- Email: [email protected]
Electroencephalography
Electroencephalography S A Keenan, The School of Sleep Medicine, Inc., Palo Alto, CA, USA O Carrillo, Stanford University, Stanford, CA, USA H Casseres, The School of Sleep Medicine, Inc., Palo Alto, CA, USA ã 2013 Elsevier Inc. All rights reserved.
Glossary Alpha activity: Electroencephalogram (EEG) activity with frequencies of 8–13 Hz; prominent in the occipital lead. Beta activity: EEG activity with low amplitude and high frequencies (>13 Hz). Bipolar derivation: A derivation in which grid 1 and grid 2 of a differential amplifier both receive input from active electrodes (i.e., both record brain activity or both record muscle activity). Compare to monopolar (referential) derivation. Delta activity: EEG activity with frequencies less than 4 Hz. Derivation: Specific input into any one differential amplifier (e.g., C3-M2). Differential amplifier: An amplifier, commonly used in electrophysiology, in which there are two inputs (e.g., C3 as input 1 and M2 as input 2) and the output is the difference between the two. Electrode popping: An EEG artifact that occurs when the voltage across amplifier exceeds the limits of the display for that channel; occurs commonly when an electrode placement is compromised. Event-related potential: An electrophysiological response, measurable by EEG, that occurs in response to a stimulus (e.g., the brainstem auditory evoked response [BAER]). Also called evoked response. Gamma activity: EEG activity with frequencies greater than 35 Hz. High-frequency filter (HFF): A filter that systematically reduces the amplitude of high-frequency activity. Inputs 1 and 2: Plugs in a differential amplifier that receive data; formally referred to as grids 1 and 2. K-complex: A sharp, negative EEG waveform that is followed by a much slower, positive component.
Physiological Basis of EEG The electroencephalogram (EEG) is primarily a product of the summation of inhibitory and excitatory postsynaptic potentials, largely in pyramidal cells, occurring in the upper layers of the cortex. Because the electrical fields generated by neurons are attenuated over distance, only the cortex near the scalp can influence the EEG. In order to produce a summated electrical potential at the surface of the scalp, a large number of neurons must be similarly aligned and must fire in concert. Layer IV of the cortex is assumed to be the primary influence of the EEG because of its alignment with the scalp. Brain activity results in changes in electrical fields and potential differences. Because the head functions as a volume conductor (i.e., a three-dimensional conductive medium), these changes in electrical field and polarity can be measured
66
K-complexes have a duration criterion of 0.5 s and are often highest in amplitude in frontal regions. Low-frequency filter (LFF): A filter that systematically reduces the amplitude of low-frequency activity. Monopolar (referential) derivation: A derivation in which an active electrode provides input to grid 1 and a theoretically silent (noncerebral) electrode provides input to grid 2 of a differential amplifier. Compare to bipolar derivation. Montage: An array of derivations. Notch filter: A filter that reduces the amplitude of 60-Hz or 50-Hz activity (depending on local electrical output). Also called A/C filter or line filter. Nyquist-Shannon theorem: In order to obtain a minimum resolution of waveforms at a specific frequency, the sampling rate must be at least twice the frequency of interest. Saw-tooth waves: A variant of theta EEG activity in which a series of theta waveforms contain notches that make them appear like the blade of a saw. Sensitivity: The ratio of voltage to signal deflection, expressed in microvolts per millimeter (or, in sleep studies, mV cm1). Sleep spindle: A readily apparent 0.5-s burst of 12–14 Hz EEG activity generated by the thalamus and sent through thalamocortical pathways; visualized predominantly in central leads. Slow waves: Sleep-related delta waveforms with high amplitude (75 mV) and low frequency (0.5–2 Hz); frontally predominant. Theta activity: EEG activity with frequencies of 4–7 Hz; prominent in the central and temporal leads. Vertex sharp waves: Sharp, negative (upward) EEG waveforms that stand out from background activity; prominent at the vertex and reflected in central leads.
at the scalp using EEG. Neuronal activity that is less in synchrony produces faster frequencies and smaller amplitudes; high-amplitude, low-frequency EEG signals are associated with synchronous activity. By convention, the EEG is typically displayed with negative values as upward deflections. The EEG is typically the only neurological parameter measured during clinical polysomnograms. It has the advantage of not being cumbersome for the individual and provides insight into the neurological underpinnings of sleep in the millisecond range.
