In this study, we investigated the continuous EEG fluctuations from alertness to drowsiness in a realistic VR driving environment. Several component clusters exhibited monotonic alpha-band (8-12 Hz) power increase during the transition from alertness to very-slight and slight drowsiness, but remain constant or slight decrease during the extreme drowsiness period. On the other hand, the theta-band (4-7 Hz) power for each component cluster increased monotonically during the transition from slight to extreme drowsiness. Hence, these controversial results may be in part caused by the different drowsiness levels of volunteers.
Additionally, drowsy related alpha and theta rhythm in these component clusters maybe modulated by the same nucleus. Lastly, we compared the EEG between different component clusters diversity of EEG power changes with respect to the transition from alertness to drowsiness and found that alpha power of BLO and OM component were most stable and desirable EEG feature for very-slight and slight drowsiness detection. The theta power of BLO and OM component were the most stable and desirable EEG feature for slight and extreme drowsiness detection.
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Table I. The number of subjects for each cluster
BLO OM FCM CM CPM LCP RCP
n / N 13/16 8/16 9/16 8/16 9/16 7/16 7/16 BLO OM FCM CM CPM LCP RCP
n / N 13/16 8/16 9/16 8/16 9/16 7/16 7/16
N represented the number of total subjects and n represented the number of subjects for each cluster.
Table II. Summary of the component index of subjects in each cluster
Table III. The peak frequency of grand mean baseline power spectra
Table IV. The percentage of subjects with high correlation between powers in time series (correlation coefficient > 0.6) of the different components
RCP
The alpha power had high correlation between BLO, OM, CPM, LCP and RCP component and the theta power had high correlation between BLO, OM, CM, CPM, LCP and RCP components for the great part of subjects.
Table V. The R-square values between grand mean of EEG power fluctuations and first-order linear regression line
Very slight drowsiness Slight drowsiness Extreme drowsiness
0.69
Very slight drowsiness Slight drowsiness Extreme drowsiness
0.69
Very slight drowsiness Slight drowsiness Extreme drowsiness
Table VI. The slop of first-order linear regression line
Very slight drowsiness Slight drowsiness Extreme drowsiness
0.06
Very slight drowsiness Slight drowsiness Extreme drowsiness
0.06
Very slight drowsiness Slight drowsiness Extreme drowsiness
Table VII. The mean value of standard deviation
Very slight drowsiness Slight drowsiness Extreme drowsiness
0.24
Very slight drowsiness Slight drowsiness Extreme drowsiness
0.24
Very slight drowsiness Slight drowsiness Extreme drowsiness
Fig. 2-1: The block diagram of the VR-based driving simulation environment with the EEG-based physiological measurement system.
Fig. 2-2: The VR-based four-lane highway scenes are projected into 360° surround screen with seven projectors.
Fig. 2-3: The 32 channel EEG cap.
Fig. 2-4: The International 10-20 system of electrode placement. (A) The lateral view, (B) The top view [22].
Deviation Onset
Reaction Time Cruising
(Trajectory)
Deviation
Response Offset
Response Onset
Cruising (Trajectory) Res pon se
Time Deviation
Onset
Reaction Time Cruising
(Trajectory)
Deviation
Response Offset
Response Onset
Cruising (Trajectory) Res pon se
Time
Fig. 2-5: An example of the deviation event. The car cruised with a fixed velocity of 100 km/hr on the VR-based highway scene and it was randomly drifted either to the left or to the right away from the cruising position with a constant velocity. The subjects were instructed to steer the vehicle back to the center of the cruising lane as quickly as possible.
Fig. 2-6: The digitized highway scene. The width of highway is equally divided into 256 units and the width of the car is 32 units.
Fig. 2-7: An example of the driving performance that represented by the digitized vehicle deviation trajectories.
0 30 60 90 120 150 250
200
150
100
50
0
Time (sec)
Driving Performance
0 30 60 90 120 150 250
200
150
100
50
0
Time (sec)
Driving Performance
Fig. 3-1: The utilized EEG signals processing procedure.
Fig. 3-2: An example of the scalp topographies of ICA weighting matrix W by spreading each w into the plane of the scalp corresponding to the ij j ICA components based on th International 10-20 system.
