# A Novel Design of 4-Class BCI Using Two Binary Classifiers  and Parallel Mental Tasks

Authors: Tao Geng; John Q. Gan; Matthew Dyson; Chun SL Tsui; Francisco Sepulveda
Journal: Computational Intelligence and Neuroscience
 (2008)

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## Abstract

A novel 4-class single-trial brain computer interface (BCI) based
on two (rather than four or more) binary linear discriminant analysis
(LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike
other BCIs where mental tasks are executed and classified in a serial
way one after another, the parallel BCI uses properly designed parallel
mental tasks that are executed on both sides of the subject body
simultaneously, which is the main novelty of the BCI paradigm used
in our experiments. Each of the two binary classifiers only classifies
the mental tasks executed on one side of the subject body, and the
results of the two binary classifiers are combined to give the result
of the 4-class BCI. Data was recorded in experiments with both real
movement and motor imagery in 3 able-bodied subjects. Artifacts
were not detected or removed. Offline analysis has shown that, in
some subjects, the parallel BCI can generate a higher accuracy than a
conventional 4-class BCI, although both of them have used the same
feature selection and classification algorithms.


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## Body

## 1. Introduction

Low communication speed is one of the main problems
hampering the application of brain computer interfaces (BCIs) outside
laboratories. Most of the current electroencephalogram(EEG-) based BCI systems
use various mental tasks which are classified and translated into different
computer commands using various pattern classification algorithms. An increased
number of mental tasks or brain patterns, if classified reliably, can
potentially boost the communication speed of the BCI systems. This is because
as the number of classes grows, the potential number of class combinations
grows exponentially. In recent years, there have appeared some BCIs employing
multiclass classifiers in their EEG pattern discrimination. Obermaier et al. [1] used four motor-imagery and one
mental-calculation tasks. Their
initial results showed that using three classes could improve the information
transfer rate. With motor-imagery tasks consisting of four different classes,
Naeem obtained accuracies between 33 percent and 84 percent using independent
component analysis (ICA) [2]. Townsend compared
common spatial patterns
(CSP) with complex band power features in a four-class BCI involving motor
imagery [3]. Widely used motor imagery mental tasks in
4-class BCIs
[2–4] involve the movements of left hand,
right hand, feet, and tongue. The tongue-related task is problematic in
EEG-based BCIs because it may produce
electromyography (EMG) which is difficult to monitor and could be treated as
EEG by the classifiers.To realize its potential higher information transfer
rate, a multiclass BCI must have a considerably high accuracy. Unfortunately,
with the number of classes increased, the accuracy of the BCIs decreases
because every additional EEG pattern to be classified brings up more difficulty
to the classifier. Moreover, many classification algorithms, such as linear
discriminant analysis (LDA) [1] and support vector machines (SVMs), are
best suited for classifying binary problems.Although the classifiers play an important role in the
accuracy of BCI systems, neurophysiological background knowledge of EEG
signals, if properly exploited in the design of mental tasks and experiment
paradigms, will also help improve the accuracy of a BCI system. It is well
known that each hemisphere of the brain is related to the opposite side of the
body. For example, left-hand movement is represented in the right motor cortex,
and right hand movement in the left motor cortex. Neighboring parts of the
cortex represent neighboring parts of the body. A principle used by many BCIs
in choosing mental tasks is that mental tasks should activate different parts
of the brain, thus generating easily separable EEG patterns.Having in mind the basic knowledge of neurophysiology
and the fact that binary classifiers greatly outperform multiclassifiers, we
propose a new approach to multiple mental/motor task classification in BCI
design, which we name “Parallel BCI.” The novelty in our approach lies in
that two binary classifiers, called left BCI and right BCI, run in parallel to
classify the properly designed parallel mental tasks that are executed
simultaneously on the left side and right side of the subject body. The mental
tasks of the parallel BCI only involve hand and feet movement. The results from
the left BCI and right BCI are combined leading to the classification of four
mental states. It is demonstrated that, in some subjects, the parallel BCI
achieves a higher accuracy than the conventional 4-class BCI for classifying
four mental states.