EEG Data EEG is often classified into frequency ranges (bandwidths) of interest. In order of increasing frequency, these include delta
Encyclopedia of Sleep
http://dx.doi.org/10.1016/B978-0-12-378610-4.00140-6
Instrumentation and Methodology | Electroencephalography
67
1 SEC.
1 SEC. Beta > 13 Hz
Alpha 8 - 13 Hz
1 SEC.
1 SEC. Theta 4 - 7 Hz
62
Delta < 4 Hz
Figure 1 Measuring EEG frequency. Reprinted with permission from Butkov N (2010) Atlas of Clinical Polysomnography, 2nd edn. Medford, OR: Synapse Media.
(<4 Hz; in practice, the lowest relevant delta frequency is 0.5 Hz), theta (4–7 Hz), alpha (8–13 Hz), and beta (>13 Hz). Some distinct EEG phenomena clearly fall within these frequency bands. For example, saw-tooth waves occur in the theta band. The amplitude of the EEG signal is related to the voltage of the signal. Recording instruments are set by the user to display voltage and allow quantification. A ratio of vertical displacement of the visual signal per unit of voltage is set. This is commonly called ‘sensitivity’ (and also referred to as ‘gain’). A commonly used sensitivity setting is 50 microvolts per millimeter or 5 microvolts per centimeter (see Figure 1 and Figure 2).
Artifact EEG artifact can prove to be very troublesome in polysomnography. Electrodes are required to adhere to the scalp for hours, as well as remaining relatively unchanged with regard to impedance values. EEG artifacts can be a product of electrical potentials changing in nearby physiology (eyeball movement, electrocardiography (ECG) artifact, muscle artifact, tongue movement artifact), rapid changes in electrode impedances (electrode popping, sweat artifact), or a mixture thereof. External artifacts include electrical line interference (60 Hz artifact), external electromagnetic interference, or a mixture thereof (movement artifact). A variety of physiological factors can result in EEG artifact. Eye movement artifact is the result of the eyeball moving in its socket. Eye blinks are the most common; this involves a slight but rapid movement of the eyeball with respect to the scalp. The ECG artifact is the result of the electrical activity of the heart conducting to the scalp. Muscle artifact is the result of muscle activity near the scalp. Tongue movement artifact is the result of the tongue, which has an electrical potential from the base to the tip, moving the mouth. Electrode popping is the result of an electrode dislodging or a change in pressure applied adhering the electrode to the scalp. It is important to realize that an electrode pop can appear differently with a common reference recording and software-applied referencing. If only one of the EEG channels
50 mV
2 cm = 100 mv (based on 50 mv per 1 cm calibration)
Figure 2 Measuring EEG amplitude. Reprinted with permission from Butkov N (2010) Atlas of Clinical Polysomnography, 2nd edn. Medford, OR: Synapse Media.
(C3-CmnRef) contains an electrode pop, the software-reference of (C3-CmnRef) – (M2-CmnRef) may not reveal an obvious electrode pop. This is an example of why EEG channels should be viewed individually with their native common reference to assess the presence and type of artifact, when suspected. Sweat artifact is the result of sweat creating a buffer between the electrode and the scalp, allowing them to be moved easily. This is usually seen in combination with sway or breathing artifact, because the sweat allows slight movements to protrude into the EEG. The electromagnetic interference (EMI) can result from any electrical device such as other physiological monitoring devices, cell phones, and electric blankets. Electrical line interference can be indicative of poor electrical grounding, improper impedances, or faulty amplification.
EEG Arousal Arousal is defined as a shift in EEG that lasts for 3 s. This time period was determined as the duration limit because of interrater reliability in the early days of sleep EEG analysis. To date, there is no research showing the minimum duration of EEG arousal that induces sleep disruption or decreases the quality of sleep. In order to score an arousal during REM sleep, a concurrent 1 s EMG increase must also occur, as the EEG of REM sleep often has waking-like EEG that makes it difficult to identify an EEG arousal. Autonomic activation often accompanies an EEG arousal. Other physiological parameters that represent autonomic activation can assist in identifying periods of arousal, such as heart rate, blood pressure, pulse wave amplitude, pulse transit time, peripheral arterial tone, and EMG activity.
