EEG analysis
Electroencephalography (EEG) analysis encompasses the processing, interpretation, and modeling of electrical signals generated by neuronal activity in the brain, captured non-invasively through electrodes placed on the scalp.[1] These signals reflect synchronized postsynaptic potentials from large populations of neurons, offering high temporal resolution on the order of milliseconds but limited spatial resolution due to signal attenuation by the skull and scalp.[1] Pioneered by Hans Berger in the 1920s, EEG provides a safe, portable, and cost-effective method for monitoring brain function, distinguishing it from other neuroimaging techniques like fMRI or MEG.[1] The core of EEG analysis involves several key steps to handle the inherent challenges of noisy, non-stationary signals contaminated by artifacts from eye movements, muscle activity, or environmental interference. Preprocessing typically includes filtering (e.g., bandpass 1–40 Hz), artifact removal via techniques such as independent component analysis (ICA) or wavelet transforms,[2] and segmentation into epochs for event-related analyses.[3] Feature extraction follows, employing methods like power spectral analysis (e.g., fast Fourier transform or Welch's method for frequency-domain insights), time-frequency decompositions (e.g., short-time Fourier transform or empirical mode decomposition for non-stationary dynamics), connectivity measures (e.g., phase-locking value or coherence to assess brain network interactions), and source localization (e.g., low-resolution electromagnetic tomography to estimate intracranial origins).[4] Advanced approaches increasingly integrate machine learning, including support vector machines, convolutional neural networks, and long short-term memory models, to classify patterns with high accuracy.[4] EEG analysis finds broad applications in clinical neurology and neuroscience, enabling the diagnosis and study of disorders such as epilepsy (e.g., seizure detection and focus localization), Alzheimer's disease (e.g., via spectral power alterations), and sleep disturbances.[1] In research, it elucidates cognitive processes like attention, memory, and emotion recognition, while supporting brain-computer interfaces for rehabilitation in conditions like stroke or locked-in syndrome.[4] Ongoing advancements in high-density electrode arrays and computational algorithms continue to enhance its precision, bridging gaps in spatial resolution and expanding its utility in real-time monitoring and personalized medicine.[3]Fundamentals of EEG
Signal Acquisition and Preprocessing
Electroencephalography (EEG) signal acquisition begins with the placement of electrodes on the scalp to detect electrical potentials generated by neuronal activity. The standard 10-20 international system, established by the International Federation of Societies for Electroencephalography and Clinical Neurophysiology, divides the skull into 10% or 20% intervals along anatomical landmarks such as the nasion and inion, enabling consistent electrode positioning across 19 to 21 channels for routine recordings.[5] For higher spatial resolution, high-density arrays extend to 128 or 256 channels, using geodesic or evenly spaced configurations to map brain activity more precisely, as demonstrated in systems like the Geodesic EEG System.[6][7] Hardware components essential for EEG acquisition include electrodes connected to differential amplifiers, which boost weak scalp signals (typically 1-100 μV) while rejecting common-mode noise, followed by analog-to-digital converters (ADCs) that digitize the amplified signals.[8] Sampling rates generally range from 250 to 1000 Hz to capture EEG frequencies up to 100 Hz without aliasing, with a minimum of 256 Hz recommended for clinical standards to ensure adequate temporal resolution.[8][9] Common artifacts arise during acquisition, including eye blinks that produce high-amplitude frontal deflections due to corneo-retinal dipole shifts and muscle activity (electromyogram) that introduces broadband high-frequency noise from nearby contractions.[10][11] Preprocessing prepares raw EEG data for analysis through a standardized pipeline. Initial filtering applies a high-pass filter at 0.5 Hz to remove slow drifts and DC offsets, a low-pass filter at 70 Hz to attenuate high-frequency noise, and a notch filter at 50 or 60 Hz to eliminate power-line interference.[12][13] Artifact removal often employs Independent Component Analysis (ICA), a blind source separation technique that decomposes the observed multichannel signals x into independent components y via an unmixing matrix W, such that y = Wx, by maximizing non-Gaussianity through negentropy approximation, J(y) \approx H(\mathbf{v}_\mathrm{gauss}) - H(y), where H denotes differential entropy and \mathbf{v}_\mathrm{gauss} is a Gaussian variable with the same variance as y.[14] Components corresponding to artifacts, such as eye blinks, are then subtracted from the data. Re-referencing follows, commonly to the average of all scalp electrodes (common average reference) to reduce reference bias and enhance signal-to-noise ratio.[15] Epoching segments continuous data into trial-based windows aligned to events or stimuli, typically 200-800 ms in duration, while baseline correction subtracts the mean pre-stimulus activity (e.g., -200 to 0 ms) from each epoch to normalize voltage fluctuations.[16] Specific challenges in acquisition include achieving low electrode-skin impedance, ideally below 5 kΩ, to minimize signal attenuation and noise pickup, often verified using saline or conductive paste with wet electrodes that maintain stable contact through gel-mediated ion conduction.[17] Dry electrodes, lacking gel, yield higher impedances (up to 100 kΩ) but enable quicker setup and are preferred for non-invasive applications, though they require active amplification to compensate for reduced signal quality.[18] Mobile EEG systems, advanced since the 2010s with wireless and wearable designs, facilitate ambulatory recording using compact, battery-powered headsets with integrated amplifiers, supporting real-world applications despite increased susceptibility to motion artifacts.[19][9]Key Characteristics of EEG Signals
Electroencephalography (EEG) signals represent the electrical activity generated by synchronized postsynaptic potentials in pyramidal neurons of the cerebral cortex, first recorded in humans by Hans Berger in 1924 using a galvanometer on a patient with a skull defect. Berger identified rhythmic oscillations known as alpha waves, with a frequency of approximately 10 Hz and amplitude around 100 μV, which he linked to mental activity and observed to attenuate upon eye opening. This discovery laid the foundation for EEG, evolving from qualitative visual inspection in the early decades to quantitative analysis in the 1960s, when Fourier transform techniques enabled frequency spectrum quantification for more objective assessment of brain rhythms.[20][21] A defining feature of EEG signals is their organization into distinct frequency bands, each associated with specific brain states and exhibiting characteristic amplitudes and spatial distributions across the scalp. These bands include delta (0.5–4 Hz), prominent during deep sleep and regeneration with amplitudes up to 100 μV and dominance in frontocentral regions; theta (4–7 Hz), linked to drowsiness, creativity, and meditation, also up to 100 μV and frontal in distribution, particularly in children; alpha (8–12 Hz), indicative of relaxed wakefulness with eyes closed, up to 100 μV and posteriorly dominant (occipital areas); beta (13–30 Hz), associated with active cognition and alertness, typically 10–20 μV and frontal-central; and gamma (>30 Hz, often up to 80 Hz), involved in high-level processing like sensory integration, up to 100 μV with widespread but task-dependent distribution. Overall, EEG amplitudes range from 1 to 100 μV, reflecting the weak, surface-recorded nature of these signals. The following table summarizes these bands for clarity:| Band | Frequency (Hz) | Associated States/Activities | Amplitude (μV) | Spatial Distribution |
|---|---|---|---|---|
| Delta | 0.5–4 | Deep sleep, healing | Up to 100 | Frontocentral |
| Theta | 4–7 | Drowsiness, meditation, anxiety | Up to 100 | Frontal (esp. in children) |
| Alpha | 8–12 | Relaxed wakefulness (eyes closed) | Up to 100 | Occipital/posterior |
| Beta | 13–30 | Active cognition, alertness | 10–20 | Frontal and central |
| Gamma | >30 | Sensory integration, learning | Up to 100 | Widespread, task-dependent |