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5.3 Results of the Proposed P300-based BCI System
Several experiments were performed for each subject using the proposed P300-based BCI with
the proposed three different evoking paradigms in different time schedules and for different time
periods and the experiments were evaluated using the metrics described in Chapter 3 in Equations
(24), (25), (26) and (27). The results of each headset position experiment are described and
discussed in the next sections.
5.3.1 Experiments Using Headset Default Position
As mentioned in the experiment setup section in the previous chapter, when the headset is placed
in its default position (see figure 4.11.a) only few electrodes lay in the centro-parietal region
where most of the P300 (more specific, P3b) components are produced. Mainly, O1 and O2
electrodes lay in this region. Consequently, the results of this experiment were promising, but the
accuracy was low and non-of the users could have high, or reasonably high true positive rate
compared to the experiment using the reversed position.
Subjects
S1
S2
TPR
26.13%
24.53%
FPR
42.77%
43.61%
bit rate
1.22
1.15
Accuracy
47%
45%
Table 5.2: System performance for each subject in the default headset mount
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5.3.2 Experiments Using Headset Reversed Position
Several experiments were performed for each subject in different time schedules and for different
time periods and the table below shows the best results were achieved for each subject using the
headset in its reversed position. Also, the figure below shows the best path the user could achieve
in one of the experiments. Numbers indicate the trial’s number at which the cubic passed through
the point.
Subjects
S1
S2
TPR
41.17%
42.11%
FPR
34.2%
33.89%
bit rate
4.61
4.88
Accuracy
58%
59%
Table 5.3: System performance for each subject in the reversed headset mount
Figure 5.9: Best achieved path
Final point
after 316
trials
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5.3.3 Discussions
The results shown in table 5.2 and figure 5.9 for the experiment of the system with the reversed
mount of the headset indicate that the overall performance of the proposed P300-based BCI
system is reasonable compared to the overall accuracy achieved by Ramírez-Cortes et al., [40],
where they achieved 60% of accuracy in the detection of only one non-persistent stimulus. The
users could move the object roughly not far from the specified path, but with reasonably high
false positive rate. None of the users could reach to the goal, but most of them reached to nearby
points.
For the two subjects, the true positive rate was fairly low and that means the algorithm
could detect the user’s intention, but each command needs several trials to be detected. This
decreases the speed of the system, but we still can have good accuracy if the false positive rate
was low. Unfortunately, the false positive rate was high compared to some studies as in Kanoh et
al., [67] where maximum of 29% was achieved. This high FPR leads to disrupting the user
intentions and finally, after several trials the user will be frustrated and may not be able to
continue the online control process. In other words, low TPR and very low FPR lead to slow and
accurate control process, but low TPR and high FPR lead to slow and inaccurate control process.
The results also showed that using arrows as stimuli in the paradigms gives better results
than using buttons. The above results were achieved using both paradigm-1.a and paradigm-2.
Regarding the ensemble technique, the results showed that when ensemble technique is
applied, where one classifier is assigned for each stimulus data, the accuracy was not improved.
Since only four stimuli are used and brain responses to all target stimuli are similar, one classifier
was able to distinguish between target and non-target data with the same accuracy as the
ensemble technique.
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The low accuracy of the proposed BCI system can probably be ascribed to different reasons
that some of them are listed below:
Electrodes Placement: in the reversed position, electrodes placement were only
approximate and since the headset was developed to be mounted in its default placement,
some of the electrodes leads were inflexible and during the experiment sometimes they
move away from the head surface and that could lead to poor or fair connections.
Additionally, placement of the reference and ground electrodes was changed; instead of
being behind the ears where the bones that have very low signals are, they became in
front of the ears which is considered as an active brain area for some other signals. This
makes signals from all electrodes grounded to a level more than zero and averaged with a
signal that may be important to P300 producing.
Number of Electrodes: in the reversed mount as shown in figure 4.11.b, roughly only
six electrodes of the headset are located within the P300 region and still none of them is
located in the best electrodes places described in the literature.
Signal to noise ratio: as mentioned before, the headset was developed for entertainment
and gaming purposes, although a special research edition as the one we used was
developed, and that means parts of the headset, including electrodes and amplifiers, may
not have the high quality as in the parts of the medical caps and that may lead to low
quality acquired signals which means low signal to noise ratio.
Aliasing Problem: each electrode’s signal was originally acquired at 2048Hz and then
filtered with a low-pass filter with 85Hz and a high-pass filter with 0.16Hz. Then the
signal is downsampled to 128Hz. According to Nyquist theorem, the anti-alias filter
should not have a cut-off frequency higher than the Nyquist frequency (64Hz) which is
half of the sampling rate. Here the 85Hz cut-off frequency leads to some aliasing as the
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developer even mentioned and that may cause some changes with the data, especially, for
long time periods (actual sampling rate 127.94Hz).
Latency Problem: we are only authorized to access the EEG raw data through the
Emotiv API and EmoEnginne libraries and accessing the data using these libraries still
have some delay which is roughly under 15 ms as the developer provided and that lead to
loosing data packets, maximum 1 out of 128 packet which is one sample per channel and
this may have some contribution in the low accuracy problem.
