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the dot product of x
i
and x
j
in the higher-dimensional space. In this thesis, a polynomial kernel of
the second order is used that is defined as:
(22)
where c is some non-negative constant, usually c = 1. Using of a generalized inner-
product instead of the standard inner-product is proposed in [46]. In this case the kernel is:
(23)
where the vector σ is such that the function satisfies the Mercer's condition. When c is
positive the kernel is called inhomogeneous and, correspondingly, homogeneous when c = 0. The
inhomogeneous kernel avoids problems with the Hessians becoming zero in numerical
calculations.
3.5 Evaluation of P300-based BCI Systems
In the previous chapter and sections of this chapter we have seen that to build a P300-based BCI
system, several different methods can be applied. Different approaches exist for stimulus
presentation, for the aggregation of information from single trials, and for discriminating P300
from non-P300 segments. Consequently, it is logically impossible to find a metric with which all
the different systems can be compared and evaluated. However, it is possible to describe the
properties of P300-based systems that should enter in a metric for comparison and evaluation. In
the following we describe some of these properties, concentrating especially on aspects related to
the practicality and suitability for daily use of P300-based BCI systems.
A very important aspect of any BCI system is the achievable speed of communication. As
mentioned before, in a P300-based BCI system the speed of communication depends on the
interstimulus interval (ISI), the number of different stimuli, the classification accuracy, and the
54
control flow of algorithm [9 and 84]. To change from all these factors, it is useful to use the
information transfer rate (also known as bit rate) as a metric for the communication speed. The bit
rate measures the number of bits that can be transferred from a subject to the system in a specified
amount of time [84]. It is also commonly used to evaluate other non-P300 BCI systems. Before
we present how bit rate can be computed, we will define the confusion matrix of a two class
classifier problem and the entries that are used to build this matrix and calculate some other
factors such as True positive rate and False positive rate, and then calculate the bit rate. Below
we list these entries and how to build the confusion matrix and calculate the above-mentioned
evaluation metrics [28 and 84]:
a = number of correctly classified non-target epochs.
b = number of incorrectly classified non-target epochs.
c= number of incorrectly classified target epochs.
d = number of correctly classified target epochs.
Predicted
Non-target
Target
Actual
Non-target
a
b
Target
c
d
Table 3.1: Confusion matrix for evaluation
True positive rate (TPR) is the proportion of target blinks that were correctly identified,
while false positive rate (FPR) is the proportion of non-target blinks that were incorrectly
classified as targets and both are calculated as in the equations below using above entries of the
fusion matrix [84]:
n
1
= number of target epochs.
n
2
= number of non-target epochs.
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(24)
(25)
Using the above metrics the bit rate can be calculated according to the following equation [80]:
, bit/min (26)
Here ITI is the inter-trial interval; the interval between the presentation of two (stimuli)
and N is the number of different stimuli used inside the interface (in our case 4 arrows). Note that
according to the noisy-channel coding theorem, the bit rate is an upper limit on the number of bits
that can be transmitted, given the characteristics of the transmission channel. This limit can only
be accomplished if optimal encoding and decoding algorithms are used [84]. Since in a BCI the
encoding has to be performed by the user and since optimal encoding algorithms are relatively
complex, the bit rate is mostly of theoretical value.
In addition to the bit rate, the estimated system accuracy is used as an evaluation metric
for such systems and can be calculated as follows:
(27)
Several important characteristics concern the overall usability of a P300-based BCI
system (or even a general BCI system) other than the bit rate and accuracy, particularly regarding
the use of a system by severely handicapped users. Clearly, any system for handicapped users
should be accommodated to their limited cognitive abilities. For example, using a large number of
different stimuli, possibly in combination with very short ISIs, may limit the usability for subjects
with visual problems. In other words, the number of different stimuli, the size of the stimuli, and
the ISI should be adapted to the user according to its situations. Generally, any BCI system
targeted to be use by handicapped subjects should also be tested by such subjects.
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Another point influencing the practicality of the interested BCI system of interest is the
time and efforts needed to setup and adjust the system for a new user. Systems that use only few
electrodes or use headsets that are comfortably fabricated like Emotiv Epoc take less time for
setup and are more user friendly than systems with many electrodes. However, if too few
electrodes are used accurate classification may not be achieved because not all necessary features
can be captured and as a consequence communication speed decreases. Therefore, a good tradeoff
between the time needed for setup and the classification accuracy has to be figured out. Moreover,
it is important to minimize the time and effort needed to train a classifier for a specific user. Long
training sessions should be avoided for a system to be accepted by end users. Consequently, the
capacity of training data that is necessary to achieve a certain communication speed is probably
as important a characteristic as the communication speed itself [10, 47 and 78].
In addition, it is important to limit the amount of user interruptions necessary during the
setup of a BCI system. Several of the prototype systems described in the previous chapter rely on
the mediation of a trained technician during setup, for example, to select ICA components or to
choose hyperparameters for classification (SVM classifier). This is clearly unsuitable for a
practical system that should be able to adjust to new users according to a simple and fully
automatic procedure.