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Instrumentation and Methodology | Electroencephalography
Technical Requirements The acquisition, recording, and manipulation of the EEG data have benefited from technological progress in many areas, and the use of EEG in sleep is changing accordingly. One factor in the quality of the data collected is accurate electrode placement. Electrodes are typically placed according to the International 10-20 system, an internationally accepted system for measuring the head using physical landmarks (the nasion, the inion, and the preauricular points). This system makes it possible to choose electrode sites that can be replicated in other individuals or future studies of the same patient. Another factor is the electrode impedance. Impedances of electrode sites should be below 5000 O; it is also important for electrodes to have similar impedances. EEG requires collaboration between the technologists who acquire and record the data and the physicians who ultimately interpret the data. In order to obtain an effective recording, the technologist should know the clinical question that the study is addressing and which montage is appropriate for the patient. The technologist should also have the patient’s complete medical history, including age, medications, and the timing of the last meal.
The correct settings will optimize the ability to visualize EEG features that are important in each clinical setting. In routine EEG, it is important to visualize high-frequency spike activity associated with epilepsy; therefore, it is standard to set the HFF at 70 Hz, the LFF at 1 Hz, and to turn the notch filter off. In comparison, when EEG is recorded as part of a PSG, it is generally more important to visualize slow-wave sleep and minimize EMG contamination of the EEG. Therefore, the HFF is set at 35 Hz, the LFF is set at 0.3 Hz, and the notch filter is turned off. Technologists and physicians should be particularly cautious about using the notch filter appropriately; in a well-controlled laboratory setting, it should be unnecessary to use the notch filter at all. In any case, if the detection of spikes is important to the study, the notch filter should not be used.
Digitization of the EEG The EEG needs to be not only amplified faithfully but also digitized by an analog-to-digital converter in a manner that allows for the greatest fidelity. A high sampling rate will make it possible to obtain a recording that represents the underlying physiology.
Differential Amplifier
Sampling Rates
The differential amplifier is the most important tool in EEG. It is characterized by the ability to receive two separate inputs of physiological data (collectively referred to as a derivation); the output is simply the difference between the two outputs. The essential feature of the differential amplifier is the presence of an isolated ground component (earth) that is not connected to the inputs. This allows for common mode rejection, or the ability to compare two inputs, eliminate the signals that are common to both inputs, and amplify the signals that are different. For example, the C3 electrode is located over the brain, so it records both brain activity and artifact, whereas the M2 electrode is not located over the brain, so it records only artifact. The differential amplifier connected to C3 and M2 eliminates the artifact, because it is common to both inputs, and amplifies the brain signal.
The Nyquist–Shannon theorem states that in order to faithfully reproduce a signal, the sampling rate must be at least twice the highest frequency present in the signal. If this criterion is not met, aliasing will result; that is, frequencies that exceed half of the sampling rate will be inaccurately encoded as lower frequencies. Hardware filters are typically employed before digitization and should be selected to ensure that this criterion is met. Nondestructive software filters can be used to further filter the signals for the purpose of viewing (i.e., LFF ¼ 0.3 Hz, HFF ¼ 35 Hz). Given that the bandwidth of interest is 0.5–35 Hz in clinical EEG and PSG, the Nyquist–Shannon theorem dictates a sampling rate of 70 Hz. Since this would give an unacceptably low resolution, however, a higher sampling rate must be used. For EEG in sleep studies, 512 Hz is the recommended sample rate. This allows a sufficiently faithful translation from the analog signal to the digital output, and it shows waveform shapes accurately.
AC and DC EEG is typically amplified via an AC coupled amplifier, which amplifies rapidly changing electrical potentials, removing DC offsets (unchanging electrical potentials). Some research is being performed with DC-coupled signals, but this type of recording is not routinely performed or analyzed. Although DC amplifiers are not used for EEG, they are used in other aspects of polysomnography (PSG).
Filters EEG uses three types of filters: the high-frequency filter (HFF), the low-frequency filter (LFF), and the notch filter (also called the 60-Hz filter, 50-Hz filter, or line filter). Because the use of filters has powerful effects on the display of data, an appropriate combination of filters must be chosen for recording EEG.
