Review paper on Brainwave Electronics
Volumn 2

Review paper on Brainwave Electronics

Kalyani Damle                                                        

Department of Electronics and Telecommunication                      

Jhulelal Institute of Technology  Nagpur, India.                                                         

Smriti Shriwas  

 Department of Electronics and Telecommunication 

Jhulelal Institute of Technology Nagpur, India.   

Prof. Anil Bawaskar                                                          

Department of Electronics and Telecommunication                      

Jhulelal Institute of Technology Nagpur, India                                           

 Prof. Swaroop Gandewar      

Department of Electronics and Telecommunication                                          

Jhulelal Institute of Technology Nagpur, India. 


There always is an ever-increasing demand for economical, effective and quality healthcare appliances and clinical devices. To achieve this, many biomedical manufacturers and scientist work for the improvement of existing devices as well as innovating new technologies which give optimum results.  This paper discusses how the signals are generated from the brain (EEG) and used to drive various applications. The basic elements of any BCI system also called as brain-machine interface (BMI) and portable designs are also discussed herein.

Keywords: Brain computer interface (BCI), Electroencephalography (EEG).


The brain is considered to be the most active part of the body. It consists of a network, of millions of cells called as neurons. These neurons communicate with each other and thereby produce an electrical impulse of very few micro volts. These brainwaves, clinically termed as Electroencephalography (EEG) readings are the result of the firing of neurons when stimulated and can be acquired from the scalp using sensors. Hans Berger (1873-1941) discovered the existence of human EEG signals.

EEG consists of frequencies present mainly between the ranges of 0-30 Hz. classified as- Delta, Theta, Alpha, and Beta.

Delta:-Delta waves are the slowest of the brainwaves which are recorded between the ranges of 0-4Hz.They are associated with deep dreamless sleep and deep relaxation. Abnormal delta activity indicates that the person has learning disorders and inability to think with conscious awareness.

Theta-Theta waves are found in the range of 4-8Hz.They are associated with daydreaming, problem solving abilities and creativity. Abnormal theta activity shows depression, hyperactivity and ADHD.

Alpha:-Alpha waves range from 9-14Hz and are mainly produced by occipital lobes. It shows a calm and meditative mind with creative visualization abilities. Low alpha levels results in anxiety, stress and insomnia.

Beta:-Beta waves are considered as the fastest waves ranging from 15-30Hz.They are mainly involved in logical thinking, high concentration and focus. People exhibit these waves most prominently throughout the day like, when reading, writing and talking.

Gamma:-These waves exist in the range of 38-90Hz.It exhibits when passing information rapidly and simultaneous processing the data from all the parts of the brain.


The BCI System:- 

To acquire these brainwaves, special sensors are used which can detect the small variations in the signal frequencies. These sensors are placed along the scalp in a standard pattern named as 10-20 system.

Fig 1:10-20 International system.

This 10-20 system was designed to observe and study the EEG patterns on the cerebral cortex. It provided a standardized platform where EEG tests could be compared on any subject. This system depicts the placement of electrodes on the skull is either 10% or 20% of the total front-to-back or right-to left area.

The alphabets F, P, T, O denotes the different lobes of the brain i.e. Frontal, Parietal, Temporal and Occipital. All the even numbered electrodes are placed on the right hemisphere whereas the all the odd numbered ones are placed on the left hemisphere of the brain. Nasion is present just above the nose and in between the eyes. This point is used as Ground electrode position.  Inion is present at the lowest point on the skull where a small bump is found. The earlobes are usually used as reference electrode positions. The alphabet ‘C’ is used to depict the central line on the brain but it is not a lobe.

The main objective of a BCI system is to detect the brainwaves, manipulate them and generate control signals for external devices. For any BCI system to exist following stages are required:-

Figure 1: Shows the block diagram of a BCI

Signal Acquisition:-

The first stage would be to acquire the signal from the scalp. For this, we use electrodes. Electrodes can be invasive or non-invasive. In invasive type, needle shaped electrodes are used which can be surgically inserted deep inside the brain or in the upper layers of scalp. In non-invasive type, electrodes are placed on the surface of the scalp to detect the neural activity. The materials used to make these electrodes are gold, silver, tin, platinum and silver/silver chloride. Ag/AgCl is a popular choice of biological electrodes due to its low half-cell potential and low impedance of the electrode by silver chloride.


After the signals are acquired from the brain through electrodes, signal processing is required. As the brainwaves are of very few micro volts (50-200uv), it needs to be amplified (10,000 times) in order to boost the signal to a usable level. Due to the circuit requirements such as low noise, high gain accuracy, high input impedance, low offset voltage, low power and high common mode rejection ratio precision instrumentation amplifiers (INA333, AD620 etc) are used. Instrumentation amplifier is a kind of differential amplifier with additional input buffer stages which makes it easy for impedance matching of the amplifier with the preceding stage.


The next stage after amplification includes the filtering of the signal in order to get the brainwaves in the desired frequency range. For this the signal needs to go through a series of filters such as HPF, LPF and notch filter each having its own significance. High pass filter with a cut off frequency of 0.5-1Hz is used for avoiding signals of very low frequencies. It also reduces the DC offset caused due to electrode mismatch and the disturbance occurs due to breathing.