1s Amplitude (dB)Amplitude (dB)Amplitude (dB)Amplitude (dB)
IC Activations Scalp Map Power
Spectrum
Amplitude (dB)Amplitude (dB)Amplitude (dB)Amplitude (dB)IC Activations Scalp Map Power
Spectrum
Fig. 3-3: Time course signals, scalp maps and power spectra of some typical independent components representing different types of artifacts and EEG sources. (A) The eye blink component. (B) The horizontal eye movement component. (C) The temporal muscle component. (D) The channel noise component. (E) The parietal EEG source
Median
Fig. 3-4: The smoothed EEG power spectral analysis procedure. The EEG data of the extracted ICA components was first accomplished using a 750-point Hanning window with 250-point overlap. Windowed 750-point epochs were further subdivided into several 125-point subwindows using the Hanning window again with 25-point step. Each 125-point frame was extended to 256 points by zero-padding to calculate its power spectrum by using a 256-point fast Fourier transform (FFT),,resulting in power-spectrum density estimation with a frequency resolution near 1 Hz.
0 10 20 30 40
Fig. 3-5: Local driving error.
Subject 1
Fig. 3-6: Component clustering analysis. The components of all volunteer were clustered semi-automatically based on the gradients values, [Gxi Gyi], of the component scalp maps. K-mean algorithm was utilized for clustering.
Data point
A lpha P o w e r (dB )
Time (s)
Lo cal D riv in g E rro r Local D ri v ing E rror Al p h a Po w e r ( d B)
Step 1
Step 2
Step 3 Baseline removal
Sorting the Error
Smoothing
Original LDE trajectory Sorts the LDE
Data point
A lpha P o w e r (dB )
Time (s)
Lo cal D riv in g E rro r Local D ri v ing E rror Al p h a Po w e r ( d B)
Step 1
Step 2
Step 3 Baseline removal
Sorting the Error
Smoothing
Data point
A lpha P o w e r (dB )
Time (s)
Lo cal D riv in g E rro r Local D ri v ing E rror Al p h a Po w e r ( d B)
Step 1
Step 2
Step 3 Baseline removal
Sorting the Error
Smoothing
Original LDE trajectory Sorts the LDE
Fig. 3-7: An example of the sorted spectral analysis. The left subplot of Fig 3-6 is a subject’s original LDE trajectory (the blue line) and the corresponding alpha power changes (the red line). The right subplot sorts the LDE values in ascending order and shows the transient alpha powers corresponding to the sorted LDE values. It can be found that the alpha power is increasing at the beginning and will decrease at the latter when LDE values are ascending.
Fig. 4-1: Equivalent dipole source locations and scalp maps for Bi-Lateral Occipital (BLO, the left column), Occipital-Midline (OM, the middle column), and Frontal-Central-Midline (FCM, the right column) independent component clusters. (Upper panels) 3-D dipole source locations (colored spheres) and their projections onto an average brain image. Dipole spheres of different volunteers are represented by different colors. (Lower panels) Scalp maps of the clustered components. The label above each scalp map represents the index of the volunteer and the component index of the volunteer.
Fig. 4-2: Equivalent dipole source locations and scalp maps for Central-Midline (CM, the left column), Central-Parietal-Midline (CPM, the middle left column), Left-Central-Parietal (LCP, the (middle right column) and Right-Central-Parietal (RCP, the right column) independent component clusters. (Upper panels) 3-D dipole source locations (colored spheres) and their projections onto an average brain image. Dipole spheres of different volunteers are represented by different colors. (Lower panels) Scalp maps of the clustered components. The label above each scalp map represents the index of the volunteer and the component index of the volunteer.
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Local Driving Error
Frequency (Hz) Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Local Driving Error Frequency (Hz)
Fig. 4-3: Activations of the Bi-Lateral Occipital (BLO) cluster. (A) The grand mean of the scalp map and the baseline power spectral. (B) The grand mean log power spectral density changes accompanying with the sorted local driving error (LDE) in ascending order. (C, D) show the transient alpha and theta powers corresponding to the ascending LDE values, respectively. (A, C, D) the solid lines represent the grand mean power spectra and the dotted lines represent the variance of the power spectra. These notifications will be utilized in the illustrations of the other component clusters. The peak frequency of the baseline power spectral is near 10 Hz for BLO component cluster. When the LDE values increase from 0 to 40, the alpha power (8~12Hz) has increasing the sustaining activations. The theta power (4~7Hz) increases monotonically from low LDE to high LDE.
