This study is devoted to the classification of fourclass

mental tasks data for a Brain-Computer Interface

protocol. In such view we adopted Multi Layer

Perceptron Neural Network (MLP) and Fuzzy C-means analysis for classifying: left and right hand movement imagination, mental subtraction operation and mental recitation of a nursery rhyme.

Five subjects participated to the experiment in two sessions recorded in distinct days. Different parameters were considered for the evaluation of the performances of the two classifiers: accuracy, that is, percentage of correct classifications, training time and size of the training dataset. The results show that even if the accuracies of the two classifiers are quite similar, the MLP classifier needs a smaller training set to reach them with respect to the Fuzzy one. This leads to the preference of MLP for the classification of

mental tasks in Brain Computer Interface protocols

Bianchi, L

Cavallo, P

Ferretti, A

Garzoli, F

Giannini, F

Marciani, MG

Quitadamo, LR

Saggio, G

Archivio della Ricerca - Università di Roma Tor vergata

Comparison of Two Different Classifiers for Mental Tasks-Based Brain-
Computer Interface: MLP Neural Networks vs. Fuzzy Logic 
 
 
Giovanni Saggio1#*, Pietro Cavallo#, Alessio Ferretti#, Francesco Garzoli#, Lucia Rita 
Quitadamo§&, Maria Grazia Marciani§, Franco Giannini#*, Luigi Bianchi§*& 
#Dept. of Electronic Engineering, “Tor Vergata” University, via del Politecnico 1, 00133 Rome, Italy 
§ Dept. of Neuroscience, “Tor Vergata” University, via Montpellier 1, 00133 Rome, Italy 
*Centro di Biomedicina Spaziale, “Tor Vergata” University, via del Politecnico 1, 00133 Rome, Italy 
& Dept. of Neurophysiology, Fondazione S. Lucia, IRCCS, via Ardeatina 306, 00179 Rome, Italy 
1saggio@uniroma2.it 
 
 
 
Abstract 
 
This study is devoted to the classification of four-
class mental tasks data for a Brain-Computer Interface 
protocol. In such view we adopted Multi Layer 
Perceptron Neural Network (MLP) and Fuzzy C-means 
analysis for classifying: left and right hand movement 
imagination, mental subtraction operation and mental 
recitation of a nursery rhyme.  
Five subjects participated to the experiment in two 
sessions recorded in distinct days. Different 
parameters were considered for the evaluation of the 
performances of the two classifiers: accuracy, that is, 
percentage of correct classifications, training time and 
size of the training dataset. The results show that even 
if the accuracies of the two classifiers are quite 
similar, the MLP classifier needs a smaller training set 
to reach them with respect to the Fuzzy one. This leads 
to the preference of MLP for the classification of 
mental tasks in Brain Computer Interface protocols.  
 
 
1. Introduction 
 
Brain Computer Interface (BCI) systems have been 
developed as assistive devices for disabled people that 
have lost the control on their body after dramatic 
events, such as strokes, spinal traumas, degenerative 
diseases, etc. [1]. 
These people can no more communicate towards 
the external world and so lie into an isolation 
condition.  
BCI systems try to furnish these people an 
alternative communication channel, by translating 
some of their brain signals into commands for piloting 
an external device that can be a wheelchair, a robotic 
arm, a Web surfer, a cursor on a screen, etc. This is 
obtained in the following way: the brain signals are 
acquired and then processed to extract some features of 
interest from them; these features are then classified 
and encoded into semantic symbols that are finally 
mapped into the output commands. 
Some BCI systems can be driven by means of 
mental tasks [2],[3], in the sense that the user of the 
system mentally imagines to perform some particular 
tasks that are then recognized by a classifier and used 
to pilot the output peripheral. For example, as in the 
case of the experimental protocol described in this 
paper, the subject is asked to imagine right and left 
hand movements, to perform mental calculation and to 
mentally recite a nursery rhyme. These mental tasks, 
after the classification, can be associated, for example, 
to the 2D movement of a cursor on a screen thus 
implementing the selection of particular targets and so 
the communication. 
Two different classifiers are here presented for the 
classification of four-class mental tasks data: Multi 
Layer Perceptron Neural Network and Fuzzy C-means 
analysis. 
Since the 4 chosen tasks are uncommon in 
literature, it is not certain yet which classifier is better. 
So we chose MLP and Fuzzy Logic because the former 
is already consolidated in BCI, and the latter, despite it 
is a fairly new methodology in this field, performs a 
pretty well spatial separation. 
Our purpose was to compare these methods and to 
find which better satisfies the requirements: accuracy, 
training time and size of the training dataset. 
 