Bit Resolution In the context of EEG, bit resolution is the number of bits that are available to represent the voltage at each point in time. The minimum for sampling and storing EEG data is 12-bit resolution, with 16-bit resolution being better and more typical. Higher resolution than this can be useful for measuring DC potentials. Bit resolution (along with the voltage range that the bits represent) determines the smallest voltage increment that is distinguishable in the recording. For a system that amplifies and records a 1000 mV signal, encoding the signal with 12-bit resolution would provide clarity down to approximately 0.49 mV (2000 mV/4096, because there is a 2000-mV range and a 12-bit string can represent 212 levels, or 4096 levels), whereas 16-bit resolution would allow approximately 0.03 mV
Instrumentation and Methodology | Electroencephalography (2000 mV/65 536). However, the fidelity of the resulting signal is limited by the noise of other components as well.
Screen Resolution Screen resolution is defined as the number of pixels in the horizontal plane by the number of pixels in the vertical plane. The AASM-recommended resolution is 1600 1200, primarily because this was a standard screen resolution of the larger monitors available when the AASM manual was released in 2007. Higher resolutions are preferable, since screen resolution limits the number of data points that can be displayed. The EEG signal is one of the more demanding signals in terms of clarity on the screen, due to its inherent complexity. Other signals, such as EMG signals, have higher frequency content and thus have more data points to display, but the morphology of the individual waves of the EMG signal is not typically salient. The ability to change time scale and amplitude scale are essential to allow clear viewing of the necessary detail of the EEG.
Montages An EEG montage is an array of derivations or inputs into differential amplifiers. The EEG in sleep montages is typically monopolar (referential), rather than bipolar. Monopolar recordings have active electrode sites compared to a relatively inactive reference electrode site. Examples of the monopolar derivation include F4-M1, C4-M1, and O2-M1. A bipolar derivation consists of two inputs that are both located over the brain (e.g., C4-O2 and C3-O1). They compare central nervous system (CNS) activity in two locations and are typically organized so that multiple channels cover a large area of the brain. This provides the ability to visually inspect the similarities between the right and left hemispheres. Bipolar montages are often used to localize the focus of seizure activity or other CNS abnormalities (e.g., stroke). Most sleep systems record each EEG channel as an individual electrode site referenced to a common reference, allowing the user to change derivations in software programs. Various combinations of derivations can be created during the recording or postrecording.
Types of Quantitative EEG Analysis
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Wavelets Wavelets are similar to FFTs but are more flexible, as they allow multiple types of waves (sine waves, triangles, etc.) to reproduce the original signal. It has the major advantage of having temporal resolution that matches the frequency range, such that faster frequencies have more temporal resolution.
Short-Time Frequency Transformation Short-time frequency transformation has similarities with both FFTs and wavelets. It is essentially the FFT, but so that it more closely resembles the temporal resolution of wavelets.
Hilbert–Huang Transform The Hilbert–Huang transform is a method that attempts to decompose a time series using empirical mode decomposition into multiple time series that represent riding waves. By grouping similar frequencies into separate time series, the instantaneous frequency of the signals can be calculated with increased integrity.
Spectral Coherence Spectral coherence is a measure of the similarity of frequency distribution of two different signals. This is typically performed on signals that represent the same portion of time but different scalp locations. The coherence across hemispheres is one such common measure.
Source Decomposition Independent component analysis (ICA) is known as a blindsource separation technique. It attempts to extract underlying signals that, when combined, produce the resulting EEG. It operates on the assumption that there are underlying signals that are linearly mixed to produce the EEG. The ICA, through iteration, attempts to unmix signals that are mutually exclusive to each other. This technique can produce remarkable results, but it can be challenging to know a priori how many underlying signals are present. It has been used very successfully in removing unwanted artifact such as electrocardiogram (EKG) and eye blink contamination.
Spectral Analysis Spectral analysis is the transformation of any time series into the frequency domain. There are many tools to do this, but all the tools essentially attempt to reduce the signal into the various frequency components present in the signal and a measure of their amplitude.
Fast Fourier Transformation Fast Fourier transformation (FFT) is the most commonly employed type of analysis. It is a computationally efficient method for performing spectral analysis, particularly for signals with a sampling rate of a power of 2 (i.e., 64, 128, 256, and 512). The FFT reduces the signal into sine waves at various frequencies.
