was performed to identify significant differences between the 10 time points and the alert baseline. This

analysis was performed for delta, theta, alpha, and beta separately for the five different sites of the brain

(central, frontal, occipital, parietal, and temporal).

3. Results

The average body mass index (BMI) of participants was 23 ± 7 kg/m2 (normal range: 18.50 – 24.99

kg/m2 [31]), which indicated that the participants were in normal proportion between weight and height. The

average pre-study blood pressure was 118 11 mmHg (systolic) and 75 9 mmHg (diastolic), and the

average post-study blood pressure was 114 13 mmHg (systolic) and 71 17 mmHg (diastolic). The

average heart beat was 72 10 beats/sec for the pre-study and 65 9 beats/sec for the post study. T-

test analysis on the pre-study and post-study results of blood pressure and heart beat revealed significant

differences for systolic blood pressure and heart beat (refer to ). Heart beat and blood pressure are

normally higher when a person is alert and lower during fatigue or resting period [32]. The statistically

significant lower blood pressure and heart beat at the end of the study showed that participants were

considerably fatigued after the monotonous driving session. The average driving time was 1 hour and 3

minutes 12 minutes. Gillberg, Kecklund and Åkerstedt [33] has previously demonstrated that a 30-

minute of monotonous driving will lead to lower alertness level, which is reflective of a fatigued state.

Table 1 t-test result for difference between pre- and post- measurements of blood

pressure and heart beat (significant p-value italicised)

Diff Std. Dev. Diff t p

Systolic BP 4.1 10.92 2.8 0.009

Diastolic BP 4.6 16.83 2.0 0.05

Heart beat 5.9 8.025 5.4 < 0.001

Note: BP = blood pressure

Figure 1 shows an example of brain topography for one of the volunteers, indicating delta, theta, alpha,

and beta activities. Red-shaded areas indicate high activity, and blue-shaded areas indicate low activity. In

this example, delta activity increased in the frontal region at the beginning of the driving session (sections 1

to 5), and decreased towards the end of the driving session (sections 8 to 10). Unlike delta activity, theta

activity steadily increased in the frontal region, starting from time section 3. Theta was higher in the frontal,

temporal, and occipital areas towards the end of the driving session (sections 9 to 10). Alpha activity in the

occipital region increased gradually, starting from low alpha activity in section2, to high alpha activity in

section 10. Beta activity showed almost the opposite from the alpha and theta activities, in which it showed

a decrease of activities. Beta activity showed higher activity in the frontal and temporal regions during alert

driving, and decreased significantly towards the end of the driving session (starting from section 6).

Figure 1 Example of brain topography of alert baseline and 10 time sections during

monotonous driving

ANOVA analysis revealed significant differences during the comparison between the alert baseline and

the 10 time points of monotonous driving. Significant differences were found at temporal sites only for alpha

(F = 2.0, p = 0.03), and beta (F = 2.3, p = 0.01). No significant differences were found at other sites. Delta

showed significant differences for the frontal site (F = 3.5, p < 0.001), whereas theta showed significant

differences for central (F = 2.3, p = 0.01), frontal (F = 2.3, p = 0.01), and parietal (F = 3.6, p < 0.001). Refer

to Table 2.

Table 2 ANOVA result for delta, theta, alpha and beta

C F O P T

Delta - 3.5/<0.001 - - -

Theta 2.3/0.01 2.3/0.01 - 3.6/<0.001 -

Alpha - - - - 2.0/0.03

Beta - - - - 2.3/0.01

Note: Showing significant p-value only (<0.05). C = central, F = frontal, O = occipital, P = parietal, T =

temporal.

Beta activity revealed significant difference between alert baseline and time section 10 (p = 0.031). Post-

hoc Bonferroni analysis for alpha activity revealed no significant differences. Post-hoc Bonferroni analysis

for delta activity at frontal site revealed significant differences in activities between alert baseline and time

sections 1 (p < 0.001), and 2 (p = 0.024), and between time sections 1-4 (p = 0.017), 1-5 (p = 0.021), 1-8 (p

= 0.008), 1-9 (p = 0.036), and 1-10 (p = 0.032). Post-hoc Bonferroni analysis for theta activity for central (p

= 0.006), and frontal (p = 0.006) sites revealed significant differences between alert baseline and time

section 2, whereas result for parietal site revealed significant differences between alert baseline and time

section 2 (p < 0.001), and between time sections 2-4 (p = 0.028), 2-5 (p = 0.015), 2-6 (p = 0.037), 2-7 (p =

0.048), 2-8 (p = 0.035), 2-9 (p = 0.023), and 2-10 (p = 0.021). Refer to Table 3.

Table 3 Post-hoc Bonferroni analysis for results showing significant p-value in ANOVA

Site Comparat

or

01 02 03 04 05 06 07 08 09 10

Delta F Alert <0.001 0.02 - - - - - - - -

Delta F 01 - - 0.02 0.02 - - 0.008 0.04 0.03

Theta C Alert - 0.006 - - - - - - - -

Theta F Alert - 0.006 - - - - - - - -

Theta P Alert - <0.001 - - - - - - - -

Theta P 02 - - 0.03 0.02 0.04 0.048 0.04 0.02 0.02

Alpha T - - - - - - - - - -

Beta T Alert - - - - - - - - - 0.03

Note: Showing significant p-value only (<0.05). C = central, F = frontal, P = parietal, T = temporal.