2. Methods 
 
978-1-4244-4439-7/09/$25.00 ©2009 IEEE This work was partially supported by the DCMC Project of the 
Italian Space Agency. This paper only reflects the authors’ views and 
funding agencies are not liable for any use that may be made of the 
information contained herein. 
2.1 The experimental protocol 
 
Five subjects participated to the experiments. The 
EEG was recorded using 61 Ag-AgCl scalp electrodes 
located according to the International 10-20 system. 
The signals were 256 Hz sampled and bandpass-
filtered between 0.5 Hz and 128 Hz. The sensitivity of 
the amplifier was set to 4 mV.  
The experimental protocol consisted of four 
different imagery tasks, left and right hand movement 
imagination, mental subtraction operation and mental 
recitation of a nursery rhyme. Two sessions on distinct 
days were recorded for each subject. Each session 
consists of 200 trials (50 for each of the four possible 
tasks). 
The subjects sat in a comfortable armchair in front 
of a computer screen. For every trial a text, indicating 
the task to perform, appeared on the black screen for 3 
secs. The inter trial interval (ITI) was set to 1 sec. 
Based on our past experiences, to reduce the 
computational workload, only 10 among the 61 
electrodes (the green highlighted electrodes in fig. 1) 
were considered for the analysis and frequencies in the 
range of 8Hz and 13Hz (corresponding to α band [4]) 
were selected for every electrode. For a single trial, the 
relative powers of the signals in the above-said band, 
for each electrode, were computed, and constituted the 
features vector fed to the two different classifiers. 
 
 
 
Fig. 1. The 10-20 standard with the ten considered electrodes 
highlighted in green. 
 
 
2.2 Artificial Neural Networks 
 
An Artificial Neural Network (ANN) is a 
computational model inspired by the way biological 
nervous systems, such as the brain, process 
information. It is composed of a large number of 
interconnected processing elements (artificial neurons) 
working to solve specific problems. An ANN is 
configured for a specific application, such as pattern 
recognition or data classification, through a learning 
process. As well as biological systems, learning 
involves adjustments to the connections that exist 
between the artificial neurones. 
We used a Multi-Layer Perceptron (MLP) Neural 
Network made of four layers: one input, two hidden 
and one output. Each neuron is connected with a 
certain weight to every other neuron in the previous 
layer. At each time step, the input is propagated 
through layers. The input layer has 10 neurons, one for 
each considered electrode. For each trial input neurons 
receive the relative powers of electrodes normalized to 
an average of 0.5 value to make measures comparable 
between each other. At this point information is fed to 
the first hidden layer through weighted connections. 
Each hidden layer has 20 neurons. Excepted for the 
input layer all the neurons have a sigmoid activation 
function 
)5.0(61
1)(
−−+
= xe
xfs
  scaled in the range of 0 
to 1. 
Sigmoid was preferred due to its independent and 
fundamental space division properties [5],[6] as it 
models the frequency of action potentials of biological 
neurons in the brain.  
The output layer has 4 neurons, one for each mental 
task to be recognized. In case of a successful 
classification the output of the neuron corresponding to 
the classified task tends to 1 whereas other outputs tend 
to 0. 
Every neuron, except for the input layer, was 
initialized with a random weight in the range of -1/ n  
to +1/ n , where n is the number of neurons 
connected by means of that weight [7],[8]. As 
commonly done it was assigned to the input layer a 
constant weight of 1. 
After the output presentation a learning rule was 
applied. We used a supervised learning method [9] 
called backpropagation [10],[11]. The backpropagation 
calculates the mean-squared error between actual and 
expected output. The error value is then propagated 
backwards through the network, and small changes are 
made to the weights in each layer. The weight changes 
are calculated in order to reduce the error signal. The 
whole process was then repeated for each trial, and the 
cycle was reiterated until the overall error value drops 
below some pre-determined threshold. 
 