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Fig. 4-4: Activations of the Frontal Central Midline (FCM) cluster. (A) The grand mean of the scalp map and the baseline power spectral. (B) The grand mean log power spectral density changes accompanying with the sorted local driving error (LDE) in ascending order. (C, D) show the transient alpha and theta powers corresponding to the ascending LDE values, respectively. (A, C, D) the solid lines represent the grand mean power spectra and the dotted lines represent the variance of the power spectra. The peak frequency of the baseline power spectral baseline is near 5 Hz for FCM component cluster. The powers of the alpha band and the theta band increase monotonically from low LDE to high LDE.
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Fig. 4-5: Activations of the Central Midline (CM) cluster. (A) The grand mean of the scalp map and the baseline power spectral. (B) The grand mean log power spectral density changes accompanying with the sorted local driving error (LDE) in ascending order. (C, D) show the transient alpha and theta powers corresponding to the ascending LDE values, respectively. (A, C, D) the solid lines represent the grand mean power spectra and the dotted lines represent the variance of the power spectra. The peak frequency of the baseline power spectral is near 7 Hz for CM component cluster. Similar to the BLO cluster, the LDE values increase from 0 to 40, the alpha power (8~12Hz) has increasing the sustaining activations. The theta power (4~7Hz) increases monotonically from low LDE to high LDE.
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Fig. 4-6: Activations of the Central Parietal Midline (CPM) cluster. (A) The grand mean of the scalp map and the baseline power spectral. (B) The grand mean log power spectral density changes accompanying with the sorted local driving error (LDE) in ascending order. (C, D) show the transient alpha and theta powers corresponding to the ascending LDE values, respectively. (A, C, D) the solid lines represent the grand mean power spectra and the dotted lines represent the variance of the power spectra. The peak frequency of the baseline power spectral is near 10 Hz for CPM component cluster. Similar to the BLO cluster, the LDE values increase from 0 to 40, the alpha power (8~12Hz) has increasing the sustaining activations. The theta power (4~7Hz) increases monotonically from low LDE to high LDE.
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Fig. 4-7: Activations of the Left Central Parietal (LCP) cluster. (A) The grand mean of the scalp map and the baseline power spectral. (B) The grand mean log power spectral density changes accompanying with the sorted local driving error (LDE) in ascending order. (C, D) show the transient alpha and theta powers corresponding to the ascending LDE values, respectively. (A, C, D) the solid lines represent the grand mean power spectra and the dotted lines represent the variance of the power spectra. The peak frequency of the baseline power spectral is near 10 Hz for LCP component cluster. Similar to the BLO cluster, the LDE values increase from 0 to 40, the alpha power (8~12Hz) has increasing the sustaining activations.
The theta power (4~7Hz) increases monotonically from low LDE to high LDE.
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Frequency (Hz)
Power (dB)
LDE
Alpha Power (dB) Theta Power (dB)
Local Driving Error Local Driving Error
Power (dB)
(A) (B)
(C) Frequency (Hz) (D)
Power (dB)
Fig. 4-8: Activations of the Right Central Parietal (RCP) cluster. (A) The grand mean of the scalp map and the baseline power spectral. (B) The grand mean log power spectral density changes accompanying with the sorted local driving error (LDE) in ascending order. (C, D) show the transient alpha and theta powers corresponding to the ascending LDE values, respectively. (A, C, D) the solid lines represent the grand mean power spectra and the dotted lines represent the variance of the power spectra. The peak frequency of the baseline power spectral is near 10 Hz for RCP component cluster. Similar to the BLO cluster, the LDE values
Fig. 4-8: Activations of the Right Central Parietal (RCP) cluster. (A) The grand mean of the scalp map and the baseline power spectral. (B) The grand mean log power spectral density changes accompanying with the sorted local driving error (LDE) in ascending order. (C, D) show the transient alpha and theta powers corresponding to the ascending LDE values, respectively. (A, C, D) the solid lines represent the grand mean power spectra and the dotted lines represent the variance of the power spectra. The peak frequency of the baseline power spectral is near 10 Hz for RCP component cluster. Similar to the BLO cluster, the LDE values