Event-Related Potentials An event-related potential (ERP; also called an evoked potential) is an electrophysiological response that occurs in response to a stimulus. Stimuli include experimentally induced triggers (e.g., audio pulses), events that are presumed to be similar in nature (e.g., periodic leg movements), or even spontaneous EEG events themselves (e.g., K-complexes). One way of analyzing ERPs is to average multiple segments of EEG that are time locked to specific events. The resulting averaged signal produces one or more ERPs, which may have negative and positive components. These components are often given names that designate the polarity and delay from trigger (e.g., the P500 is a positive deflection at a time delay of 500 ms.)
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Instrumentation and Methodology | Electroencephalography
Sleep EEG across the Lifespan The early development of the CNS is reflected in the EEG patterns seen during sleep in neonates and infants; maturational changes can be seen in early infancy. The EEG patterns of healthy children are characterized by an abundance of highamplitude slow-wave sleep. The amplitude and quantity of delta activity decrease throughout the lifespan, starting in young adulthood. This change in delta activity, especially in terms of amplitude, is most dramatic in older adults. Other changes in sleep EEG are associated with CNS diseases, such as Parkinson’s disease and Alzheimer’s disease.
EEG Research EEG research is moving at a rapid pace. New techniques are continually being developed and combined with other analyses. EEG has excellent temporal resolution; thus, combining the EEG with imaging measures, which have better spatial resolution (magnetic resonance imaging, positron emission tomography, etc.), provides a wealth of information. Neural pathways involved in producing EEG phenomena are being investigated with the use of optogenetics, a method that employs a viral attack vector to introduce genes into specific cells that can then be selectively excited or inhibited by exposing the neurons to a specific wavelength of light. This may allow the ability to initiate EEG phenomena and determine the pathways that are required. The pathway involved with arousal from sleep has been investigated using this method to selectively excite and inhibit neurons in the locus coeruleus and hypocretin neurons. Machine learning (neural networks, support vector machines, cluster analysis, etc.) is another area that is helping researchers to understand the complex dynamical nature of the EEG and allowing the ability to extract features. There are
many features of the EEG that appear to be highly involved with specific brain function, such as spindles with memory. The EEG has also been shown to be highly heritable in twin studies and other genetic studies. Spectral analysis of the sleep EEG has been shown to be highly repeatable from night to night; it has been likened to an EEG fingerprint. Moreover, features of the EEG have been proposed to change with psychiatric disorders such as depression, schizophrenia, alcoholism, and dementia. The genetics and epigenetics of the EEG are certainly intertwined, but work is underway to find genes that are involved in producing EEG phenomena, as well as to potentially elucidate new pathways and markers of pathophysiology.
See also: Instrumentation and Methodology: Adult Polysomnography; Monitoring and Staging Human Sleep; Neuroimaging and Sleep; Pediatric Polysomnography; Intrinsic Factors Affecting Sleep Loss/Deprivation: Electroencephalographic and Neurophysiological Changes; Neurologic Disorders: Epilepsy and Sleep; Pediatric Sleep: Electrophysiological Changes in Sleep During Childhood; Sleep and the Nervous System: Electrophysiology of Sleep–Wake Systems.
Further Reading Niedermeyer E and Lopes da Silva F (1987) Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 2nd edn. Baltimore, MD: Urban & Schwarzenberg. Nunez PL and Srinivasan R (2006) Electric Fields of the Brain: The Neurophysics of EEG, 2nd edn. Oxford: Oxford University Press. Tyner FS, Knott JR, and Mayer WB Jr. (1983) Fundamentals of EEG Technology: Basic Concepts and Methods, vol. 1. New York: Raven Press. Wong PKH (1996) Digital EEG in Clinical Practice. Philadelphia, PA: Lippincott-Raven.
