International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
A Review of Brain Computer Interface
Ms Priyanka D. Girase1, Prof. M. P. Deshmukh2
1
ME-IInd (Digital Electronics), 2 Prof. in Electronics and Telecommunication Department
1,2
S.S.B.Ts C.O.E.T.Bambhori, Jalgaon,
North Maharashtra University, Jalgaon- 425001, Maharashtra (India)
Abstract: A brain-computer interface (BCI)is a
direct neural interface between a human or animal
brain and an external world. It is a hardware and
software communications system which provides a
new non-muscular channel for communication and
control with the external world. Brain computer
interfaces (BCIs) are divided into two main
approaches: the EEG pattern recognition approach
based on different mental tasks and the operant
conditioning approach based on the self-regulation of
the EEG response. This paper focus on basic idea of
BCI and reviews numerous BCI technologies
depending upon operations such as signal acquisition,
pre-processing or signal enhancement, feature
extraction, classification and the control interface.
Here machine application is controlled
according to the thoughts of the affected
individual and hence the brain activity is
monitored. For this various techniques are
available that includes[10], for example,
functionalMagnetic Resonance Imaging
(fMRI), magnetoencephalography (MEG),
Positron Emission Tomography (PET),
Single
Photon
Emission
Computer
Tomography (SPECT), optical brain
imaging, single neuron recording (with
microelectrodes) and electroencephalography
(EEG) .
From these methods, MEG, EEG and
Keywords— BCI (Brain Computer Interface), EEG
single neuron recording give continuous and
(Electroencephalography),ERD,ERS
instantaneous recordings of the brain activity
(time resolution about 1 ms), which is
I. INTRODUCTION
required for real-time BCI. However, MEG
In the first international meeting which is
is not practical to be used with BCI. The
devoted to BCI research held in June 1999 at
MEG measurements are made using a large
the Rensselaerville Institute near Albany,
device inside a magnetic shielded room. The
New York, brain computer interface was
single neuron recording, on the other hand,
defined as “A brain computer interface is a
requires that the electrodes are inserted inside
communication system that does not depend
the skull. Therefore, almost all of BCIs
on the brains normal output pathways of
reported to date have been based on EEG.
peripheral nerves and muscles”[9]. It is also
called as Brain Machine Interface (BMI), or
II. ELECTROENCEPHALOGRAPHY (EEG)
often called a Mind-Machine Interface
Electroencephalography (EEG) is a
(MMI), or sometimes called a direct neural
method
used to measure the electrical
interface which is able to detect the user’s
wishes and commands while the user remains activityof the brain caused by the flow of
silent and immobilized. There exist various electric currents during synaptic excitations
diseases of the nervous system that gradually of the dendrites in the neurons and is
cause the body’s motor neurons to extremely sensitive to the effects of
degenerate, Example: Amyotrophic Lateral secondary currents [10]. It measures the
Sclerosis (ALS), brain stem stroke, or spinal combined electrical activity of millions of
cord injury. Eventually causes total paralysis nerve cells called neurons. It is most widely
and the affected individual becomes trapped used neuroimaging modality since it has high
in his own body, unable to communicate. A temporal resolution, relative low cost, high
Brain-Computer
Interface
enables portability, and few risks to the users. EEG
communication under such circumstances.It signals are recorded using the electrodes
introduces new communication link between placed on the scalp. The signals have to cross
the affected individual and externalworld. the scalp, skull, and many other layers and it
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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
provides very poor quality signals. This
means that EEG signals in the electrodes are
weak, hard to acquire and of poor quality.
This brain signals are severely affected by
background noise generated either inside the
brain or externally over the scalp. There exist
various properties in EEG [9], which can be
used as a basis of classification of the brain
signals for a BCI as:
A. Rhythmic Brain Activity
Depending on the level of consciousness,
normal people’s brain waves show different
rhythmic activity. For instance, the different
sleep stages can be seen in EEG.Different
rhythmic waves also occur during the waking
state. These rhythms are affected by different
actions and thoughts, for example the
planning of a movement can block or
attenuate a particular rhythm. The fact that
mere thoughts affect the brain rhythms can
be used as the basis for the BCI.The EEG can
be divided into several frequency ranges.
Delta, theta, alpha, beta and gamma are the
names of the different EEG frequency bands
which is related to various brain states.In fig
1 the EEG waves for different frequency
ranges are shown.
