2016 5th International Conference on Informatics, Electronics and Vision (ICIEV)
780
Real Time EEG Based Automatic Brainwave
Regulation by Music
Alamgir Hossan
A.M.Mahmud Chowdhury
Department of Applied Physics, Electronics and
Communication Engineering
University of Chittagong
Chittagong, Bangladesh
ah.apece@cu.ac.bd
Department of Applied Physics, Electronics and
Communication Engineering
University of Chittagong
Chittagong, Bangladesh
rizveemahmud@gmail.com
Abstract— In this paper, we proposed an approach to
automatically regulate the mood or brainwave of the paralyzed
or sensory impaired or psychologically sick people. A MATLAB
based algorithm has been developed to analyze the brain wave
signals obtained from a real time EEG data acquisition system
corresponding to different moods of the subject people. The
program translated the brain wave signals into command to
select and play suitable music tracks according to the state of the
brain. These music or sound tracks, selected based on the choice
of the target people, acted in turn as a feedback to continuously
adjust the subject’s mood as desired. Relaxation music,
subcontinental soft patriotic, Rabindra Sangeet & motivational
songs were used as example for relaxation, entertainment and
warm-up respectively. Sample stored EEG data of different
patients having different moods were used to verify the feasibility
of the approach and the obtained results ensured its effectiveness.
Keywords- Brainwave regulation; BCI; Real time EEG data
acquisition; Music Therapy; Neurofeedback.
I.
INTRODUCTION
Automatic brainwave regulation is an extremely useful
technique, especially for mentally disordered, completely
paralyzed or severely disabled people who cannot physically
do anything for their mental comfort unlike normal human
being. For different internal and external reasons, their mood
can change any time. Most of the time, neither they can express
their emotion to their attendant, nor always, their near people
can understand what to do for these people, how they feel,
what type of music they need to or want to listen. Further,
nowadays, it is hardly being possible for their near and dear
people to attend them all the time. They get lonely, sometimes
unhappy or restless and some other times drowsy untimely. We
have attempted to make these unlucky people get relief from
these sort of unpleasant mental states by automatically playing
time-suitable music, such as relaxation music during excited or
restless mood while motivational music for drowsy mood and
so on.
Interaction of neurons in the brain takes place in the form
of electro-chemical signals creating the brain electric and
magnetic fields. Based on the frequency and amplitude range,
these electric fields are classified into five different bandlimited signal patterns, known as the brain waves, namely delta
(δ), theta (θ), alpha (α), beta (β) and gamma (γ) in ascending
frequency range [1]. Different brain wave patterns directly
correspond to the different states of the brain. In fact, the
mood, emotions, feelings or any mental status are simply
reflection of these brainwaves [2]. These brain wave patterns
change according to one’s feeling, thought and work. Internal
and external stimuli can also significantly change these brain
waves [3]. Brain waves are sensed and transduced by placing
electrodes on the scalp. Traditionally, invasive and noninvasive
electroencephalogram (EEG) techniques have been used to
record these brainwaves for the diagnosis of some brain
affecting diseases such as epilepsy, brain tumors, mental
disorders, sleep pattern change, dementia, Alzheimer’s
Parkinson disease etc. [4]. These techniques are massive wired
and not user friendly. But with the advent of portable, costeffective, wireless EEG recording devices, such as Emotive
[5], and tremendous developments in the neuro signal
processing tools [6], it has been possible to utilize these brain
waves in real time, to directly communicate and control
external devices and machines [7], which has initiated a
potential technology called brain computer interface (BCI) [8].
Numerous BCI systems, such as EEG-based brain controlled
wheelchair, spelling device, cursor, web browser, assistive
mobile robots, vehicles, home appliances, etc. have recently
been reported in literature, especially to assist paralyzed or
severely disabled people, a detail review of these systems can
be found in [9]-[11].
In this work, we used brain waves, obtained from wireless
EEG data acquisition system and analyzed with MATLAB, to
select and play different music tracks stored in a digital device
which in turn act as an audio feedback to the subjects to
regulate their mood. We chose music as feedback keeping in
mind that music can not only adjust mood but also ease pain,
increase immune ability and has overall a great influence in
human mind [12]. A number of music tracks of different
categories, namely, relaxation, soft and motivational music
were selected corresponding to different brain wave patterns by
a MATLAB program. For example, when high frequency brain
waves, beta and gamma (excited mental state) had been found,
relaxation music tracks were played which in turn helped the
subject to gain relaxed mental state. Similarly, for low
frequency brain waves, theta (drowsiness), motivational or
high scale music tracks were played to influence the subject to
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781
warm-up or get active. In case of alpha brain wave pattern,
which correspond to the more relaxed state of the brain, soft or
melodious music tracks were played to let the subject have a
sustainable relaxed state. This process continued until a delta
brain wave pattern (deep sleep) was found when the music
player remained apparently off. Music tracks were selected
studying the choice or personal profile of the subjects and
could be changed as desired.