Glossary Alpha activity: Electroencephalogram (EEG) activity with frequencies of 8–13 Hz; prominent in the occipital lead. Beta activity: EEG activity with low amplitude and high frequencies (>13 Hz). Bipolar derivation: A derivation in which grid 1 and grid 2 of a differential amplifier both receive input from active electrodes (i.e., both record brain activity or both record muscle activity). Compare to monopolar (referential) derivation. Delta activity: EEG activity with frequencies less than 4 Hz. Derivation: Specific input into any one differential amplifier (e.g., C3-M2). Differential amplifier: An amplifier, commonly used in electrophysiology, in which there are two inputs (e.g., C3 as input 1 and M2 as input 2) and the output is the difference between the two. Electrode popping: An EEG artifact that occurs when the voltage across amplifier exceeds the limits of the display for that channel; occurs commonly when an electrode placement is compromised. Event-related potential: An electrophysiological response, measurable by EEG, that occurs in response to a stimulus (e.g., the brainstem auditory evoked response [BAER]). Also called evoked response. Gamma activity: EEG activity with frequencies greater than 35 Hz. High-frequency filter (HFF): A filter that systematically reduces the amplitude of high-frequency activity. Inputs 1 and 2: Plugs in a differential amplifier that receive data; formally referred to as grids 1 and 2. K-complex: A sharp, negative EEG waveform that is followed by a much slower, positive component.
Physiological Basis of EEG The electroencephalogram (EEG) is primarily a product of the summation of inhibitory and excitatory postsynaptic potentials, largely in pyramidal cells, occurring in the upper layers of the cortex. Because the electrical fields generated by neurons are attenuated over distance, only the cortex near the scalp can influence the EEG. In order to produce a summated electrical potential at the surface of the scalp, a large number of neurons must be similarly aligned and must fire in concert. Layer IV of the cortex is assumed to be the primary influence of the EEG because of its alignment with the scalp. Brain activity results in changes in electrical fields and potential differences. Because the head functions as a volume conductor (i.e., a three-dimensional conductive medium), these changes in electrical field and polarity can be measured
66
K-complexes have a duration criterion of 0.5 s and are often highest in amplitude in frontal regions. Low-frequency filter (LFF): A filter that systematically reduces the amplitude of low-frequency activity. Monopolar (referential) derivation: A derivation in which an active electrode provides input to grid 1 and a theoretically silent (noncerebral) electrode provides input to grid 2 of a differential amplifier. Compare to bipolar derivation. Montage: An array of derivations. Notch filter: A filter that reduces the amplitude of 60-Hz or 50-Hz activity (depending on local electrical output). Also called A/C filter or line filter. Nyquist-Shannon theorem: In order to obtain a minimum resolution of waveforms at a specific frequency, the sampling rate must be at least twice the frequency of interest. Saw-tooth waves: A variant of theta EEG activity in which a series of theta waveforms contain notches that make them appear like the blade of a saw. Sensitivity: The ratio of voltage to signal deflection, expressed in microvolts per millimeter (or, in sleep studies, mV cm1). Sleep spindle: A readily apparent 0.5-s burst of 12–14 Hz EEG activity generated by the thalamus and sent through thalamocortical pathways; visualized predominantly in central leads. Slow waves: Sleep-related delta waveforms with high amplitude (75 mV) and low frequency (0.5–2 Hz); frontally predominant. Theta activity: EEG activity with frequencies of 4–7 Hz; prominent in the central and temporal leads. Vertex sharp waves: Sharp, negative (upward) EEG waveforms that stand out from background activity; prominent at the vertex and reflected in central leads.
at the scalp using EEG. Neuronal activity that is less in synchrony produces faster frequencies and smaller amplitudes; high-amplitude, low-frequency EEG signals are associated with synchronous activity. By convention, the EEG is typically displayed with negative values as upward deflections. The EEG is typically the only neurological parameter measured during clinical polysomnograms. It has the advantage of not being cumbersome for the individual and provides insight into the neurological underpinnings of sleep in the millisecond range.
EEG Data EEG is often classified into frequency ranges (bandwidths) of interest. In order of increasing frequency, these include delta
Encyclopedia of Sleep
http://dx.doi.org/10.1016/B978-0-12-378610-4.00140-6
Instrumentation and Methodology | Electroencephalography
67
1 SEC.
1 SEC. Beta > 13 Hz
Alpha 8 - 13 Hz
1 SEC.
1 SEC. Theta 4 - 7 Hz
62
Delta < 4 Hz
Figure 1 Measuring EEG frequency. Reprinted with permission from Butkov N (2010) Atlas of Clinical Polysomnography, 2nd edn. Medford, OR: Synapse Media.
