DATA REDUCTION OR DATA FUSION IN BISOGINAL

PROCESSING?

Martin Golz

, David Sommer

and Udo Trutschel

2,3

University of Applied Sciences Schmalkalden, Faculty of Computer Science, Schmalkalden, Germany

Department Circadian Technologies Inc., Stoneham, Massachusetts, U.S.A.

Institute for System Analysis and Applied Numerics, Tabarz, Germany

Keywords: EEG, EOG, Eyetracking, Driving Simulator, Microsleep, Vigilance Monitoring, Computational

Intelligence, Support Vector Machines, Feature Fusion, Feature Reduction, Validation.

Abstract: When subjects are monitored over long time spans and when several biosignals are derived a large amount

of data has to be processed. In consequence, the number of features which has to be extracted is mostly very

restricted in order to avoid the so-called “curse of high dimensionality”. Donoho (Donoho, 2000) stated that

this applies only if algorithms perform local in order to search systematically for general discriminant

functions in a high-dimensional space. If they take into account a concept for regularization between

locality and globality “blessings of high dimensionality” are to be expected. The aim of the present study is

to examine this on a particular real world data set. Different biosignals were recorded during simulated

overnight driving in order to detect driver’s microsleep events (MSE). It is investigated if data fusion of

different signals reduces detection errors or if data reduction is beneficial. This

was

realized

for nine

electroencephalography, two electrooculography, and for six eyetracking signals. Features were extracted of

all signals and were processed during a training process by computational intelligence methods in order to

find a discriminant function which separates MSE and Non-MSE. The true detection error of MSE was

estimated based on cross-validation. Results indicate that fusion of all signals and all features is most

beneficial. Feature reduction was of limited success and was slightly beneficial if Power Spectral Densities

were averaged in many narrow spectral bands. In conclusion, the processing of several biosignals and the

fusion of many features by computational intelligence methods has the potential to establish a reference

standard (gold standard) for the detection of extreme fatigue and of dangerous microsleep events which is

needed for upcoming Fatigue Monitoring Technologies.

1 INTRODUCTION

The fusion of many features is often under criticism,

because it is assumed that processing of a large

number of features leads to performance

deteriorations of classifiers. This is because local

optimizations of discriminant functions suffer from

the so-called “curse of high dimensionality”. It has

been shown theoretically that non-local learning

algorithms, like the Support Vector Machine (SVM),

suffer less from this problem and that there are also

“blessings of high dimensionality” (Donoho, 2000),

i.e. certain random fluctuations are very well

controlled in high dimensions, whereas in moderate

dimensions these fluctuations lead to deteriorations

in statistical measures. Therefore, the question of

fusion or reduction of features remains open and

answers depend on signal characteristics

(nonlinearities, randomness) and should be given

problem specific.

Here we present experimental investigations uti-

lizing 15 different signals of electroencephalography

(EEG), electrooculography (EOG), and eyetracking

signals (ETS). All signals are featured by relatively

high temporal resolution and are corrupted by large

noise originated by a lot of other simultaneously

ongoing brain processes. This leads to more or less

extensive signal processing which results in a large

variety of different features. Then, it is often discus-

sed if a fusion of all features or, in controversy, fea-

ture reduction should be strived in order to optimize

performance of subsequent processing methods. On

the one hand, fusion of features of different types of

signals should be beneficial, because EEG, EOG and

ETS are reflecting different processes. On the other

hand, ETS and EOG are relatively close related.

440

Golz M., Sommer D. and Trustschel U. (2009).

DATA REDUCTION OR DATA FUSION IN BISOGINAL PROCESSING?.

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pages 440-445

DOI: 10.5220/0001782604400445

 SciTePress

Both contain components of eye movement, but they

are differing in that ETS outputs the time series of

pupil size and that the EOG contains components of

blink movements. Therefore, it should be of interest

if a fusion of both closely related types of signals is

still of advantage or not.

2 VIGILANCE MONITORING

Over the past years the development of vigilance

monitoring systems has made considerable progress.