The plots shown in Figure 2 show an example of the changes for delta, theta, alpha and beta activities

over time at the temporal site. The slow wave EEG activities (delta and theta) show an initial increase

followed by a decrease, and then is steady over time (Figure 2a and b), whereas the fast wave EEG

activities (alpha and beta) show a general decrease over time, indicated by a slight decrease of alpha

activity, and a steeper decrease for beta activity (Figure 2c and d).

Figure 2 Temporal activity plotted over time during driving for delta, theta, alpha, and

beta

4. Discussion

There is strong evidence supporting a relationship between railway accidents and locomotive crew

fatigue, such as time on duty, work-sleep-rest cycles and shift work [6]. Fatigue and severe sleepiness at

work are common among personnel responsible for train driving and the control of train traffic [34, 35].

Härmä et al. [34] conducted a study on irregular shift and the effect on sleepiness for train drivers and

controllers and found that over half of the train drivers and traffic controllers reported severe fatigue during

their night shifts, and fatigue occurred in about 20% of all morning shifts. Austin and Drummond [35] found

that about 25% of train drivers dozed off while driving or waiting at the station. Unlike the driver of a car

who can swerve his or her vehicle to avoid collision, a train driver can only apply the emergency brake,

sound the horn and hope that the train will stop in time before the collision [35]. Hence, investigation into

possible means to prevent train accidents will greatly assist the community.

EEG has been found to be a reliable fatigue indicator [12] and can be used in a fatigue countermeasure

device. It also has been proven to have a high reliability and reproducibility in the delta, theta and alpha

bands in the previous studies [13, 14]. Hence, this study assesses the change in brain activity from the

alert baseline to the monotonous driving session.

From the result of the current study, significant differences were found at the temporal site for alpha and

beta activities, at the frontal site for delta activity, and at the central, frontal, and parietal sites for theta

activities. The EEG activity in alert baseline and early stages of fatigue were significantly different to the

later stages of fatigue. For example, the delta activity in the frontal site, theta in the parietal site, and beta

activity in the temporal site, which were significantly lower during later stages of driving.

Figure 2 captures the information presented in Table 2 and Table 3 for the temporal site only. Other sites

of the brain also show similar changes for each brain activity, with slight difference in the magnitude of

change. However, from this figure, a significant decrease in beta activity can be observed, which can also

be utilized in a fatigue detection algorithm. The gradual decrease of alpha activity can also be utilized

together with beta activity to improve robustness of fatigue detection process.

Different EEG algorithms can be combined together in order to increase reliability and accuracy when

detecting fatigue [36]. This approach would most likely be successful for on-road fatigue detection in the

longer term, because it combined different approaches to produce a solid fatigue detection system.

However, such system can become complex because there will be some needs for an agreement in the

outcome of the analysis of all the different algorithms about the current state of the driver. The

disadvantage of using multiple algorithms in detecting driver fatigue is when the outputs contradict each

other and the system does not have certainty whether a driver is fatigued or not.

However, as reliable as it can be in detecting fatigue, the setup for fatigue monitoring process using an

EEG is a complex procedure, and in its current status, it would be difficult to use in the real driving

environment [37]. Hence, simple and wireless EEG systems that can be worn by drivers whilst driving need

to be researched and developed, before fatigue countermeasures employing the accuracy and reliability of

EEG to detect fatigue will become viable.

For the purposes of transmitting an alert signal to the train dashboard, Bluetooth can be utilized as a

Personal Area Network (PAN) for the application of a simple EEG device to monitor fatigue. Bluetooth is a

non-expensive, short range data communication with low data rate and low power consumption [38-41].

The fatigue countermeasure device needs to be battery-powered, and hence, Bluetooth’s low power

feature works together to prolong the battery life of the device. Train drivers will be confined to the train

cabin, and hence, the short range data communication that Bluetooth offers is sufficient for the fatigue

detection application. The short range data communication can also be viewed as a security measure,

which reduces the probability of eavesdropping from other wireless devices.

The existing communication network in the current train system can also be utilized to transmit

information back to the train control base. Should the train be stopped due to the driver dozing off, or

having other problems, during driving, the control base may need to be alerted to this event, which may

enable them to take appropriate safety measure to the situation.

5. Conclusion

The result of the current study showed a significant change in the brain activity during a fatigue-inducing

driving session, and several sites that showed significant changes during fatigue could potentially be used

to detect fatigue. Future development of EEG-based fatigue countermeasure device can utilize the results

of the current study to improve detection of fatigue by targeting specific frequency bands and sites that

show significant changes. However, because of the complexity of the current equipment, the equipment

used in this study was not suitable for use in future driver fatigue countermeasure device. A simple and

wireless EEG device needs to be developed to suit the driving environment. The EEG device may utilize

sites on the brain, at which significant differences occur between alert and fatigue states in this study, such

as a large drop of beta activity over a period of time. This study has implications on future development of

driver fatigue countermeasures using wireless technology for application in the railway industry.

6. Acknowledgement

The research was supported by an ARC Linkage grant Australia (LP0560886) and by SENSATION

Integrated Project (FP6-507231) co-funded by the Sixth Framework Programme of the European

Commission under the Information Society Technologies priority.

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