3.6 Other Issues Relevant to P300-based BCI Development
Averaging Across Trials
In most cases, several trials need to be averaged together to detect the P300 ERP. Continues brain
activity makes averaging in demand because scalp recorded EEG signals are a combination of all
current brain activity [30]. However, by time locking averages to a known event (e.g., stimulus
presentation) the brain activity not related to the stimulus presentation can be basically averaged
to zero, thereby not affecting the ERP. When the signal of interest is large (e.g., a 10 μV P300)
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fewer trials may only be need to be averaged together to realize the ERP. In terms of current
studies, subjects who evoke a large stable P300 wave will require fewer stimulus presentations
before averaging is required due to the fact that the signal to noise ratio will be higher for these
subjects. A faster rate of communication is achieved by using the consequence of fewer trials is.
Single Trial Classification
The most important problem that faces mainly all of the BCI systems is how to maximize the
signal to noise ratio and classify the selection as accurately and as fast as possible. Ideally, we
would like to be able to present a unique stimulus; then, directly apply a classification algorithm
to determine if that stimulus is most likely the targeted item (i.e., the action to be executed).
However, in most practical case this is not very likely. A main factor that contributes to this is
relevant to the fact that the ERP is a small signal compared to the amount of continues EEG
activity [9, 30 and 79].
Furthermore, other factors can effect whether or not a single stimulus will evoke a
response. For example, it is possible that, on any given trial, the user may blink his/her eye, move,
cough, or for any reason interrupt the EEG signal. It becomes necessary to present a series of
duplicated random stimuli to amplify the signal and to reduce the variability introduced into any
single time-locked stimulus event. To further complicate the ability to classify using single trial
classification, it has been shown that within an oddball sequence there are local sequential
dependencies relevant to stimulus identity that affect the evoked response [30 and 62].
Epoch Overlapping
The amount of epoch overlap also influences the signal to noise ratio. Generally, The P300
latency is in the range of 300 to 750 ms, and depends on the time of recognizing and evaluation of
the task-relevant event [45]. Consequently, if a stimulus, for example, is presented every 100 ms,
and each epoch contains 900 ms, each epoch will overlap with preceding and subsequent epochs.
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For example, assume that a P300 occurs on trial M, trial M-1 will show the increase in amplitude
approximately at 700 ms and trial M+1 will show an increase in amplitude at 150 ms. Averaging
over multiple trials will reduce the noise accompanied with overlapping epochs. Another way to
reduce this noise would be presenting stimuli at a slower rate, as we are doing in this study. The
ISI in this study is 2500 ms. The effect of this solution is a reduction in the efficiency of the
system in terms of communication rate. Increasing the overall speed with which communication
can occur is a logical next step in the process. Farwell and Donchin et al., [29] examined the
effects of ISI using the P300 Speller. Two levels of ISI were tested, 125 ms and 500 ms. Three of
the four tested subjects were able to reach accuracy levels of 80% and 95% faster in the 500 ms
ISI situation than in the125 ms ISI. These results indicate that three of the four subjects were able
to select more characters in a given amount of time even though the presentation rate per stimulus
is four times longer. For the participants in the study the increase in signal to noise ratio provided
by the longer ISI is more useful than presenting more stimuli before averaging. The relationship
between rate of stimulus presentation and maximizing the signal to noise ratio will need to be
determined for each user independently because individual differences will specifically play a
role in the speed of an individual ability of processing the stimulus display [9 and 84].
Individual Differences
As mentioned above, the speed and accuracy of the system has to be suitable for the user to
comfortably continue with the system. A tradeoff between speed and accuracy need to be
established for each individual user. One assumption is that some users or tasks want or need to
communicate fast and consequently more errors are accepted. Others may want or need to
communicate slowly but guarantee that the message they type or the command they execute is
accurate. Operant conditioning and shaping techniques are essential for the SCP and mu based
systems, and users learn to control the EEG activity over weeks or months using feedback in real
time as cursor movement on a computer display [47]. In contrast, in the P300 system, a P300 is
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evoked without any prior training. The feedback in this case is only a performance feedback for
the process of attention. The P300-based BCI system provides feedback only after each stimulus
selection. After a predefined number of stimulus presentations, the system applies a classification
algorithm to the set of possible stimuli and selects the stimulus with the highest classification
output. For both types of systems, the feedback is only as good as the classification that the
system uses for evaluation; however, the algorithm is derived from previous data. This means that
classification depends on how similar the evoked response is across time for the P300 system.