## 2. Data Acquisition

We designed two parallel paradigms for our
experiments. One only involves hands movement (paradigm A), and the other
involves hand and feet movement (paradigm B). Their corresponding labels are
described in Table1 and
Figure 1, respectively. The experiment consisted of 3
runs with 40 trials each for each subject. In each trial, from t=3 seconds, an
arrow pointing to left, right, up, or down was displayed (see
Figure 2). Subjects
were instructed to execute or imagine hand/foot movement at one or both sides
of the body, as indicated in Table 1. For example, in the experiment of paradigm
B (see Table I(b)), when the cue of an up arrow is displayed, the subject should
imagine movements of both hands at the same time. When a left arrow displayed,
the subject should imagine a left-hand movement and a right-foot movement at
the same time. For a right arrow, it means simultaneous right-hand movement and
left-foot movement. The down arrow means simultaneous movements at both feet.
Combining the movements executed simultaneously at both sides of the subject
body, we can get the class labels of the 4-class whole system (see
Table 1). In
paradigm A, these classes (combinations) are both hands, left hand only, right
hand only, no movement at all (see
Table I(a)). In paradigm B, they are both
hands, left hand and right foot, right hand and left foot, both feet (see
Table 1). No feedback was shown to the subject in the experiments. It should be noted
that, in paradigm A, “no movement” (or relax) at left/right side of the
subject body is regarded as a mental task (EEG pattern) in the left/right BCI.
“Relax” has been used as an EEG pattern in synchronous BCIs, though not quite
commonly. For example,  Akrami et al. [5] employed baseline as a mental task in
a 3-class BCI.Table 1
The mental tasks and corresponding class
labels of the binary left/right BCI and the 4-class whole system in paradigm A
and B. Note that the class label of left/right BCI corresponds to the movement
of the left/right side of the body. For example, in paradigm A, positive output
(+) of the left BCI indicates the left-hand movement. The subject is instructed
to execute or imagine the movements at both sides of his body simultaneously.
The left/right BCI only classifies the two kinds of movements of the left/right
side at the subject body.
(a)
Paradigm A(b)
Paradigm B(a) The map between the two classes of the
left/right BCI and the 4 classes of the whole system in paradigm A (also see
TableI(a)). The 4-class classification result of the total system is determined
by the outputs of the left BCI and right BCI. For example, when and only when
both left BCI and right BCI have positive outputs, the class of the total
system will be regarded as 4. (b) The map in paradigm B is similar to that of
paradigm A except that it involves foot movement.
(a)
Paradigm A(b)
Paradigm BFigure 2
Experimental paradigm begins with a blank
screen. After 2 seconds, a fixation cross appears and an audio tone warns the
subject to prepare. At second three, an arrow appears on the screen, indicating
the motor imagery the subject should perform (adapted from [1]).The electrode positions with respect to the
international 10–20 systems are shown in
Figure3. The recording was made with a
16-channel EEG amplifier from G-Technology (http://www.gtec.at/). The
channels in the left hemisphere were referenced to the left mastoid.
The channels in the right hemisphere were referenced to the right mastoid. The
EEG was sampled at 256 Hz.Figure 3
The electrode positions of the parallel BCI.


## 3. Data Processing

The recorded EEG data was first filtered for
0.5–100 Hz, and then preprocessed with common average reference and band power
feature extraction (with 16 bands covering 8–45 Hz, that is, 8-9 Hz, 10-11 Hz,
12-13 Hz, 14-15 Hz, 16-17 Hz, 18-19 Hz, 20-21 Hz, 22-23 Hz, 24-25 Hz, 26-27 Hz,
28–30 Hz, 31–33 Hz, 34–36 Hz, 37–39 Hz, 40–42 Hz, 43–45 Hz). The band power of each
frequency band at each channel is calculated by first digitally bandpass
filtering the data, squaring each sample and taking logarithm, and then
averaging over a one-second sliding window [6]. Averaging the samples
of band power over a one-second window is a method widely used in EEG-based
BCIs to smooth the data and reduce the variability. Electrooculogram (EOG) and
other artifacts were not detected or removed. A subset of no more than 20
features was selected using a sequential forward floating selection (SFFS)
[7] algorithm based on 3-fold cross-validation. SFFS starts from an empty
set and in each iteration generates new subsets by adding a feature selected by
an evaluation measure (here, it is the LDA classifier) [7].
It has been
found that simple linear classifiers were just marginally worse than complex
nonlinear methods [8, 9]. It was shown in the BCI Competition
2003 and 2005 that LDA performed as well as (sometimes even outperforms) SVMs
[10], and almost all the winning classifiers were linear
[11]. Hence, two binary LDA classifiers, one in left BCI and the
other in right BCI (see Figure 1), were used to classify the two motor tasks of
the left side and right side, respectively. The binary LDA classifier assigns
linear weights to the band power features so as to provide a separating
hyperplane between the two classes in feature space. For details of the LDA
algorithm, please refer to [1]. The 4-class result of the Parallel BCI was
obtained according to the class label coding indicated in
Table 1
and Figure 1.For a comparison, we also processed the data by
regarding the system as a conventional four-class BCI, which, for convenience,
is called “conventional BCI.” The 4 classes of the conventional BCI in
paradigm A are the four combinations of the
movements executed simultaneously on the left and right sides of the subject
body (i.e., both hands, left hand only, right hand only, none movements at all)
(see Figure4). Similarly, the 4 classes of the conventional BCI in paradigm B
are both hands, left hand and right foot, right hand and left foot, both feet.
The conventional BCI used the same feature extraction and classification (LDA)
methods. As LDA is not directly appropriate for 4-class classification, we used
four one-versus-all binary LDA classifiers. Rifkin's analysis and review
[12] has shown that, for multiclass problems, the “one-versus-all”
scheme can be as accurate as any other approaches. In the conventional BCI,
each LDA classifier was trained to discriminate one of the four classes from
the remaining three. For each test sample, the four classifiers were run each
with the data. The classifier that generated the largest positive value was
chosen to give the result of the conventional 4-class BCI [12].Figure 4
The map between the positive output of the 4
one-versus-all LDA classifier and the 4 classes of the conventional BCI (also
see Table1) in paradigm A. For example, LDA 3 is a binary classifier
discriminating class 3 against all remaining classes (1, 2, 4). If its positive
output is the largest among all the four binary classifiers, the class of the
conventional BCI will be regarded as 3.