So EEG is described in terms of frequency
band in which each rhythm has some
specified range of frequency as listed in table
I
Table IList of EEG Rhythms with frequency
range
Rhythm
Delta (δ)
Theta (Ɵ)
Alpha ( ά)
Beta (ß)
Gamma (γ)
Mu (µ)
Frequency(Hz)
0.1 – 3.5
4 - 7.5
8 – 13
13 – 30
30 – 100
Around 10
B. Event-Related Potentials (ERPs)
Event-related potentials are a common title
for the potential changes in the EEG that
occur in response to a particular “event” or a
stimulus. These changes are so small that in
orderto reveal them, EEG samples have to be
averaged over many repetitions. This
removes the “random” fluctuations of the
EEG, which are not stimulus-locked.
Event-related potentials can be divided into
exogenous and endogenous. Exogenous
ERPs occur up to about 100 ms after the
stimulus onset. They depend on the
properties of physical stimulus (intensity,
loudness etc.). The potentials from 100 ms
onward are called endogenous. They depend
largely on psychological and behavioural
processes related to the event.
3. Event-Related Desynchronization (ERD) and
Event-Related Synchronization (ERS)
Fig.1 EEG Waves over different Frequency
Ranges
ISSN: 2231-5381
ERD is an amplitude attenuation of a
certain EEG rhythm. ERS is an amplitude
enhancement of a certain EEG rhythm.The
imagination of motor tasks produce
measurable changes on the on-going EEG
and represents frequency specific changes of
the which can either be an increase in power
(ERS) or a decrease in power (ERD).
Traditionally, ERD is measured through the
estimation of EEG signal power in specific
frequency bands. So a certain motor task
induces ERD over the corresponding cortical
area while there is ERS in unrelated areas.
The ERD/ERS is the fundamental
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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
physiological property which can be used as translate these features into appropriate
a basis of classification of mental tasks in a device commands to take particular action.
BCI system based on motor tasks.
The application machine can be wheelchair,
robotic arm, cursor, speller or any other
III. BRAIN COMPUTER INTERFACE
device as shown in fig 2.
Brain Computer Interface (BCI) [1],
technology is a new and fast evolving field
that measures the specific features of brain
activity and translates them into device
control signals. Signal is acquired using the
electrodes on the scalp. These signals are
weak hence amplified and are converted into
digital form. Then features are extracted from
amplified and digitized version of EEG
signals in the signal processing stage.In this
stage useful EEG data is separated from
noise. The modern BCIs often use several
types of feature extraction such as Hjorth
Fig. 2 Brain Computer Interface
parameters wavelet transforms, Fourier
transforms, and various other types of filters.
But there is no best way of extracting
features from EEG data.The algorithms
III. LITERATURE REVIEW
Sr.
No.
Paper Title
/ Reference
EEG signal
Extraction method
Approach & Concept about Work
Results of Works
1
Parallel
manmachine training
in development of
EEG based cursor
control [11].
EEG
data
recorded using 28
electrodes
arranged
according to the
international 1020
electrode
system.
Based on the pattern recognition approach.
Signal amplification, initial filtering done by
Brain Imager, a device manufactured by
Neuroscience Inc. Features were extracted for
electrodes C3, C4, P3 and P4 using the 4th
order autoregressive (AR) feature extraction
method. The EEG patterns were classified with
an adaptive neural network.
Only two subjects are
train to achieve 2-D
cursor control. They
achieved the hit rates
of 70 % and 85%,
respectively.
Once
subjects
are
fully
trained they can hit the
target close to 100%.
2
Experiments with
an EEG based
computer interface
[12].
EEG
recorded
from one bipolar
channel with two
electrodes located
3 cm behind C3
and C4 of the
international 1020 system
Hard and soft rejection
improves classification
accuracy.
In
hard
rejection 21% of data
blocks were entirely
rejected.
In
soft
rejection
method,
anaverage of 34 % of
the data samples was
rejected.
3
Current trends in
Graz
braincomputer interface
research [13].
The EEG was
recorded with 29
gold electrodes of
which
ground
electrode
was
placed on the
forehead
Based on the pattern recognition approach,8th
order autoregressive (AR) model is used. The
upward movement was associated with the
math task and downward movement with motor
imagery. Two methods were a latent-space
smoothing means that the low certainty
decisions may be rejected by the higher
certainty decisions and a reject option means if
the certainty of the classification did not exceed
a particular threshold then the EEG was
classified to “reject” class.
