How were brainwaves visualized by researchers? — a sneak peek!

The brain is considered as the CPU of the human body. It makes sense out of our interactions with the outside world. There’s another fact about the brain: It generates numerous tiny electrical impulses every millisecond. These impulses can be compared to that of a wave, a wave streak, to be precise. We call these streaks brainwaves, and this article will give you a pretty modest understanding of these. I hope you don’t mind reading on.

Brainwaves are periodic neural oscillations in the central nervous system of the human body. The oscillations are essentially fluctuations in electric potential across the membranes of neurons, which are the brain cells. Brainwaves are generated either by mechanisms within a neuron or by synchronization of several neurons oscillating through feedback connections present in them. These oscillatory activities, which are essentially electrical impulses that travel along neurons at speeds ranging from a sluggish 0.5 to a blazing 120 m/s is how the human brain communicates with the body, with the nervous system being the medium. The complex network of electrical impulses in the brain leads to the formation of an electric field that envelops and influences the functioning of the brain, especially during deep sleep.

The study of such neural oscillations is called electroencephalography (EEG), and it is of fair importance because of the potential applications of brainwaves, such as Brain-Computer Interfaces (BCI) and clinical biomarkers.

Brainwaves can exist in many forms, such as spike trains, local field potentials and large-scale oscillations, and are linked to cognitive functions such as perception, memory, motor control and information transfer. They are also classified on the basis of the spectral content:-

• Delta Wave (0.5–4 Hz) — The slowest brain-wave in the list. Delta Waves are generated during stage 3 NREM (Non Rapid Eye Movement) sleep, also called deep sleep. This is where the person is in a complete and dreamless sleep. Apart from being slow, these waves also have the largest amplitude among all brainwaves. These waves facilitate healing, growth and regeneration in the human body, and that’s a reason why we are advised to have plenty of sleep. So remember, it’s beneficial for you if you sleep during classes.
• Theta Wave (4–8 Hz) — These brainwaves are faster in speed and lesser in amplitude than Delta Waves. These waves are dominant during drowsy, hypnotic, meditative, or sleeping states, not during the deepest stages of sleep (REM sleep, for example). Theta waves stimulate vision and deep memories, and augments the person’s learning and creativity.
• Alpha Wave (8–12 Hz) — These brainwaves are faster and softer than Theta Waves. They are also called ‘Berger’ Waves, in honor of Hans Berger, as EEG was his brainchild. Alpha waves are dominant during quietly flowing thoughts, and in some meditative states, or during wakeful relaxation with closed eyes. These waves help the person to experience relaxation, and signify the resting state of the brain.
• Beta Wave (12–30 Hz) — Beta Waves come in between Alpha and Gamma Waves. These waves are normally associated with waking consciousness. They are present during our normal cognitive activities and our interaction with the outside world. Beta waves are classified into three waves on the basis of their frequencies:
• Low Beta Waves (12–16 Hz) — Dominant during ‘fast idleness’ or a state of musing.
• Beta Waves (16–20 Hz) — Dominant during high engagement of the brain or while actively figuring something out.
• High Beta Waves (20–30 Hz) — Dominant during highly complex thought, anxiety or excitement.

Prolonged high frequency processing is not a very efficient way to run the brain as tremendous amounts of energy is expended in such processes.

• Gamma Wave (25–100 Hz) — The fastest and softest brain wave. It is speculated that these waves are dominant during states of expanded consciousness and spiritual awareness. They are also induced when the brain is processing multiple streams of information from different sources, especially during fast and uninterrupted processing.

Cramming and pulling all-nighters before exams is a sure-shot way of flooding your mind with Gamma Waves.

I see you’ve come this far reading about brainwaves. Please try not to change your mind as we’re about to have a sneak peek into how these subtle waves are observed. Now we come to the question, how are these brainwaves observed and studied? We employ EEG to suit our purposes and we shall now see how it works.

Electro-Encephalo-Graphy (EEG) is a non-invasive test that records electrical patterns in the human brain. It is primarily used to detect and diagnose epilepsy, sleep disorders, coma, and brain death. It is also employed as a first-line diagnosis method to record tumors, or other focal brain conditions.

A typical EEG scan is carried out by placing electrodes over the scalp, with each electrode placed on a specified location. The scalp area is prepared by light abrasion, so as to remove any impedance caused by dead skin cells (unless your head shines brighter than your future).

A typical EEG setup looks like this.

Then, a conductive gel or paste is applied on the spot where the electrode has to be attached. This way, around 20 electrodes are attached in a configuration called 10-20 electrode system(additional electrodes can be added, depending on the situation), of which a few are configured to be ground and system reference electrodes. Each electrode is connected to an amplifier, while the reference is connected to the other. The output of the amplifier is digitized using an analog-digital converter (ADC) and other signal processing components and is displayed and stored electronically.

EEG signals are extremely weak and affected by different types of noises and impairments that need to be carefully eliminated. To remove the power-line noise of the signal recording device, notch filter is used. A considerable amount of voltage is generated from the galvanic skin response across the head. To filter the same, we may use a high pass filter to attenuate frequencies below 5 Hz.

Here, take a look at this TED video(hyperlinked), where the computer is learning to read human brain and predict what exactly the mind is able to comprehend from the picture that the person sees. You could get an insight on the process of collecting EEG signal, which is a temporal data and the importance that must be given for the proper classification of real-time signals.