(<4 Hz; in practice, the lowest relevant delta frequency is 0.5 Hz), theta (4–7 Hz), alpha (8–13 Hz), and beta (>13 Hz). Some distinct EEG phenomena clearly fall within these frequency bands. For example, saw-tooth waves occur in the theta band. The amplitude of the EEG signal is related to the voltage of the signal. Recording instruments are set by the user to display voltage and allow quantification. A ratio of vertical displacement of the visual signal per unit of voltage is set. This is commonly called ‘sensitivity’ (and also referred to as ‘gain’). A commonly used sensitivity setting is 50 microvolts per millimeter or 5 microvolts per centimeter (see Figure 1 and Figure 2).
Artifact EEG artifact can prove to be very troublesome in polysomnography. Electrodes are required to adhere to the scalp for hours, as well as remaining relatively unchanged with regard to impedance values. EEG artifacts can be a product of electrical potentials changing in nearby physiology (eyeball movement, electrocardiography (ECG) artifact, muscle artifact, tongue movement artifact), rapid changes in electrode impedances (electrode popping, sweat artifact), or a mixture thereof. External artifacts include electrical line interference (60 Hz artifact), external electromagnetic interference, or a mixture thereof (movement artifact). A variety of physiological factors can result in EEG artifact. Eye movement artifact is the result of the eyeball moving in its socket. Eye blinks are the most common; this involves a slight but rapid movement of the eyeball with respect to the scalp. The ECG artifact is the result of the electrical activity of the heart conducting to the scalp. Muscle artifact is the result of muscle activity near the scalp. Tongue movement artifact is the result of the tongue, which has an electrical potential from the base to the tip, moving the mouth. Electrode popping is the result of an electrode dislodging or a change in pressure applied adhering the electrode to the scalp. It is important to realize that an electrode pop can appear differently with a common reference recording and software-applied referencing. If only one of the EEG channels
50 mV
2 cm = 100 mv (based on 50 mv per 1 cm calibration)
Figure 2 Measuring EEG amplitude. Reprinted with permission from Butkov N (2010) Atlas of Clinical Polysomnography, 2nd edn. Medford, OR: Synapse Media.
(C3-CmnRef) contains an electrode pop, the software-reference of (C3-CmnRef) – (M2-CmnRef) may not reveal an obvious electrode pop. This is an example of why EEG channels should be viewed individually with their native common reference to assess the presence and type of artifact, when suspected. Sweat artifact is the result of sweat creating a buffer between the electrode and the scalp, allowing them to be moved easily. This is usually seen in combination with sway or breathing artifact, because the sweat allows slight movements to protrude into the EEG. The electromagnetic interference (EMI) can result from any electrical device such as other physiological monitoring devices, cell phones, and electric blankets. Electrical line interference can be indicative of poor electrical grounding, improper impedances, or faulty amplification.
EEG Arousal Arousal is defined as a shift in EEG that lasts for 3 s. This time period was determined as the duration limit because of interrater reliability in the early days of sleep EEG analysis. To date, there is no research showing the minimum duration of EEG arousal that induces sleep disruption or decreases the quality of sleep. In order to score an arousal during REM sleep, a concurrent 1 s EMG increase must also occur, as the EEG of REM sleep often has waking-like EEG that makes it difficult to identify an EEG arousal. Autonomic activation often accompanies an EEG arousal. Other physiological parameters that represent autonomic activation can assist in identifying periods of arousal, such as heart rate, blood pressure, pulse wave amplitude, pulse transit time, peripheral arterial tone, and EMG activity.
68
Instrumentation and Methodology | Electroencephalography
Technical Requirements The acquisition, recording, and manipulation of the EEG data have benefited from technological progress in many areas, and the use of EEG in sleep is changing accordingly. One factor in the quality of the data collected is accurate electrode placement. Electrodes are typically placed according to the International 10-20 system, an internationally accepted system for measuring the head using physical landmarks (the nasion, the inion, and the preauricular points). This system makes it possible to choose electrode sites that can be replicated in other individuals or future studies of the same patient. Another factor is the electrode impedance. Impedances of electrode sites should be below 5000 O; it is also important for electrodes to have similar impedances. EEG requires collaboration between the technologists who acquire and record the data and the physicians who ultimately interpret the data. In order to obtain an effective recording, the technologist should know the clinical question that the study is addressing and which montage is appropriate for the patient. The technologist should also have the patient’s complete medical history, including age, medications, and the timing of the last meal.
