An Electroencephalogram (EEG) is a non-invasive test that measures the electrical activity of the brain, generated by neurons communicating through electrical impulses. EEG analysis provides insights into brain function, both in healthy states and when neurological conditions are present. By recording these signals, healthcare providers and researchers can observe patterns that reveal the brain’s underlying workings.
How EEG Works and Basic Measurements
EEG involves placing small, flat metal discs, known as electrodes, onto a person’s scalp. These electrodes are connected by wires to an EEG machine, which acts as an amplifier to detect the tiny electrical charges produced by brain cells. The signals picked up by the electrodes are primarily generated by the collective electrical activity of pyramidal neurons in the brain’s outer layer, the cortex, near the scalp surface.
The EEG machine records these amplified signals, translating them into wavy lines or waveforms displayed on a computer screen. These waveforms represent brain waves, the synchronized electrical activities of many neurons. Brain waves are characterized by two main properties: amplitude and frequency. Amplitude refers to the strength or intensity of the electrical signal, measured in microvolts. Frequency, measured in Hertz (Hz), indicates how many wave cycles occur per second.
When brain activity increases, the EEG shows higher frequencies and lower amplitudes. Conversely, lower frequency EEG patterns correspond to diminished cortical arousal and are associated with higher amplitudes. This inverse relationship means that large amplitude, low frequency waves indicate a more synchronized brain pattern, while low amplitude, high frequency waves suggest a desynchronized pattern.
Understanding Brain Wave Frequencies
Brain wave frequencies are categorized into distinct bands, each associated with different mental states and activities. Delta waves, the slowest, range from 0.5 to 4 Hz and are observed during deep, dreamless sleep. In awake adults, the presence of delta waves can indicate brain dysfunction, though they are normal in infants and young children.
Theta waves, with a frequency range of 4 to 8 Hz, are linked to states of creativity, intuition, and deep relaxation, appearing during drowsiness or REM sleep. These waves are also associated with memory processing. Alpha waves, ranging from 8 to 12 or 14 Hz, are characteristic of a relaxed, yet awake and conscious state.
Beta waves, operating at 12 or 13 to 30 or 38 Hz, are associated with active thinking, problem-solving, and focused attention. A high amount of beta waves can indicate stress. Gamma waves, the fastest at 30 or 38 to 80 or 100+ Hz, are linked to strong mental activities such as learning, complex cognitive tasks, heightened awareness, and integrating information from different sensory inputs. These higher frequencies are involved in high-level processing.
Applications of EEG Analysis
EEG analysis is widely used across various fields to gain insights into brain function and diagnose neurological conditions. It is primarily used in diagnosing and monitoring neurological disorders, particularly epilepsy, by detecting abnormal electrical discharges and helping identify the type and origin of seizures.
EEG also plays a role in diagnosing and understanding sleep disorders such as narcolepsy, insomnia, and sleep apnea, by identifying irregularities that affect sleep quality. It is also employed in assessing brain damage from head injuries, brain tumors, strokes, and conditions like dementia or encephalopathy, by revealing changes in brain activity patterns.
In cognitive research, EEG is a valuable tool for studying processes like attention, memory, perception, and language processing. Researchers use EEG to observe how the brain responds to different stimuli. EEG-based neurofeedback training can also be used to improve cognitive function.
Another application of EEG analysis is in brain-computer interfaces (BCIs), which allow individuals to control external devices using their thoughts. These systems translate brain signals into commands for controlling various devices. EEG’s high temporal resolution and portability make it a suitable technology for developing BCIs.
Advanced Analysis: Unpacking EEG Complexity
Beyond simply measuring frequency and amplitude, advanced EEG analysis delves into “EEG complexity,” which provides a deeper understanding of brain health and cognitive states. Brain activity exhibits complex temporal fluctuations, reflecting intricate interactions among neuronal populations. Analyzing this complexity can reveal subtle changes not apparent through basic measures.
Multiscale entropy (MSE) is one advanced technique used to quantify the irregularity or unpredictability of brain signals across various time scales. This process helps assess how predictable or unpredictable brain signals are at both short and long time scales, reflecting the brain’s ability to adapt and process information.
For example, studies using MSE have shown that individuals with Alzheimer’s disease may exhibit altered complexity profiles in their EEG signals, with less complexity at smaller scales in frontal brain areas but potentially higher complexity at larger scales across the brain, which correlates with cognitive decline. This suggests that changes in brain signal complexity can serve as indicators of neurological conditions. Other advanced concepts include dynamic range, which relates to the brain’s capacity to respond to stimuli, and connectivity analysis, which examines the statistical dependencies and interactions between different brain regions over time. Analyzing these dynamic functional connections can reveal how brain networks reconfigure themselves during various cognitive tasks or in the presence of disorders.