Temporal data is data that varies over time. A temporal data denotes the evolution of an object characteristic over a period of time.

EEG has one major limitation, which is its pathetic spatial resolution.

Spatial resolution means how precisely the EEG at one particular electrode “matches” the activity being generated below it, in the cortex. Since EEG is electricity, it mixes and spreads in the gel-like substrate of the brain.

EEG signals are complex to extract information out of them. With the advent of latest technologies and better algorithms, we can apply complex automatic processing algorithms that allow us to extract ‘hidden’ information/features from EEG signals. There are several techniques such as time domain features which are mean, standard deviation, entropy, root mean square of the signal etc. that can be extracted from the spatio-temporal data. In addition to time-domain features, frequency domain features can be obtained by applying wavelets and synchronization features such as coherence, correlation etc. could also be extracted.

We can then apply Independent Component Analysis or Principle Component Analysis over the signal to decompose the data and to reverse the superposition by separating the EEG into mutually independent scalp maps.

High-resolution anatomical imaging techniques such as Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT) are replacing EEG techniques nowadays, but the latter is still widely used because of its millisecond-range temporal resolution, something which the former two techniques fail to provide. Other methods of looking at brain activity, such as PET(Positron Emission Tomography) and MRI have time resolution between seconds and minutes.

EEG measures the brain’s electrical activity directly, while other methods record changes in blood flow (e.g., SPECT, fMRI) or metabolic activity (e.g., PET), which are indirect markers of brain electrical activity.

Neural oscillations and EEG techniques are being widely researched to implement Brain-Computer Interfaces (BCI), which are direct communication pathways between an extended or wired brain and an external device. BCIs are expected to enable motor recovery, functional brain mapping and most importantly, though-controlled devices. BCIs are integral to cybernetics and bionics, where physically disabled humans can control prosthetic devices by thought.

Applications of brainwaves.

Other applications of Brainwaves and EEG include:

• Creation of music — Electroencephalography (EEG) headwear devices record the electric signals that are produced when the brain is at work and can connect them wirelessly to a computer. To make music, such thoughts are associated with notes or sounds to create a language of musical thought that’s produced directly from the brain. With this established, users can simply think musical scores to life and play them via the computer. The University of Michigan has developed the MiND ensemble (Music in Neural Dimensions) to enable the same.
• Building 3-D Objects — In 2012, George Laskowsky, the CTO of a Chilean startup, Thinker Thing, created the world’s first thought-generated object using an Emotiv EPOC EEG headset. The headset maps the user’s brainwaves and sends them to Emotional Evolutionary Design, the company’s own software, to display “building-block” shapes on a screen. From a basic beginning, the shapes change and “evolve,” while the user’s emotional positive and negative reactions to each change are monitored by the headset. As the software processes brain feedback, the well-received shapes and changes are kept and expanded, while the disliked ones fade away. The process is repeated until a final object is produced according to the thought preferences of the designer. The company’s Monster Dreamer project gave schoolkids the opportunity to use the software to create the monster of their dreams in less time.

Building 3D objects using EEG signals.

• Driving a wheelchair — In 2009, Japanese scientists at Toyota and research lab RIKEN announced a thought-controlled wheelchair that used an EEG sensor cap to capture brainwaves and turn them into directional commands in just 125 thousandths of a second — with 95 percent accuracy. This is extremely useful and convenient for physically handicapped beings with healthy and functioning minds. The Free University of Berlin, Germany, has attempted to develop a car that drives according to human thoughts. The car, a Volkswagen Passat, was fitted with Emotiv’s commercially available EEG Scanner. Drivers were trained to produce recognizable thought commands by manipulating a virtual cube on-screen, which was translated to commands interpretable by the car.

A mind controlled car by Volkswagen — blog.al.com

Apart from the ones mentioned, there are numerous other applications of brainwaves, and a decent chunk of them are clinical, but the day isn’t too far when we can sit down and carry out all the day’s work just by thought.

For people interested to pursue work on Neuroscience, you could have a look on Emotiv, Biosemi devices and Neurosky, the pioneers in the making of brain monitoring devices. For Signal Processing Enthusiasts in the field of Neuroscience, MATLAB offers you a toolbox called EEGLAB for performing various processing techniques on the signals acquired from the brain via the brain monitoring devices. OpenViBE is a cloud software platform for designing, testing and using brain computer interfaces.

Presently, researchers are trying out various Machine Learning and Deep Learning algorithms such as Support Vector Machine, Convolutional Neural Networks, Recurrent Neural Networks and Capsule Networks over DEAP dataset, which contains physiological EEG Signals. You could perhaps have a try on the dataset with various deep learning models for classification of various emotions.

For further references on Computational Neuroscience, please check on the hyperlinks that are provided below:

1. Basic EEG paradigms — Steady State Visually Evoked Potential,
2. P300,
3. Motor Imagery,
4. Motor Imagery Data(You can search for many types of open databases in the internet for other paradigms as well),
5. EEG processing tools for Matlab (EEGlab) — (Ideal for offline signal processing),
6. OpenViBE— (Ideal for online signal processing/classification/machine learning),
7. Cykit — A python library/open source project that can be used to extract raw data from Emotiv Device used in the lab.
8. DEAP dataset — A Dataset for Emotion Analysis using EEG, Physiological and video signals.

Now, that’s some mind-boggling stuff you just heard!

- Researched and written by Tushar Sairam.

Additional information and resources by Venkatesh Bharadwaj. S