In case of applications to transportation industries

several stages of interactions between the system

and the driver are under discussion and are to some

extend implemented. On a low level of interaction

the estimated vigilance level is displayed to the

driver in order to give him a feedback and to support

his own decision making. Advantageously, the accu-

racy in such “alertometer” applications must be at

least as high as to display the vigilance level in two,

or three, or some more steps. This is not the case on

higher levels of interaction where e.g. acoustic or

visual stimuli are presented in order to give insistent

warnings to the driver. Highly accurate estimations

are required here. If the rate of false alarms would be

too high, such systems are scarcely accepted by

drivers. On the other hand, missing errors are very

dangerous for the driver and are therefore not accep-

table especially during very low vigilance and in its

extreme extent, the microsleep events (MSE). The

latter are defined as short and non-anticipated intru-

sions of sleep into wakefulness under demands of

sustained attention (www.microsleep.de).

The question remains whether there exists a

unique sign of extreme fatigue and of MSE which

can be measured non-intricately. In a recent paper

Schleicher et al. (Schleicher et al., 2007)

investigated oculomotoric parameters in a data set of

82 subjects. The parameter most correlating to

independent vigilance ratings was the duration of

eye blinks. In addition to correlation analysis this

parameter was investigated in detail immediately

before and after a MSE which they defined as

overlong eye blinks. The mean duration of overlong

eye blinks is substantially longer (269 ms) than of

blinks immediately before (204 ms) and after (189

ms) a MSE. Furthermore, considerable inter-

individual differences were reported and the

duration of overlong eye blinks seems to be much

lower than the reported 700 ms of Summala et al

(Summala et al., 1999). Ingre et al (Ingre et al.,

2006) also reported large inter-individual variability

of blink duration in a driving simulation study of 10

subjects after working on a night shift. In

conclusion, only gradual changes and a large inter-

subject variability are observable in this important

parameter which is mostly used in industrial fatigue

monitoring devices. The same is reported of other

variables, e.g. delay of lid reopening, blink interval,

and standardised lid closure speed (Schleicher et al.,

2007).

EEG studies on strong fatigue of several authors

have resulted in a similar picture of inter-individual

differences, of non-unique parameter values and of

non-specific patterns. In their review paper Santama-

ria and Chiappa (Santamaria and Chiappa, 1987)

stated: “There is a great deal of variability in the

EEG of drowsiness among different subjects”. In a

large normative study with 200 male subjects the

EEG of drowsiness was found to have “infinitely

more complex and variable patterns than the wakeful

EEG pattern” (Maulsby et al., 1968). Åkerstedt et al.

(Akerstedt et al., 1991) showed that with increasing

working time subjectively rated sleepiness strongly

increases and the EEG showed a significant but

moderate increase of hourly mean power spectral

densities (PSD) only in the alpha but not in the theta

band. In contrast, Makeig & Jung (Makeig and Jung,

1995) concluded from their study that the EEG

typically loses its prominent alpha and beta frequen-

cies as lower frequency theta activity appears at the

time when performance is deteriorating due to strong

fatigue. Sleep deprived subjects performing a conti-

nuous visuomotor compensatory tracking task

(Makeig et al., 2000) showed increasing PSD in the

lower theta range (3-4 Hz) during periods of poor

performance. But, other studies stated a broad-band

increase of PSD in the theta-alpha-range and Lal &

Craig (Lal and Craig, 2002) found significant

increases of PSD in the delta-theta-alpha-beta-range

by factors of 22%, 26%, 9%, 5%, respectively.

Another variable which has the potential to have

a relatively close relationship to the sleep/wake sys-

tem is the pupil size. Experiments to get normative

values of the pupil unrest index including 349 sub-

jects at the age between 20 and 60 years resulted in

significant variations depending on sleepiness

(Wilhelm, et al., 2001). Pupillograms can be

measured contactless by camera based eyetracking

systems (ETS). This measure is additionally

dependent on several other influences, like e.g.

ambient illumination. Therefore, it is like EEG and

EOG problematic as a basic indicator for vigilance

monitoring devices on real roads. Varying ambient

illuminations do not appear in laboratories.

Despite the above mentioned difficulties in sear-

ching for unique signs of extreme fatigue, the analy-

sis of brain electric and of oculomotoric signals are

DATA REDUCTION OR DATA FUSION IN BISOGINAL PROCESSING?

441

accepted as most favourable for detections of sudden

performance deteriorations on a second-by-second

basis. It is unlikely that biosignals, like e.g. electro-

cardiogram, electromyogram, electro-dermal activi-

ty, or indirect measures of driver fatigue like driving

parameters, e.g. variability of lane deviation and of

steering angle, contain such information which im-

mediately reflect ongoing MSE.