2.3 Fuzzy Logic 
 
Fuzzy logic arises as a method to formalize real-
world concepts that cannot be categorically identified 
as true or false, but that may have some degree of truth. 
The fuzzy logic has particularly effectiveness in 
applications of information extraction and 
interpretation. 
One of the hallmarks of fuzzy logic is that it allows 
nonlinear input/output relationships to be expressed by 
a set of qualitative “if-then” rules. Fuzzy rules provide 
a powerful framework for capturing and explaining the 
input/output data behavior. 
Extracting fuzzy rules for pattern classification can 
be viewed as the problem of partitioning the input 
space into appropriate fuzzy clusters: groups of trials 
with similar structural characteristics [12]. 
This is made by applying the algorithm Fuzzy C-
Means (FCM) on each n-dimensional vector of trials 
containing the relative powers of each electrode 
considered. The FCM is a clustering algorithm based 
on optimizing an objective function [13]. Given a set 
of elements 
p
nxxX ℜ⊂= },...,{ 1 , the aim of fuzzy 
clustering is to determine the prototypes in the way that 
the objective function 
),()(),,(
1 1
2∑ ∑
= =
=
c
i
n
k ik
m
ik vxduvUXJ  is 
minimized [13],[14],[15], where ]1,0[∈iku  stands for 
the membership degree of kx  to the cluster i, and 
),( ik vxd  is the Euclidean distance between kx  and 
the cluster i, represented by the so called prototype iv ; 
The apices c is the number of clusters. The choice of 
the value of the parameter c varies from case to case. 
For example, through many tests, the best 
classification for right hand thought was obtained with 
c equal to 8, whereas for left hand thought was 
obtained with 6. No theoretical foundations are yet 
available for the optimal choice of the parameter of the 
exponent m which was empirically set to 2.7 [12], [16]. 
This algorithm was applied for each mental task, 
such that they are represented by a set of clusters. 
FCM is an iterative process in which each cluster 
),...,2,1( Cici =  is regarded as a fuzzy set.  
To deduce the Fuzzy rules from clusters, it is 
necessary to write a membership functions for each of 
them. We decided to use a triangular membership 
function as a best choice for adequately represents the 
clusters. This was made by projecting on each i-th axis, 
the i-th coordinate of the prototype and the two data 
points (trials) that are most distant from the prototype. 
We assigned the minimum membership function value 
(0) to the projected trials and the maximum value of 1 
to the center of the cluster (prototype). In this way a 
Mamdani type fuzzy controller was implemented 
[17],[18]. 
 
3. Results And Discussion 
 
In Table I, the accuracies achieved by the neural 
network applied to the 4 tasks are shown: the average 
percentage of correct classifications is around 78%. 
 
TABLE I 
Multilayer Perceptron Classification 
Subject PERCENTAGE OF CORRECT 
CLASSIFICATIONS 
E. T. 81% 
P. C. 79.6% 
F. G. 78% 
D. P. 76.8% 
F. C. 76.7% 
Average percentage 78.4% 
 
Percentage of correct classifications of the four mental tasks, using 
five examples per task to train the Neural Network. 
 