The variability in the user’s responses and the speed at which the desired response can
be evoked determines how many stimulus presentations are needed for a given level of
classification accuracy. In relation to response variability, if the variance of the evoked responses
is small, fewer stimulus presentations are needed before classification. Similarly, to the extent
that discriminable responses can be quickly detected, the ISI between target flashes can be
reduced. The number of stimulus presentations and the ISI can be optimized for each user. Some
users may not be able to tolerate fast presentation rates, while others may not tolerate the
increased error rate associated accordingly. Monitoring of the situation careful by the researcher
should lead to the most effective solution for each case independently [47, 62 and 65].
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CHAPTER 4
EXPERIMENTAL METHODS
4.1 Introduction
In this chapter we present a P300-based BCI that uses one of the commercial EEG sensors to
acquire the EEG raw data. The system is based on the P300 evoked potential and functions only
in synchronous mode. In synchronous mode, communication is possible only during predefined
time intervals. This means the system tells the user when it is ready to receive the next command,
while in asynchronous mode users can send commands whenever they want. The proposed BCI is
tested with three able-bodied subjects. Additionally, in order to test and validate the efficiency of
several algorithms proposed in the literature and their usability in our system, experiments are
performed to apply such algorithms to one of the past BCI competition’s P300 datasets.
The rest of the chapter is organized as following: Section 4.2 briefly reviews some of the
related work on the P300-based BCIs and BCI researches that use the same commercial non-
invasive headset in the same field of study. Section 4.3 shows the experimental steps of applying
specific P300 detection algorithms to P300 datasets from the BCI competition III, 2004. In
Section 4.4 the real-time approach for the proposed P300-based BCI system is explained;
experiments setup and schedule for P300 evoking are first described, then online EEG data
recording from the Emotiv headset, methods for analyzing and classifying this data, and system
training are explained. Finally, real-time testing of the proposed BCI system is presented.
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4.2 Related Work
Recently, many studies have been published in which P300-based BCI systems were tested with
both disabled and able-bodied subjects. P300-based BCI systems have been widely used and
explored for the use in the human environmental control applications. In the last decade many
researchers were involved in developing brain headsets that are more reliable, comfortable,
mobile, and cheap. Referring to our idea, we can categorize related works into two parts. The first
part is about recent researches developed P300-based BCIs for different control applications
using different scientific EEG sensors, while the second part discuses recent studies that use one
of the recent developed EEG headsets, the. Emotiv Epoc headset, which is also used in this work.
Piccione et al., [28] tested a 2D cursor control system with five disabled and seven able-
bodied subjects. For cursor control, a four-choice P300 paradigm was used (see figure 4.1).
Subjects had to concentrate on one of four arrows (forward, backward, right, and left) flashing
every 2.5 s in random order in the peripheral area of a computer screen. Signals were recorded
from one Electrooculogram electrode and four EEG electrodes, preprocessed with independent
component analysis and classified with a neural network. They showed that the P300 is a viable
control signal for both healthy and disabled subjects. However, the average communication speed
obtained in their study was relatively low when compared to state-of-the-art systems, as for
example the system described in [49]. This can probably be ascribed to the use of signals from
only very few electrodes.
Figure 4.1: Stimuli paradigm used by Piccione et al., [28]
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Sellers and Donchin et al., [74] also used a four-choice paradigm and tested their system
with three subjects suffering from ALS and three able-bodied subjects. In their study four stimuli
(’YES’, ’NO’, ’PASS’, ’END’) were presented every 1.4 s in a random order, either in the visual
modality, in the auditory modality, or in a combined auditory-visual modality. Signals from 16
electrodes were classified with a stepwise linear discriminant algorithm. The research of Sellers
and Donchin showed that P300 based communication is possible for subjects suffering from ALS.
The research also showed that communication is possible in the visual, auditory, and combined
auditory-visual modality.
Hoffmann et al., [80] proposed a P300-based BCI system that uses a six-choice paradigm.
Users were facing a laptop screen on which six images were displayed. The images showed a
television, a telephone, a lamp, a door, a window, and a radio (see figure 2.8). The images were
selected according to an application scenario in which users can control electrical appliances via a
BCI system. The images were flashed in random sequences, one image at a time. Each flash of an
image lasted for 100 ms and during the following 300 ms none of the images was flashed, i.e. the
ISI was 400 ms. The EEG was recorded at 2048 Hz sampling rate from thirty-two electrodes
placed at the standard positions of the 10-20 international system. Bayesian discriminant analysis
was used as a classification and machine learning algorithm. The study showed that significant
accuracy was achieved by using the six-choice paradigm and different ISIs.
Kansaku et al., [44] developed a P300-based system that enables the user to control an
agent robot and a desk light in the robot’s environment through its cameras as shown in figure 4.2.
The user is provided with a robot control panel that has four icons (forward, backward, right, and
left), as does the light control panel (turn on, turn off, make brighter, and make dimmer).The
study used the green/blue flashings proposed in [76], and the ISI was 100/50 ms. All of the icons
flickered in a random order, which formed a sequence of 600 ms data points. One classification is
carried out every 15 sequences. Eight-channels (Fz, Cz, Pz, P3, P4, Oz, PO7, and PO8 of the
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