The rest of the paper is organized as follows. In Section II,
a brief review of brain waves and its relation with mental states
is given. In Section II, we discuss the EEG signal acquiring,
processing and buffering techniques briefly and then describe
the complete algorithm development and coding techniques.
Performance verification of the designed program is
summarized and discussed in Section III. In Section IV, we
conclude this paper discussing the significance, limitations and
future research directions of this work.
II.
BRAINWAVES AND ITS RELATION WITH HUMAN MINDS
Human brain is made up of billions of brain cells known
as neurons. These brain cells use synchronized electrical
pulses to communicate with each other. The combination of
electrical activity of the brain produce wave like cyclic
rhythms commonly known as brainwaves [13]. These waves
are detected and recorded by EEG equipment by placing a
number of electrodes on the different parts of the scalp. The
amplitude, frequency and phase of these waves depend on the
location of the electrodes [14]. For example, at the cranial
surface, the amplitude ranges from 1 to 100μV at low
frequencies while at the cerebrum, it can be 10 times stronger.
Typical brainwave signals for a normal adult are illustrated in
Fig. 1. The frequency bands are usually classified into five
categories as follows:
Delta (δ) waves
Theta (θ) waves
Alpha (α) waves
Beta (β) waves
Gamma (γ) waves



0.5-4 Hz
4-8 Hz
8-13 Hz
13-22 Hz
22-30 Hz and above
Delta (δ) waves are usually low frequency and high
amplitude signals. These types of signal are usually
observed during deep dreamless sleep and extremely
deepest meditation. When people remain in this state,
they loss total external awareness. These are also
dominant waves among infants.
Theta (θ) waves are slightly high frequency and low
amplitude than delta waves. Theta waves are usually
associated with reduced consciousness, drowsiness or
light sleep.
Alpha (α) waves are of around 10μV peak to peak
amplitude and the most stable and balanced states.
This is associated with physical and mental relaxation
but a complete awareness of surrounding. Alpha
waves help overall mind body coordination,
calmness, alertness and learning.
Figure 1. Brain waves pattern on a normul adult

Beta (β) waves are less than 20μV peak to peak and
indicate high states of wakefulness. When we remain
highly alert, focused, or get agitated, tensed, afraid,
perform calculations, we experience more beta
waves. These wave are also associated with some
mental disorders such as anxiety, insomnia, and OCD
etc. [15].
 Gamma (γ) waves are the highest frequency brain
waves and linked with highly disordered or restless
mental states.
For various reasons human mood or related brain waves
change. Normal people do a lot to regulate their brain waves
or adjust their moods naturally and unnaturally. Such, as
meditation, listening to music, watching television, relaxation
or deep breathing practice, yoga, praying etc. However,
physically and mentally disabled people cannot do these for
their mood adjustment.
III.
PROPOSED METHOD
The system we proposed, is composed of signal
acquisition, buffering, MATLAB based processing and
communication, and audio feedback blocks. A schematic of
the overall functional flow of the realtime music-based brain
wave regulation system is shown in Fig. 2.
Real time Data
Buffering
System
Real time EEG
Data
Acquisition
System
Music Player
MATLAB
Spectral
Analysis
Classification
and
External
Command
Relaxation
Music
Soft Music
Motivational
Music
Subject
Figure 2. Schematic of the proposed approach
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782
In this section, we describe the functions of these blocks
and procedure used to develop MATLAB based algorithm in a
step by step way.
A. EEG Signal Acquisition, Preprocessing and Buffering
EEG data could be collected in real time from subject’s
scalp by using different commercially available, cost-effective
user-friendly portable Neuro-headset, such as Emotive,
Xwave, Muse etc. [16]. For better resolution and higher bit
rate Emotive which collects data using 14 electrodes located at
different positions of the scalp (AF3, F7, F3, FC5, T7, P7, O1,
O2, P8, T8, FC6, F4, F8, AF4) as suggested by the American
Electroencephalographic Society(AES) [17] with a sampling
rate of 128 Hz, 0.2-45Hz bandwidth, A/D converter with 16bit resolution could be used [18]. The recorded signals are
transferred via Bluetooth. For removing artifacts resulted from
surroundings such as power line noise, acquired signal could
be preprocessed by using different filters including high pass,
low pass, band pass and notch filters [7]. These primarily
processed data could directly be interfaced by MATLAB in
real time by calling a dynamically-linked library(dll) routine.