We had better results by applying this method to the 
classification of the two motor tasks (left and right 
hand movement imagination) and of one motor 
towards one non-motor tasks (e.g. right hand and 
subtraction). In those cases, in fact, we achieved an 
average accuracy of 90%.  
Also we empirically found that the best compromise 
between speed and correct classifications percentage is 
given by considering 5 trials per task in the training 
phase. 
The results obtained with the fuzzy logic in 
classifying each task are on an average of 78% of 
correct classifications, with a peak of 82% (table II).  
The FCM algorithm has the advantage of obtaining 
a good compromise between the effectiveness of 
aggregation and the computational cost. In fact, each 
task required an average of 20 iterations to reach the 
optimum. Unfortunately this algorithm needs 40 
training trials in the clustering step to get a high 
percentage of classification. 
It is to be noticed that, in literature, the considered 
tasks are not easy to classify; so the obtained 
accuracies are fairly good.  
Both classifiers achieve the same mean percentage 
of correct classifications (fig. 2). However if we 
limited our analysis to this result it would seem that 
these classifiers can be used indifferently.  
But it was fundamental to evaluate a focal 
parameter: the number of examples (in fig. 2 
represented on the x axis) needed to reach the highest 
percentage. 
 
0
10
20
30
40
50
60
70
80
90
0 5 10 15 20 25 30 35 40 45 50
Number of trials
Pe
rc
en
ta
ge
 o
f c
or
re
ct
 
cl
as
si
fic
at
io
n
MLP Fuzzy
 
Fig. 2. Percentage of correct classifications as a function of the 
number of trials in the training set. 
 
In fact, MLP uses a training set constituted by five 
trials per task respect to the Fuzzy classifier. As 
previously mentioned, it takes 4 seconds to record a 
trial, being spent 3 secs for performing the mental task 
and 1 sec for the ITI. This leads to a recording session 
of 80 secs for training the MLP (5 trials x 4 tasks x 4 
secs) and 640 secs for the Fuzzy clustering (40 trials x 
4 tasks x 4 secs) and so a reduction of 8 times in the 
training of the former. 
This is critical because the training phase should be 
performed every time the patient that uses the BCI-
system changed (BCI machines are ad-personam 
systems), and also if the system is reused by a different 
patient (in the replacement of the helmet the electrodes 
position can change). 
Thus the training phase is done an enormous 
number of times and therefore it is essential for this 
stage to be as fast as possible. 
In conclusion, it can be deduced that, as it needs a 
small training set, MLP is preferable with respect to 
the FCM algorithm, for the classification of mental 
tasks in BCI protocols. 
 
4. Conclusion  
 
Here it is presented a comparison among two different 
classifiers for the classifications of four-class mental 
tasks for BCI protocols: Multi Layer Perceptron Neural 
Network and Fuzzy C-means analysis. The results of 
the classifications show that both classifiers achieved 
the same mean accuracy; however it is evident that the 
MLP neural network needs a reduced number of trials 
for training purposes, having the advantage in the 
reduction of the recording session up to 8 times lower 
than Fuzzy analysis. This is of fundamental importance 
because, as we already said, training is a critical step 
since it is done a huge number of times in a BCI-
system life. And so, the less a classifier is time-
demanding in the training phase, the more the patient is 
comfortable with it, being part of the communication 
load dramatically reduced. 
  