3 EXPERIMENTS

During the week preceding the study subjects had to

keep a sleep diary to assess sleep habits. In addition,

subjects had to carry a wrist actometer during the

three days and nights preceding the experiments.

Actograms were checked immediately after arrival

of the subject to the experimental night, normally at

11 pm. If total sleep length (6 … 10 hrs), time-since-

sleep (14 … 16 hrs) and if the subject accomplished

the demand of no nap, then a permit for experiments

was given. Three days before the experimental night

subjects were familiarized with the lab equipment

and had to drive on a 20 min training course in the

driving simulator. Two subjects complained about

simulator sickness and were excluded from further

investigations. During the experimental nights one

further subject has quitted because of simulator sick-

ness and one because of back pain. In total twenty-

two healthy subjects (21 male, 1 female; mean age

24.4 ± 3.1 years, range 19-28 years) finished experi-

ments completely. All subjects gave written infor-

med consent and gave a written declaration on their

transfer home after experiments. Only driving as

passenger or, in case of campus residents, walking

was allowed.

Figure 1: Lab layout: dark simulator room (grey) with a

real car; operator room (light grey).

Experimental investigations were conducted in

our driving simulation lab consisting of an operator

room and a fully dark, temperature controlled simu-

lator room (Fig. 1). Subjects had to drive a real car

(GM Opel “Corsa”) on a slightly winding road under

conditions of night vision. No oncoming traffic was

simulated in order to maintain high level of monoto-

ny. The driving scene was projected on a projection

plane 2.6 m in front of the subject; maximum visual

angle is 56 deg. In case of complete road departures

a force feedback to the steering wheel was switched

on. This was in nearly all cases effective enough to

waken drowsy subjects.

For monitoring subjects behaviour three video

cameras were utilized: (1) of subjects left eye region,

(2) of her / his head and upper part of the body, and

(3) of driving scene. Video recordings were used for

online and offline scoring as explained later. Logged

variables of the driving simulator were lane devia-

tion, velocity, steering angle, and pedal movements;

sampling rate was 10 s

-1

. Furthermore, several elec-

trophysiological signals were derived. Seven EEG

channels (C3,

Cz,

C4,

O1,

O2,

A1,

A2, common ave-

rage reference), two of EOG (vertical, horizontal),

one of ECG, and one of EMG (musculus submenta-

lis) were sampled at a rate of 128 s

-1

. Further 6 sig-

nals were recorded by a binocular eye tracking sys-

tem (ETS) at a rate of 250 s

-1

. For each eye the pupil

size

and

the two coordinates of eye gaze on the plane

of projection were measured.

Figure 2: Operating schedule of one experimental night.

Subjects had to complete seven driving sessions. In each

session subjects drove in the simulator and attended three

vigilance tasks (VT 1 - 3), and two questionnaires (VAS =

Visual Analogue Scale, ADACL = Thayer Activation-De-

activation Adjective Checklist).

In all, subjects had to complete seven driving

sessions lasting 35 min, each preceded and followed

by vigilance tests and responding to sleepiness ques-

tionnaires (Figure 2) which are not considered in this

paper. Before starting the next driving session a 10

min long break was inserted for subjects needs and

in order to motivate the subject to continue driving

with best possible performance. Driving started at

1:00 a.m. after a day of normal activity and a time

since sleep of at least 16 hours.

On the one hand, our design has the disadvantage

of non-continuous driving due to questionnaires,

vigilance tests and breaks. But on the other hand a

large total time-on-duty is gained and a time-of-day

effect due to passing the circadian trough can be

observed. We experienced earlier that it is hard to

BIOSIGNALS 2009 - International Conference on Bio-inspired Systems and Signal Processing

442

motivate a subject for continuous driving in a simu-

lator for longer than two or three hours; most of

them are willing to give up when the first MSE arise.

We believe that our design results in much more

examples of MSE than in continuous driving of

equivalent total length (4 h).

Driving tasks were chosen intentionally monoto-

nous and with time-since-sleep up to 24 hours to

support drowsiness and occurrence of MSE. MSE

were detected online by two operators who observed

the subject utilizing three video camera streams as

aforementioned. Typical signs of MSE are e.g. pro-

longed eyelid closures, roving eye movements, head

noddings, major driving incidents and drift-out-of-

lane accidents. We have found 3,573 MSE (per sub-

ject: mean number 162 ± 91, range 11 - 399).