Low pass filters on the other hand is used to attenuate the high frequency signals. On the other hand the main reason to use notch filter is to reduce the 50Hz noise obtain from power supply.

Analog to Digital Conversion:-

After the signal is filtered it is in the analog form, this analog signal is converted to digital with the help of an ADC. Analog to digital conversion helps in reducing the noise being added in the input signal. It consists of sampling, quantization and encoding blocks each having a special feature. Sampling is a process of selectively picking up signals at equal time intervals on the basis of Nyquist criteria. These sampled signals are then quantized into a rounded off integer value of the nearest step size. Encoder block encodes these quantized values into bit streams which can be transmitted over a channel.

Wired/Wireless Communication:-

Connecting the whole circuitry to a computer, laptop or any device from which the application is to be run is based on wired/wireless path. Wired communication can be done by using connecting wires between the designed PCB and the CRO, Laptop on which the output response can be observed. USB to serial cable is being used extensively for these purposes now a days. Here the bare wire noise is also added to the circuit noise.

Wireless transmission can be achieved by using RF transmitters and receivers, Bluetooth modules, Wifi. Wireless transmission is done in the digital format so that noise is reduced. Using wireless protocols, the device becomes portable and such devices are very useful for emergency medical services.

Application on Laptop/PC:-

Applications consist of reading the real time data from the EEG headset, sampling it, processing it further to reduce noise levels and finally showing the desired waveforms. These waveforms can be used for coding programs in application specific devices or to run any gadget directly. Applications made so far are discussed later in this paper.

Portable designs:-

As discussed earlier, wireless BCI systems facilitate portability of the device. The device can work in a specific range as provided by the manufacturer of various systems. The top-left image shows a headset developed by        NeuroSky Inc. It has all the blocks of a BCI structure inbuilt into the headset. The second-right image shows how a BCI controlled robotic arm is tying the words on the screen by interpreting the EEG signals obtained by the headgear. The bottom left image shows a dry electrode 64-channel BCI headset developed by Cognionics. The bottom-right image shows a 14-channel Emotive Epoc headset.

Description: robotic_bci_wheelchair.jpg
Description: dry-electrode-BCI-headset.jpg
Description: NeuroSky-MindWave-Mobile-1-544x408.jpg
Description: section5-epoc.png
Figure 2:Shows the portable BCI headgear devices.


Sleeping disorders, cognitive disabilities, ADHD, schizophrenia can be detected in early stages using EEG. To improve their cognitive conditions, BCI neuro-feedback systems can be used.

BCI has evolved from clinical practices to a wide range of consumer based product and user friendly applications mainly to assist the disabled persons in their day to day activities and improving their quality of life leading to cost saving on attendant services and rendering the person to be self reliant.

Assisting drivers in vehicles like cars and trucks can reduce the road accidents tremendously by using BCI based devices to keep in check the attentiveness and the drowsiness of the driver. A research team from University of Arizona developed a portable brainwave monitor which could detect the drowsiness of the driver. Such assistive applications can ensure user safety and ease. A research team from Nankai University in Tianjin developed the first brain-controlled car.

Yet another application of BCI has found its way in FES (functional electrical stimulation) based systems where the spinal injuries are connected to a set of electronic implants controlled by the signals produced by paralyzed person’s thoughts. The researchers at the University of California-Irvine worked on the project where the subject, Adam Fritz suffered from spinal cord injury (SCI) rendering him with no motor functioning from the lower extremities. He was able to use his brain impulses to stimulate and move his legs.

Similarly various applications using FES and bionics have come forth proving a great scope of BCI based controlling of one’s own injured/disabled body part.

The Brain-Computer-Music-Interface (BCMI) enables the user to create music just with the brain impulses and eye movements. This was created by Eduardo Miranda and U.K’s University of Plymouth BCMI project.

University of Washington researcher Rajesh Rao controlled his colleague  Andrea Stocco’s finger movement in the first brain-to-brain interface. Rao sent his brain signals over the internet and made Andrea’s finger move involuntarily.

BCI devices are greatly used in games and entertainment purposes. Virtual and 3-D games have also evolved with the interface of BCI. Mindflex, Emotive Epoc, and Neurosky headgears can be used for these applications where the user can make decisions directly from the brain impulses rather than handling control devices manually.


Thus we have discussed the recent technological development in the BCI systems during the past 20 years. More advanced BCI systems are being evolved with the passing years and what brain can think can now be made a reality.


  1. University of California-Irvine project on Paralysed person’s movements.
  2. Music created by Eduardo Miranda and U.K’s University of Plymouth BCMI project.
  3. Rutgers State University of New Jersey’s project on Brain Wave Music Synthesizer.           MIDI Basics. <>.
  4. S. Saeid and C. Jonathon. EEG Signal Processing. s.l.: John Wiley and Sons,  2007.
  5. J. Millan, et al., “Noninvasive brain-actuated control of a mobile robot by human EEG,” Biomedical Engineering, IEEE Transactions on, vol. 51, pp. 1026-1033, 2004.
  6. NeuroSky—Brainwave Sensors for Everybody Available online:


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