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CHAPTER 6
CONCLUSION AND FUTURE WORK
6.1 Summary
This thesis discusses two closely related aspects of research on BCI systems. The first aspect was
the review of the state-of-the-art of approaches and algorithms for P300-based BCI systems. The
thesis also discusses how to implement, validate, and compare these approaches with well known
P300 benchmark datasets. The second aspect is the development of an inexpensive P300-based
BCI system. This development uses commercial EEG sensors and then tests their feasibility in
simple control applications, such as cursor movement using different testing paradigms proposed
by different studies in the literature.
6.1.1 P300-based BCI systems up-to-date approaches and algorithms
BCI devices can allow people to communicate through nontraditional means. The current rate of
information transfer is slow compared to speaking, or writing. However, people who suffer from
neuromuscular disabilities now have an alternative method of communication. As with any new
technology, more research will refine the system, and performance will increase accordingly. In
addition, it will become clear as to which approaches and interfaces work best under different
circumstances and conditions. For both disabled and able-bodied people, it is clear that BCI based
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on P300 waves are the most efficient systems. These systems enable the users to continuously
control their tasks with high accuracy and less effort. Several studies were interested in using
P300 wave in BCI. Consequently, several schemes and algorithms are developed for both
eliciting and detection processes of this wave. Some of these approaches and algorithms were
explored, implemented, and tested in this study. These approaches and algorithms are used later
in real-time P300-based BCI that collects EEG data using commercial sensors. This thesis
investigates different EEG data processing methods, EEG features and feature extractors, and
classifiers. Combinations of different methods are tested with BCI competition III P300 data sets
and their results are discussed. Results show that performance of algorithms and approaches still
depends on the signal to noise ratio of EEG signals. The signal to noise ratio mainly depends on
the quality of EEG sensors. For the case of the BCI competition data sets, EEG signals were
collected with scientific good-quality devices and that made it possible for simple data processing
methods, such as down-sampling and DWT, and classifiers to reach high accuracies. However,
simple approaches were not useful with BCI that uses noisy EEG sensors. This is proven when
applying two different approaches with the proposed inexpensive BCI. One approach uses both
ICA and DWT for data processing, while the other approach uses only DWT. The first approach
led to higher accuracy than the second approach.
6.1.2 An inexpensive P300-based BCI development
First, different P300 visual evoking paradigms were explored and studied. Second, two
approaches of the P300 wave detection algorithms were chosen to be used according to their
results from the previous step. Finally, the proposed system was developed. This system uses the
Emotive Epoc headset as an EEG sensor. The system also uses the different visual paradigms to
evoke the P300 wave. The system uses the two chosen algorithms to detect this wave. The results
show that the P300 can be evoked with different visual paradigms. The results also show that the
evoked P300 can be detected using the tested methods. However, the results also show that the
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performance depends on the quality of the EEG signal. The simple data processing methods were
not sufficient for the classifier to discriminate between P300 and non-P300 epochs. The proposed
system was able to reach to reasonable accuracy using high-demand processing methods, like
Independent Components Analysis, and more complex classifiers. Also, it has been noticed that
using the ensemble classifiers technique did not improve the system accuracy. This leads to the
conclusion that when few stimuli are used, brain responses to the target stimuli are similar and
one classifier can handle the space of the four stimuli responses. Although more explorations and
development still need to be carried out, the overall performance of the proposed system proved
that using commercial EEG sensors in research fields is promising, especially, for P300 wave
detection.
6.2 Perspectives and Future Directions
6.2.1 EEG Sensor
As mentioned in this thesis, commercial EEG sensors, such as the Emotiv Epoc headset, were
mainly developed for the entertainment and gaming purposes. Involving them in the research
work, where the accuracy is the most important key point, requires sufficient data processing
approaches and machine learning algorithms. In contrast, there is still more work that need to be
carried out to improve the quality of the data acquiring and processing of these commercial
devises. This work may include hardware development, software development, or both to meet
the required goals. Since the commercial EEG sensor used in this work is the Emotiv Epoc
headset, we will only discuss perspectives regarding the Emotiv Epoc headset.
Electrodes leads may be relocated to cover more of the visual cortex. This requires more
investigations due to the possibility of affixing problems. Also, the current way of
affixing electrodes needs to be more stable. This is especially true where the electrode
contact loss problem is frequent during the experiment. This is also true for cases
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involving the movement of the electrodes during the EEG measuring. Currently,
movement can cause voltage offsets, which increases the need for more data analysis
work.
Sampling rate and hardware filtering need to be reviewed to overcome the aliasing
problem. In addition, since the data was originally captured at 2048 Hz, the sampling rate
may be increased, for example using 256Hz, or even more, instead of 128Hz, to increase
the resolution of the captured signals.
In addition to the above perspectives, different commercial EEG sensors may also be used instead
of Emotive epoc such as Mynd
TM
headset developed by NeuroFocus Company.
6.2.2 Control Applications
If the results from this system improve, the proposed P300-based BCI can tackle more complex
control applications, such as controlling mobile robots. Similar to the study proposed by Kansaku
et al., [44], the user can control the robot with this system while receiving feedback through its
camera. The system can be provided with a GUI that includes a live video from the robot’s
camera and the user should stare at the stimulus that corresponds to the direction they want to
move the robot.
In addition, the proposed BCI system can be extended to be used for different applications that
use P300 wave detection, such as helping security people in the monitoring process.
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