The step of online scoring is critical, because

there are no unique signs of MSE, and their exact

beginning is sometimes hardly to define. Therefore,

all events were checked offline by an independent

expert and

were corrected if necessary. Unclear MSE

characterized by e.g. short phases with extremely

small eyelid gap, inertia of eyelid opening or slow

head down movements were excluded from further

analysis. Non-MSEs were selected at all times out-

side of clear and of unclear MSE. We have picked

out the same amount of Non-MSE as of MSE in or-

der to have a balanced data set. Our intention was to

design a detection system for clear MSE versus clear

Non-MSE classification. We hypothesize that such a

system can not only detect the MSE recognized by

human experts. They should also offer a possibility

to detect unclear MSE cases which are not easily

recognizable by experts. In another paper we report

on first positive results to this hypothesis (Sommer

et al., 2008).

4 DATA ANALYSIS

Pre-processing, feature extraction, classification and

validation are typically the main steps of discrimi-

nant analysis. Three main steps of pre-processing

were performed: signal segmentation, artefact remo-

val and missing data substitution. Segmentation of

all signals was done with respect to the observed

temporal starting points of MSE or Non-MSE using

two free parameters, the segment length and the tem-

poral offset between first sample point of the seg-

ment and starting point of the event. The first para-

meter adjusts the trade-off between temporal and

spectral resolution whereas the second parameter

controls the location of the region-of-interest on the

time axis. Both parameters are of high importance

and were found to be optimal when offset is -3 sec

and segment length is 8 sec (Golz et al., 2007). This

means that classification is working best when

biosignals from 3 sec immediately before MSE to 5

sec after MSE onset are analyzed. Artefact detection

in EEG and missing data in ETS during every eyelid

closure were both of minor importance (Golz et al.,

2007).

We utilized the common periodogram and the re-

cently introduced method of Delay Vector Variance

(DVV) (Lal and Craig, 2002) as feature extraction

tools. The first method assumes stationary signals

and their generating system is linear. It is a direct

method to estimate logarithmic PSD. DVV

transforms the signal to the state space utilizing time

delay embedding. Provided that distinct conditions

are fulfilled, e. g. if the signal generating system can

be described by relatively simple coupled ordinary

differential equations, this has the advantage that

signals exhibiting some degree of irregularity in the

time domain are mapped on relatively simple

trajectories in the state space. Simple statistical tests

in the state space can then be utilized to estimate to

which degree the signal may be generated by a

nonlinear system and to estimate how large may be

the amount of stochasticity in the signal. Both

features are important and are dependent on one free

parameter which controls the degree of similarity in

the sate space. Therefore, two feature sets are

generated by DVV. They may vary over time if the

signal generating process alters as it might by when

a MSE is oncoming.

After completion of pre-processing and feature

extraction the stage of classification analysis is up

next. It turned out that Support Vector Machines

(SVM) outperform several other methods (Golz et

al., 2007). It is a stochastic learning method and is

adapting discriminant functions in order to gain high

adaptivity and also high generalizability. In order to

gain this several internal parameters are to be

optimized which is computational time consuming

(Golz et al., 2007). For comparison we utilized also

a winner-takes-all neural network, namely the

OLVQ1 algorithm. It demands much less

computational effort and is a good choice of effici-

ency when many parameters of pre-processing and

feature extraction are to be optimized empirically.

Next, validation is performed in order to estimate

the true error of classification. The expectation value

of the

classification

error

based on the training data is

known to be biased. This so-called training set error

is a useful

measure

to check how good the adaptation

of the discriminant function has been working. Seve-

ral cross validation methods have been developed in

order to get a second

measure, the test

error. Here we

DATA REDUCTION OR DATA FUSION IN BISOGINAL PROCESSING?

443

have used the “leave-one-out” cross validation, be-

cause it provides an almost unbiased estimation of

the true classification error, but it is computationally

expensive. Advantageously, in case of SVM an effi-

cient implementation is possible due to the support-

vector concept.

Figure 3: Mean and standard deviation of test set errors for

single signals and several examples of feature fusion.