However, it is difficult to continuously stream and process
data in real time since it requires some time for acquiring data
and computing those data. To overcome this, a buffering
software such as FieldTrip, DataSuite could be used [19].
Further, some MATLAB based efficient tools, such as
EEGLAB, BCILAB, BCI2000, OpenVibe, BioSig etc. are
widely used nowadays for these sorts of BCI applications [20].
However, in lack of real time data acquisition system, we
used sample EEG data similar to the output of these real time
systems collected from PhysioNet [21] and DEAP databases
[22]. The dataset we used were collected from six patients at
different condition for a number of short durations with a 48
channel EEG device using standard 10/20 system at 512Hz
sampling rate. We imported these data in MATLAB
workspace and processed according to the algorithm discussed
in next section, to translate these brainwave signals into
command to control the audio tracks.
B. Algorithm Development and Coding
An algorithm to implement the proposed approach was
developed as described in this section. Various samples of
typical single channel EEG data, acquired from different
people having different modes and same person for different
modes, were loaded to MATLAB for analysis by using simple
load command. The loaded data were further filtered with
Butterworth filters to remove unwanted noises induced by
powerlines, muscle activities etc. and then spectral analysis
was carried out using fft algorithm. The frequency
corresponding to the peak amplitude was found out using
simple looping and logical commands to determine which
brain wave band (δ, θ, α, β, γ) was dominant. MATLAB
audioread command was used to play music tracks from the
music library using soundcard.
Different music tracks under three main categories, namely,
relaxation, soft and motivational music, were selected
regarding subject’s taste and preferences. These tracks were
saved in wav format in the current MATLAB directory. The
number of music tracks could be changed desirably. In real
time applications, these music tracks would dynamically be
changed based on the dominant band of the subject’s
brainwaves which in turn would adjust the subject’s mood as
audio feedback. The decision boundaries were defined in the
program as depicted in the flowchart below. Fig. 3. illustrates
the flowchart of the designed algorithm and Fig. 4. depicts a
typical arrangement of music library. MATLAB version
8.5.0.197613 (R2015a) was used in 64 bit windows OS
platform.
Figure 3. Flowchart of the algorithm designed
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783
different patients of different mental states. Results for six
such sample data are summarized in TABLE I. When these
samples were loaded to the MATLAB program we developed,
it was observed that the music tracks corresponding to
dominant band were playing expectedly in the audio player as
mentioned in the table. For dominant δ band (during deep
sleep), no music was playing. In real time system, these music
tracks would act as audio feedback to the real subjects and
adjust their moods thereby. The spectral resolution of different
bands, along with the raw EEG signal of such a typical sample
of 20ms duration is illustrated in Fig. 5.
Music library
•
•
•
•
•
•
•
•
•
•
•
•
Relaxation Music
Track 1
Track 2
-----Track n
Soft Music
Track 1
Track 2
---------Track n
Motivational Music
Track 1
Track 2
---------Track n
TABLE I.
Figure 4. Arrangement of music library
IV.
PERFORMANCE EVALUATION
To test the effectiveness of the approach, relative power
spectral densities were calculated using Fast Fourier
Transform(FFT) algorithm for a large number of single
channel(F4) raw EEG segments(epochs) collected from
RELATIVE SPECTRAL DENSITIES OF BRAINWAVES
Sam
ple
δ
Wave
θ
Wave
α
Wave
β
Wave
γ
Wave
Music Played
1
5%
10%
15%
50%
20%
Relaxation
2
8%
17%
45%
20%
10%
Soft
3
10%
60%
15%
10%
5%
Motivational
4
20%
28%
30%
20%
2%
Soft
5
60%
20%
10%
9%
1%
No Music
6
15%
17%
48%
12%
8%
Soft
Figure 5. Relative Spectral densities of different brainwaves
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784
V.
DISCUSSION AND CONCLUSION
In this work, we studied an approach to adjust the mood of
different physically and mentally disabled people by music.
The feasibility and effectiveness of the approach were verified.
Not only music, any kind of entertainment, along with or
instead of music, can be selected for the library. Though we
proposed the approach for real time applications, we could not
implement it in real time because we could not afford to have
real time EEG acquisition system at the time of experiment.
However, we verified the system’s performance by offline data
which ensured the effectiveness of our approach. We continue
working to make it a fully real time system for real patients.
Our proposed system can be potentially used not only in
hospitals but also at home level for the aforementioned
patients. This approach can further be extended for automatic
mood regulation of any people in real time if the range of the
real time EEG signal acquisition system is further extended and
developed. The signals we used were collected with few
channels thus covering a small area of the brain, more realistic
brain signals collected through many channels that cover large
area would give more accurate insight of the brain and then a
more appropriate feedback could be given.
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