It is based on the classification of the EEG
patterns during five different mental tasks using
detection of the ERD and the ERS patterns
during the motor imagery
ISSN: 2231-5381
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Classification accuracy
decreased steadily with
an increasing number
of classes N with all
subjects.
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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
4
Non-invasive
Brain-Actuated
Control
of
a
Mobile Robot by
Human EEG [2].
5
Brain-Computer
Interface: Next
Generation
Thought
Controlled
Distributed Video
Game
Development
Platform [1].
6
P300 brain
computer
interface: current
challenges and
emerging trends
[7].
7
EEG-based
asynchronous BCI
control of a car in
3D virtual reality
environments [8].
EEG potentials, at
the 8 standard
fronto-centroparietal locations
F3, F4, C3, Cz,
C4, P3, Pz, and
P4. The sampling
rate was 128 Hz
Signals
were
acquired
from
three
electrode
sites C3, C4 &Cz,
unipolar
signal
was
recorded
using left ear as a
reference and the
right ear as the
ground.
EEG signal is
acquired
using
electrodes
cap
placed on scalp
and filtered with a
with a low cut off
frequency of 0.1
Hz and high cut
off frequency of
30 Hz
Asynchronous
approach
hence
robot
continuesto execute a high-level command until
the next is received. The different mental tasks
are recognized by a statistical classifier. The
robot executes these commands autonomously
using the readings of its on-board sensors.
Robot executes commands with six different
mental states or behaviours.
Right and left movements were controlled by
using a differential Mu over both brain
hemispheres as the bases for the classification
that is the Mu power difference over electrode
locations C4, C3. BCI2000 acts as a platform
for signal acquisition and processing which
provides a way to directly communicate with an
external device through UDP. The user
application module was driven using control
signals from signal processing.
This closed-loop BCI approach relies on the
P300 and other components of the ERP, based
on an oddball paradigm presented to the
subject. The P300 is the largest ERP component
and generated during an oddball paradigm.
Asynchronous
EEG-based BCI,
Signal is obtained
from the sensors
or
electrodes
placed on scalp
Cumulative Incremental Control strategy is
used to control duration of Non-Control and
Intentional Control states (Motor Imagery (MI)
for left, right hand, foot and etc.). This method
calculates the duration of ERD/ERS states
which is different with common system that
only detects the existence of ERD/ERS states.
CONCLUSION
A BMI allows a person to communicate
with or control the external world without
using the brain’s normal output pathways
of peripheral nerves and muscles. The
application is controlled by thoughts or
wishes only for which brain activity is
monitored continuously. In this paper
seven EEG-based brain computer interface
systems were reviewed and compared. In
all BCI system the neural activity is
recorded using non-invasive technique.
There are many challenges in the future of
the BCI field. An exhaustive research
about the mental tasks should be done so
that applications could be improved.
ISSN: 2231-5381
The task drive the
robot through different
rooms in a house like
environment
using
asynchronous
EEG
analysis and machine
learning
techniques
with ratio of 0.74%.
The participants played
the video game by
using their thoughts
only with up to 80%
accuracy
over
controlling the target
Close
to
72.8%
subjects were able to
spell
with
100%
accuracy in the RC
paradigm and 55.3%
spelled with 100%
accuracy in the SC
paradigm. Less than
3% of the subjects did
not spell any character
correctly.
Speed and steering
angle controlled by
type and duration MI.
Best accuracy obtained
76% for 1 sec MI time
and 91% for 4 sec MI
time
ACKNOWLEDGMENT
Priyanka Devendrasing Girase
has recieved her B.E. graduation
degree in Electronics and
Telecommunication in 2013 and
now pursuing M.E. degree in
Digital Electronics from SSBT’s
COET Bambhori, Jalgaon.
Manish P. Deshmukh has received his B.E. degree
in Electronics from Amaravati in 1989 and
Master’s Degree in Control and Instrumentation
from MNREC, Allahabad in 1997. He has
completed his PhD in Electronics and
Telecommunication from North Maharashtra
University, Jalgaon in 2014.Presently, he is
working as a Professor in the Department of
Electronics and Telecommunication Engineering at
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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
SSBTs COET Bambhori, Jalgaon. He has
published 04 research papers in National and
International Journals. His interests include Digital
Image Processing and Solid state devices.
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