5 RESULTS

Mean and standard deviation of test errors (Figure 3)

of different feature sets extracted from only one bio-

signal were estimated. The PSD feature set resulted

in lower errors than the DVV feature set (white bars

are always higher than grey bars). DVV shows good

potential in exploring the horizontal EOG, which is

due to eye blinks far from quasi-stationarity. This is

required for PSD estimation but not for DVV. The

fusion of both PSD and DVV features performed al-

ways better than PSD features alone (black bars are

always lowest). The vertical EOG component turned

out as most successful for microsleep detection, but

error rates are around 20 %.

The fusion of features of different signals always

reduces errors (six right most groups of bars in Figure

Figure 4: Mean and standard deviation of test set errors for

several examples of feature fusion.

4). The fusion of the best single channels (EOG ver-

tical and EEG Cz) performs better than the fusion of

all EEG signals, or of all ETS, or of both EOG sig-

nals. But this is clearly outperformed by fusion of all

EEG and all EOG features, or of all EEG + EOG +

ETS features (two right most groups of bars in Fig.

4). The latter resulted in mean test errors lower than

10 %. A comparison of more classification methods

and a report of some more details on discriminant

analysis, their parameters and their computational

costs can be found elsewhere (Golz et al., 2007).

Different methods of feature reduction were app-

lied to all nine EEG

and EOG

signals (Table

1). First,

no reduction was aimed to have a baseline result. So,

513 features per channel were processed. SVM (E

Test

= 13.1 %) performs much better than OLVQ1 (E

Test

27.7

%). Next, PCA (principal

component analysis)

was utilized to reduce the number of features down

to 60 for OLVQ1 and 128 for SVM. This was found

as an empirical optimum with minimal test errors

(OLVQ1: 17.4 %; SVM: 10.9 %). The third method

was the commonly used summation in four spectral

bands (delta, theta, alpha, beta), which leads to total

number of features of N

= 4 features / signal x 9

signals = 36 features. It clearly came out that this

reduction is too much. Next, summation in small,

equidistant spectral bands was performed, whereby

frequency range and width of the bands were deter-

mined empirically. We found a range of 0.5 to 23.5

Hz and a width of 1 Hz optimal, i.e. 24 features per

signal. The fifth method was a summation in bands

whereby ranges where determined by utilizing Evo-

lutionary Strategies (ES). The number of features

per channel was preset to 10. Further details can be

found elsewhere (Golz et al., 2007), (Sommer and

Golz, 2007). Results show that feature reduction

leads to more than 3 % of error reduction which can

be gained by simple averaging in small spectral

bands or by ES optimization. The common method

of reduction to the delta, theta, alpha, and beta band

is as bad as no reduction.

Table 1: Results of 5 different feature reductions applied

to EEG and EOG. Test set errors (E

Test

) were estimated by

Multiple Hold-Out and by Leave-One-Out cross validation

utilizing OLVQ1 and SVM, respectively. The number of

features (N

) varies largely between cases.

Case N

OLVQ1 SVM

TEST

[%] E

TEST

[%]

(1) No reduction 4617. 27.7 ± 0.6 13.1 ± 0.3

(2) PCA 60 / 128. 17.4 ± 0.4 10.9 ± 0.2

(3) fixed band 36. 17.5 ± 0.4 13.2 ± 0.3

(4) equidistant bands 216. 15.7 ± 0.4 9.9 ± 0.1

(5) ES-OLVQ1 90. 14.1 ± 0.4 9.8 ± 0.1

BIOSIGNALS 2009 - International Conference on Bio-inspired Systems and Signal Processing

444

6 CONCLUSIONS

It has been shown that fusion of features has poten-

tial to improve detection accuracy of driver’s micro-

sleep. Features of two different extraction methods,

namely the Power Spectral Density (PSD) and the

Delay Vector Variance (DVV), were fused first, but

with a limited success. Fusion of different signals of

one signal type, such as all EEG signals, as well as

fusion of different signal types, namely EEG, EOG,

ETS, resulted in clear improvements. The best single

EEG signal (Cz) gained a mean error of 25 %. The

fusion of all 7 EEG signals reduced errors down to

16 %, and the fusion of all 15 signals available redu-

ces errors down to 9 %.

In high-dimensional spaces it is apparently intra-

ctable to search systematically and to approximate a

general, high-dimensional function accurately. This

is known as the so-called “curse of high dimensiona-

lity”. But, Support-Vector Machines and also other

modern methods of computational intelligence, but

not OLVQ1, impressively demonstrated that high di-

mensionality must not be a curse. OLVQ1 perfor-

mance decreased largely when the number of input

variables (features) was very high. Our results also

showed that fusion of features of all signals is most

beneficial.

