Defining the functionality of the human brain, our most powerful organ, has been one of the most important and enduring problems in science.
From the crude first experiments in the 20th century using needles and electrodes right up to the most modern, cutting-edge technologies, we have developed various techniques to determine how the brain operates and what controls our thought processes and emotions. These methods have obvious implications for medical research and diagnostics. However, advances in the field have also been focused on mental health, diagnostics, and treatment, as well as consumer-based applications.
Why Measure Brain Activity?
The brain is made of 86 billion neurons that are active; that is, neurons are constantly firing and connecting with other neurons. In order to answer all the questions we have about the brain, we need a system that can measure and record these patterns of activity (neural firing, changes in blood flow, etc.) that correlate with brain function. We want to know more about:
- How do memory and attention work in real time?
- What happens in the brain during sleep or meditation?
- How can abnormal activity help us detect neurological disorders like epilepsy, Alzheimer’s, or Parkinson’s disease?
In addition, the answers to these questions will enable practical applications to improve the lives of people, including more precise diagnostic devices and ultimately to technologies like brain computer interfaces for patients to control artificial limbs, etc.
The Evolution of Brain Activity Measurement
The electroencephalogram, or EEG, was one of the first tools for exploring your brain. Invented during the 1920s, the EEG measures electrical activity from your scalp. Researchers and clinicians use the records to understand what is happening within the brain. For example, information may include a person’s wakefulness, drowsiness, or whether someone is experiencing a seizure. Though records do not have specific spatial precision, EEGs are widely favored for being non-invasive, cheap, and users can see brain activities in real-time.
Since that time, other types of brain imaging have been developed, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), which help to better pinpoint the location of neural activity during various types of tasks. Each of these imaging techniques has its own strengths and constraints, making it well-suited for some types of empirical questions and clinical applications.
For readers seeking a deeper dive into specific methods, BrainAccess provides a clear overview of how you measure brain activity across several scientific techniques.
Major Techniques in Use Today
Electroencephalography (EEG)
EEG is used extensively in both clinical and research settings. Clinicians use EEG in the diagnosis of epilepsy.
Scientists use EEG to study various functions such as sleep staging, attention, and brain-computer interface.
Functional Magnetic Resonance Imaging (fMRI)
fMRI provides measures of changes in the blood oxygen level dependent (BOLD) response in the brain and gives researchers information that they use to infer which part of the brain is more involved in particular tasks. fMRI also allows for good spatial resolution, making this method preferable for research on higher-level processes such as language and emotion.
Magnetoencephalography (MEG)
MEG uses magnetic field detectors to characterize the magnetic fields produced by neural activity to further infer where and when a particular brain region is involved in the performance of a task.
Near Infrared Spectroscopy (NIRS)
A newer method, NIRS, is used to measure blood oxygenation in the brain and is a relatively new method. Light is passed through the skin and skull and is reflected back, passing through more oxygenated blood in a distinct manner. Conveniently, the equipment for NIRS is portable and non-invasive, allowing it to be used in a variety of different settings that are not purely lab-based.
The Future of Measuring the Mind
The next frontier lies in combining multiple measurement techniques for a more complete picture of brain activity. Hybrid approaches that merge EEG with fMRI or MEG, for example, could allow researchers to capture both the timing and the location of brain processes with unprecedented detail.
Artificial intelligence is also playing a growing role. Machine learning algorithms are now being trained to detect subtle patterns in brain activity that humans might miss, opening new possibilities for early diagnosis of mental and neurological conditions.