 
5. References 
 
[1] J. R. Wolpaw, N. Birbaumer. D. J McFarland, G. 
Pfurtscheller and T. M. Vaughan "Brain-computer 
interfaces for communication and control," Clinical 
Neurophysiology, vol. 113, no. 6, 2002, pp. 767-791. 
[2] A. Schogl, F. Lee, H. Bischof and G. Pfurtscheller, 
“Characterization of four-class motor imagery EEG 
data for the BCI-competition 2005”, Journal of Neural 
Engineering, 2005, vol. 2, L14-L22. 
[3] N. J. Huan and R. Palaniappan, “Neural network 
classification of autoregressive features from 
electroencephalogram signals for brain–computer 
interface design”, Journal of Neural Engineering, 2004, 
vol. 1, 142-150. 
[4] E. Kandel, J. Schwartz, and T. Jessell, “Principles of 
Neural Science” USA: McGraw Hill, 2000. 
TABLE II 
Fuzzy Logic Classification 
Subject PERCENTAGE OF CORRECT 
CLASSIFICATIONS 
E. T. 82% 
P. C. 79.1% 
F. G. 77% 
D. P. 76.2% 
F. C. 77.1% 
Average percentage 78,2% 
 
Percentage of correct classifications of the four mental tasks, with 
the Fuzzy Logic analysis, using forty examples per task to make 
clusters. 
 
[5]  S. Cammarata, “Reti neuronali, Dal perceptron alle reti 
caotiche e neuro-fuzzy”, Etas 1997 
[6] K. Hara and K. Nakayamma, “Comparison of 
activation functions in multilayer neural network for 
pattern classification” IEEE World Congress on 
Computational Intelligence., 1994, vol. 5, pp. 2997-
3002. 
[7] C. Hernàndez-Espinosa and M. Fernandez Redondo, 
“Multilayer Feedforward Weight Initialization” 
European Symposium of Artificial Neural Networks 
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[8] H. Lari-Najafi, M. Nasiruddin and T. Samad “Effect of 
initial weights on back-propagation and its variations” 
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[9] L. G. Allred and G. E. Kelly, “Supervised learning 
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International Joint Conference on Neural Networks, 
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[10] R. Hecht-Nielsen “Theory of the backpropagation 
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backpropagation network for pattern recognition 
systems” 
[12] L. Mikhailov, A. Lekova, F. Fischer and H. A. Nour 
Eldin, “Method for fuzzy rules extraction from 
numerical data”, IEEE International Symposium on 
Intelligent Control, 1997.  
[13] J. Abonyi, R. Babuska and F. Szeifert, “Modified Gath-
Geva Fuzzy Clustering for Identification of Takagi-
Sugeno Fuzzy Models”, IEEE transactions on systems, 
man, and cybernetics, 2002. 
[14] M. Menard, “Extension of the objective functions in 
fuzzy clustering”, Fuzzy Systems, 2002.  
[15] J.C. Bezdek, “Pattern Recognition with Fuzzy 
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[16] L.B. Romdhane, B. Ayeb and S. Wang, “An improved 
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Comparison of two different classifiers for mental tasks-based Brain-Computer Interface: MLP Neural Networks vs. Fuzzy Logic

This study is devoted to the classification of fourclass
mental tasks data for a Brain-Computer Interface
protocol. In such view we adopted Multi Layer
Perceptron Neural Network (MLP) and Fuzzy C-means analysis for classifying: left and right hand movement imagination, mental subtraction operation and mental recitation of a nursery rhyme.
Five subjects participated to the experiment in two sessions recorded in distinct days. Different parameters were considered for the evaluation of the performances of the two classifiers: accuracy, that is, percentage of correct classifications, training time and size of the training dataset. The results show that even if the accuracies of the two classifiers are quite similar, the MLP classifier needs a smaller training set to reach them with respect to the Fuzzy one. This leads to the preference of MLP for the classification of
mental tasks in Brain Computer Interface protocols

SAGGIO, GIOVANNI

MARCIANI, MARIA GRAZIA

GIANNINI, FRANCO

BIANCHI, LUIGI

English

Quitadamo, L

Marciani, Mg

Cineca Institutional Research Information System

https://art.torvergata.it/retrieve/handle/2108/100272/203515/1569188913%20BCI.pdf

Comparison of two different classifiers for mental tasks-based Brain-Computer Interface: MLP Neural Networks vs. Fuzzy Logic

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