3.1.3 Digitization
Any localization device that could acquire the locations of electrodes was able to be used in our experiment. Our study used a Polhemus FastTrak that contains two receivers and one transmitter. (See Figure 3.2) Two receivers include a stylus which points to the desired locations for collection and three receivers which are fixed to the head of subject to endure slight displacements of the head without producing digitization errors. The subject sits before the fixed transmitter which orientation with X pointing up and Y pointing to the right.
Figure 3.2: Polhemus FastTrak.
3.1.4 Experimental environment
We setup a pair of studio lamp beside the subject because of getting uniform color images and reducing the effect of the shadow. The Kinect for windows mounted in front of the subject and toward the subject, and subject near Kinect for windows as close as possible. The proposed system is implemented in C/C++ language, compiled by Microsoft Visual Studio 2010 compiler. OpenCV [3], OpenNI [4] and PCL [5] library are also applied in our system.
3.1.5 Blue marker
Before the experiment started, the subject would be pasted some markers that used to find the relation of transformation between Kinect and digitizer coordinate systems. In order to extract the markers conveniently, we choose a specially crafted marker that was 11 mm blue round, white outer ring and white cross line in the central. There were two reasons for choosing this design of makers: (i) we wanted the color be great different to
Figure 3.3: Blue marker.
3.2 Results
Our study used three error estimations to evaluate the accuracy of co-registration, and we calculated them in root mean square (R.M.S) form and arithmetic mean form respec-tively. Figure 3.4 shows the Kinect-derived facial points and MRI-derived facial points in the same coordinate system before co-registration, and Figure 3.5 displays the result of co-registration of Kinect-derived facial points and MRI-derived facial points in the form of point cloud. Then Figure 3.6 and 3.7(a)-(i) shows example of Kinect-derived facial points superimposed on the different slices after co-registration. In this example, we could ob-serve some noise and outlier that extracted by Kinect for windows floating in the air. Table 3.1 shows the result of the residual error of the face. The mean of residual error of the face was equal to 0.89 mm ± 0.09 mm (Mean ± SD). Table 3.2 shows our method compared with other methods made before. It demonstrates that our method has better results by
3.2 Results 35
using facial points to do registration.
Table 3.1: The results of the residual error of the face. (unit: mm) R.M.S Mean Std. dev. intra-subject
The results of the residual error of the sensor were listed in Table 3.3. The mean of the residual error of the sensor was equal to 1.67 mm ± 0.46 mm (Mean ± SD). The max value of the residual error of the sensor by different subjects were about 2.67 - 4.76 mm due to the hair that cause the digitized points floating on the head and the sized difference between the EEG cap and the head. In addition, The thickness compensation also affected the residual error of the sensor. On the other hand, we compared with Koessler et al. [11]
(a)
(b)
Figure 3.4: The Kinect-derived facial points and MRI-derived facial points (red points).
There are put in the same coordinate system. (a) and (b) are two different perspectives.
3.2 Results 37
(a)
(b)
Figure 3.5: The result of co-registration of Kinect-derived facial points and MRI-derived facial points. (a) Before co-registration. The centroids of Kinect-derived facial points (white points) and MRI-derived facial points (red points) were matched together by trans-lation. There are different perspectives. (b) After co-registration. The Kinect-derived facial points(white points) superimposed on the MRI-derived facial points (red points).
(a)
(b) (c)
Figure 3.6: Example of the Kinect-derived facial points superimposed on MRI after co-registration. The red points are the Kinect-derived facial points. (a) The view from the right side. (b) and (c) are one of slices in vertical and horizontal tangent direction.
3.2 Results 39
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 3.7: Example of Kinect-derived facial points on the MR slice. The red points are Kinect-derived facial points. (a) to (h) are different slices in vertical tangent direction.
Min = 0.94 Max = 1.19
that listed in Table 3.4, and our method had smaller errors. Figure 3.8 shows the example of digitized sensor points co-registered with MRI head surface, and Figure 3.8(c) can see the four makers on the face. Moreover, Figure 3.8(d) shows the sensors and the name of sensors, and Figure 3.9 shows example of digitized sensor locations on the MR slices.
By residual error of the cross, we could evaluate our system that use Kinect for windows as interface whether produce the great transformation errors. The results of the residual error of the cross were listed in Table 3.5. The mean of the residual error of the cross was equal to 1.83 mm ± 0.43 mm (Mean ± SD), and in R.M.S form was equal to 2.29 mm ± 0.48 mm. Then Figure 3.10 shows example of digitized cross locations on the head surface, and Figure 3.11 shows the example of digitized cross locations on the different MRI slices.