Reduction is of limited advantage and was only

successful for highly correlating features, e.g. sum-

mation of PSD values in small spectral bands. There

is presumably no potential for further improvements

due to feature reduction. This was demonstrated by

computational expansive optimizations of the para-

meters of spectral bands utilizing Evolutionary Stra-

tegies. Note that these optimizations are capable to

search for different spectral bands for each subject,

if it would be advantageously.

Future work should reveal if a further diversifi-

cation of feature extraction may increase performan-

ce of discriminant analysis. Different types of featu-

res should then be fused which is likely to improve

accuracy and robustness of MSE detection.

On the one hand the detection of driver’s micro-

sleep is a relatively clear case illustration for the

problem of spontaneous behavioural events and their

detection. On the other hand, their detection in bio-

signals will be a necessary milestone for future on-

line driver monitoring technology. It explores the

extreme end of driver’s fatigue where it is essential

to avoid attention losses. The practical goal of such a

detection system is to establish a laboratory referen-

ce standard for detection of microsleep and extreme

hypovigilance. Contactless operating online driver

monitoring technology, which is currently under

development by car industry, must be validated uti-

lizing such a laboratory reference standard.

REFERENCES

Donoho, D. (2000). High-Dimensional Data Analysis: The

curses and blessing of dimensionality. Ann Conf Amer

Math Soc, Los Angeles. (http://www-stat.stan-

ford.edu/~donoho/Lectures)

Overview at http://www.microsleep.de

Schleicher, R., N. Galley, S. Briest, L. Galley (2007).

Blinks and Saccades as Indicators of Fatigue in

Sleepiness Warnings: Looking Tired? Ergonomics,

vol. 51, pp. 982-1010.

Summala, H., H. Häkkänen, T. Mikkola, J. Sinkkonen

(1999). Task effects on Fatigue Symptoms in Over-

night Driving. Ergonomics, vol. 42, pp. 798-806.

Ingre, M., T. Akerstedt, B. Peters, A. Anund, G. Kecklund

(2006). Subjective Sleepiness, Simulated Driving

Performance and Blink Duration. J Sleep Research,

vol. 15, pp. 47-53.

Santamaria, J., K.H. Chiappa (1987). The EEG of

Drowsiness in Normal Adults, J Clin Neurophysiol,

vol. 4, pp. 327-382.

Maulsby, R.L. et al (1968). The Normative Electro-

encephalographic Data Reference Library, Final

Report, Contract NASA 9-1200, National Aeronautics

and Space Administration.

Akerstedt, T., G. Kecklund, A. Knutsson (1991). Manifest

Sleepiness and the Spectral Content of the EEG

During Shift Work, Sleep, vol. 14, pp. 221-225.

Makeig, S., T. P. Jung (1995). Changes in Alertness are a

Principal Component of Variance in the EEG

Spectrum, Neuroreport, vol. 7, pp. 213-216.

Makeig, S., T.P. Jung, T. Sejnowski (2000). Awareness

During Drowsiness: Dynamics and Electrophy-

siological Correlates, Can J Exp Psychol, vol. 54, pp.

266-273.

Lal, S., A. Craig (2002). Driver Fatigue: Electroence-

phalography and Psychological Assessment, Psycho-

physiology, vol. 39, pp. 313-321.

Wilhelm, B. et al (2001). Daytime Variations in Central

Nervous System Activation Measured by a

Pupillographic Sleepiness Test, J Sleep Res, vol.10,

pp.1-7.

Sommer, D., M. Golz, J. Krajewski (2008). Consecutive

Detection of Extreme Central Fatigue. Proc 4

Europ

Congr Med & Biomed Engineering (EMBEC-2008),

to appear.

Golz, M., D. Sommer, M. Chen, U. Trutschel, D. Mandic

(2007). Feature Fusion for the Detection of

Microsleep Events. J VLSI Signal Proc Syst, vol. 49,

pp. 329-342, 2007.

Sommer, D., M. Golz (2007). Feature Reduction for

Microsleep Detection. In IWK’07, 52nd Int. Sci. Koll.,

Technical Univ. of Ilmenau, Germany, Vol I, 77-82.

DATA REDUCTION OR DATA FUSION IN BISOGINAL PROCESSING?

445