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The language of brain waves: Do you know how EEG decodes brain electrical activity?
Electroencephalography (EEG) is an electrographic method that records spontaneous electrical activity in the brain. This technology captures the activity of neurons in the brain and is integral to the study and diagnosis of various neurological diseases.
The electrical signal of the EEG reflects the postsynaptic potentials of pyramidal neurons in the neocortex and paired cortex.
EEG is usually a non-invasive test with electrodes placed along the scalp, and a commonly used standard is the International 10-20 system. Although EEG has limited spatial resolution, it supports temporal resolution in the millisecond range, which means that EEG is able to provide immediate data related to brain activity that is unmatched by other imaging technologies such as CT or MRI.
How EEG works
The recording of an electroencephalogram involves the detection of voltage fluctuations through electrodes, resulting from underlying neuronal activity. EEG reflects the activity of cortical neurons near the electrodes, while deep structures farther away from the electrodes will not directly affect EEG readings.
A healthy person's EEG will show certain patterns of activity that are related to the individual's level of alertness.
The frequency range of EEG recording is usually between 1 and 30Hz, including alpha waves, beta waves, delta waves and theta waves. These different waveforms can provide valuable information about a person's current mental state. In a relaxed waking state, alpha waves will be prominent, and intense mental activity will show more beta waves.
Clinical Application
EEG is most commonly used clinically to diagnose epilepsy. For patients with epilepsy, standard EEG examination can confirm the condition. Although its sensitivity is only 29-55%, if abnormal neuronal discharges such as sharp waves appear in the electrogram, it is almost certain to be a case of epilepsy. This reflects the key role of EEG in modern medical diagnosis.
EEG can effectively identify the onset time and spatial evolution of epileptic seizures and can assist doctors in assessment and diagnosis.
In addition to epilepsy, EEG can also be used to diagnose various neurological diseases, including brain tumors, brain injuries, brain dysfunction, etc. In particular, it can distinguish epileptic seizures from other types of neurological and non-neurological events, which is important for confirming the diagnosis.
Research purposes
In the research field, EEG is widely used in neuroscience, cognitive psychology and psychophysiological research to explore various psychological and physiological functions. The combination of EEG and event-related potentials (ERPs) provides profound insights into how the human brain processes information.
Research has found that certain EEG patterns can show how the brain processes information under different situations and are crucial to understanding mental states.
EEG technology continues to evolve, and advances in data analysis methods allow us to extract data from each test more efficiently, which has shown remarkable results in recent studies, such as detecting neurological symptoms after concussion. Physiological changes.
Advantages and Challenges
Although EEG has relatively low spatial resolution, its price is relatively low, the equipment is portable, and it can be used in various environments, which are unique advantages that other imaging technologies cannot provide. However, EEG has a low signal-to-noise ratio, so complex data analysis methods are required to extract useful information.
EEG-based research technology is constantly innovating, such as combining machine learning technology to analyze psychological states and emotional reactions.
In the near future, EEG may further enhance its status in clinical diagnosis and research. With the emergence of new technologies and analysis methods, we may be able to gain a deeper understanding of how the brain operates and behavioral responses. This raises a new question: If brain electrical activity could be more accurately decoded, could we predict changes in human thoughts and emotions in a meaningful way?