From ten subjects, we could observe that s9 and s10 have relative larger errors that because of our EEG cap only having medium and big size, the head of subjects s9 and subjects s10 were not fit the medium or big sized EEG cap. Simultaneously, the hair of subjects would influence the locations of digitized sensors. The hair of subject s5 was short and the head was medium size, so the results were relatively small.
3.2 Results 41
Table 3.3: The results of the residual error of the sensor. (unit: mm) R.M.S Mean Std. dev. Max Min intra-subject
S1 2.00 1.48 1.34 4.63 0.08 0.025
1.76 1.43 1.01 3.84 0.21
S2 2.00 1.14 1.64 5.84 0.05 0.21
2.31 1.56 2.15 4.87 0.21
S3 1.57 1.27 0.86 3.01 0.17 0.025
1.68 1.32 1.12 2.67 0.13
S4 2.11 1.93 0.86 3.87 0.11 0.105
2.18 1.72 2.31 4.76 0.88
S5 2.56 0.92 2.39 3.43 0.10 0.065
2.17 1.05 3.14 4.33 0.17
S6 2.4 1.97 3.41 2.43 0.03 0.03
2.07 1.91 1.74 3.33 0.12
S7 3.03 2.54 1.82 4.49 0.28 0.77
1.57 1.00 1.21 3.23 0.12
S8 2.78 1.93 1.34 3.49 0.15 0.205
1.83 1.52 1.44 3.23 0.21
S9 3.2 2.34 2.35 3.49 0.16 0.165
3.17 2.01 2.27 3.34 0.15
S10 3.47 2.31 3.51 5.47 0.35 0.075
3.76 2.16 3.64 5.58 0.12
(a) (b)
(c) (d)
Figure 3.8: Example of digitized sensor points co-registered with MRI head surface. The red points are the locations of EEG sensors. (a) The right side of head surface. (b) The left side of head surface. (c) The front side of head surface. There are four markers on the face.
(d)Looking down form height. The name of sensors were labelled.
3.2 Results 43
(a)
(b)
Figure 3.9: Example of digitized sensor locations on the MR slice. The red points are the locations of EEG sensors.
R.M.S Max = 3.76 Max = 3.49 Min = 1.57 Min = 1.80
(a) (b)
Figure 3.10: Example of digitized cross locations on the head surface. The red points are the digitized cross points. (a) The right side of head surface. (b) The left side of head surface.
3.2 Results 45
Table 3.5: The results of the residual error of the cross. (unit: mm) Points R.M.S Mean Std. dev. intra-subject
S1 192 2.2 1.57 1.64 0.04
S4 134 2.34 2.31 2.17 0.035
127 1.83 1.62 1.74
S5 162 1.54 1.15 1.01 0.345
173 2.07 1.32 1.59
S6 158 2.33 1.83 2.17 0.165
127 1.65 1.38 0.90
S7 181 1.78 1.47 1.36 0.225
170 2.56 2.09 1.48
S8 212 2.42 1.97 1.41 0.478
179 2.07 1.34 1.64
S9 164 2.48 2.2 1.61 0.01
178 2.79 2.22 1.69
S10 157 3.07 2.78 1.64 0.16
161 3.24 2.46 1.58
(a) (b)
(c)
Figure 3.11: Example of digitized cross locations on the MRI slices. The red points are the digitized cross points. (a), (b) and (c) are digitized cross locations on different MR slices.
Chapter 4
Discussions
for windows was equal to 0.89 mm ± 0.03 mm (mean ± SD), and using Kinect was equal to 1.24 mm ± 0.07 mm. This experiment showed under the same subjects, using Kinect for windows to extract the RGB-D data to do registration had better results. The results of using Kinect to do registration can refer to Table 4.2.
Table 4.1: Comparison of Kinect and Kinect for windows. (unit: mm) Kinect for windows Kinect
0.89 ± 0.03 1.24 ± 0.07
Mean Max = 0.95 Max = 1.31
Min = 0.83 Min = 1.13
Table 4.2: The results of using Kinect to do registration. (unit: mm) Case 1 Case 2 Case 3 Case 4
Mean 1.23 1.31 1.3 1.13
Standard deviation 0.61 0.67 0.63 0.67
Maximum 3.04 3.3 2.97 3.41