## 4. Results

Each data set was obtained from an experiment
(paradigm A or B) of one subject, consisting of 3 sessions, each with 40
trials. It was processed using 3-fold cross validation. The averaged accuracy
got from the test data of the three folds of each data set is shown in
Tables2
and  3. Subjects 1 and 2 are male and right-hand dominant. They had
experience in BCI experiments. Subject 3 is female and left-hand dominant, and
had no experience of BCI experiments before. All subjects were able-bodied. In
some experiments of paradigm A and paradigm B, the parallel BCI produced a
higher accuracy than that of the conventional BCI. Experiences and training for
the parallel BCI experiments in Subjects 1 and 2 have incurred better results
than in Subject 3.Table 2
Classification accuracies (mean and standard
deviation) of the parallel BCI and conventional BCI with 3 subjects executing
real motor tasks (3-fold cross-validation).Table 3
Classification accuracies (mean and standard
deviation) of the parallel BCI and conventional BCI with 3 subjects executing
motor imagery tasks (3-fold cross-validation).


## 5. Discussion

The novelty of the parallel BCI is in the design of
the mental tasks (i.e., the coded parallel mental tasks). Unlike other BCIs
where the mental tasks are executed and classified one after another in a
serial way, the mental tasks in the parallel BCI are executed parallel at both
sides of the subject body. Moreover, the binary mental tasks at each side of
the subject body are separately classified by a binary classifier. The
potential separability of the EEG patterns caused by the left and right limbs
has been exploited to reduce a 4-class BCI to two binary BCIs. For some
subjects, this reduction has brought the whole system, a 4-class BCI, an
accuracy higher than that of a conventional 4-class BCI which employed 4
one-versus-all binary classifiers.The parallel BCI and the conventional BCI involved in
this paper have indeed used the same binary classification algorithm (LDA), the
same features (band power), and the same feature selection algorithms (SFFS).
The vital difference between them is that the parallel BCI has exploited the
coding in the properly designed parallel mental tasks while the conventional
BCI has not. Therefore, the improved performance of the parallel BCI for some
subjects is due to the coded mental tasks rather than the classifier or the
feature selection algorithm it used.One drawback of the parallel BCI (especially in
paradigm B involving hand and foot movements) is that the subjects need a few
training sessions before they can get used to the simultaneous parallel mental
tasks at their left and right hand/foot. Because this is the first time this
kind of simultaneously executed mental tasks were used in a BCI study, the
neurological difference between the topographic patterns of parallel mental
task and serial mental task is not clear. Moreover, currently only simple band
power features were used for the classification. Common spatial pattern (CSP)
method has shown its efficacy in extracting topographic pattern of brain rhythm
modulations [13]. Phase synchronization reflects the cooperative
interactions between anatomically disparate neural populations [14].
These methods could be more appropriate for classifying the parallel mental
tasks, which will be investigated in the future work.Our current work considers only offline analysis of
synchronous BCI experiments. An offline scenario is more suitable for comparing
the schemes of the parallel BCI and conventional BCI as it is more reliable and
stable [10, 15]. However, the aim of our next work is
online BCI. As shown in other BCIs, with online feedback, the classification
accuracy can be increased even more.



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*Source: 437306-2008-06-22.xml*