The correct settings will optimize the ability to visualize EEG features that are important in each clinical setting. In routine EEG, it is important to visualize high-frequency spike activity associated with epilepsy; therefore, it is standard to set the HFF at 70 Hz, the LFF at 1 Hz, and to turn the notch filter off. In comparison, when EEG is recorded as part of a PSG, it is generally more important to visualize slow-wave sleep and minimize EMG contamination of the EEG. Therefore, the HFF is set at 35 Hz, the LFF is set at 0.3 Hz, and the notch filter is turned off. Technologists and physicians should be particularly cautious about using the notch filter appropriately; in a well-controlled laboratory setting, it should be unnecessary to use the notch filter at all. In any case, if the detection of spikes is important to the study, the notch filter should not be used.
Digitization of the EEG The EEG needs to be not only amplified faithfully but also digitized by an analog-to-digital converter in a manner that allows for the greatest fidelity. A high sampling rate will make it possible to obtain a recording that represents the underlying physiology.
Differential Amplifier
Sampling Rates
The differential amplifier is the most important tool in EEG. It is characterized by the ability to receive two separate inputs of physiological data (collectively referred to as a derivation); the output is simply the difference between the two outputs. The essential feature of the differential amplifier is the presence of an isolated ground component (earth) that is not connected to the inputs. This allows for common mode rejection, or the ability to compare two inputs, eliminate the signals that are common to both inputs, and amplify the signals that are different. For example, the C3 electrode is located over the brain, so it records both brain activity and artifact, whereas the M2 electrode is not located over the brain, so it records only artifact. The differential amplifier connected to C3 and M2 eliminates the artifact, because it is common to both inputs, and amplifies the brain signal.
The Nyquist–Shannon theorem states that in order to faithfully reproduce a signal, the sampling rate must be at least twice the highest frequency present in the signal. If this criterion is not met, aliasing will result; that is, frequencies that exceed half of the sampling rate will be inaccurately encoded as lower frequencies. Hardware filters are typically employed before digitization and should be selected to ensure that this criterion is met. Nondestructive software filters can be used to further filter the signals for the purpose of viewing (i.e., LFF ¼ 0.3 Hz, HFF ¼ 35 Hz). Given that the bandwidth of interest is 0.5–35 Hz in clinical EEG and PSG, the Nyquist–Shannon theorem dictates a sampling rate of 70 Hz. Since this would give an unacceptably low resolution, however, a higher sampling rate must be used. For EEG in sleep studies, 512 Hz is the recommended sample rate. This allows a sufficiently faithful translation from the analog signal to the digital output, and it shows waveform shapes accurately.
AC and DC EEG is typically amplified via an AC coupled amplifier, which amplifies rapidly changing electrical potentials, removing DC offsets (unchanging electrical potentials). Some research is being performed with DC-coupled signals, but this type of recording is not routinely performed or analyzed. Although DC amplifiers are not used for EEG, they are used in other aspects of polysomnography (PSG).
Filters EEG uses three types of filters: the high-frequency filter (HFF), the low-frequency filter (LFF), and the notch filter (also called the 60-Hz filter, 50-Hz filter, or line filter). Because the use of filters has powerful effects on the display of data, an appropriate combination of filters must be chosen for recording EEG.
Bit Resolution In the context of EEG, bit resolution is the number of bits that are available to represent the voltage at each point in time. The minimum for sampling and storing EEG data is 12-bit resolution, with 16-bit resolution being better and more typical. Higher resolution than this can be useful for measuring DC potentials. Bit resolution (along with the voltage range that the bits represent) determines the smallest voltage increment that is distinguishable in the recording. For a system that amplifies and records a 1000 mV signal, encoding the signal with 12-bit resolution would provide clarity down to approximately 0.49 mV (2000 mV/4096, because there is a 2000-mV range and a 12-bit string can represent 212 levels, or 4096 levels), whereas 16-bit resolution would allow approximately 0.03 mV
Instrumentation and Methodology | Electroencephalography (2000 mV/65 536). However, the fidelity of the resulting signal is limited by the noise of other components as well.
Screen Resolution Screen resolution is defined as the number of pixels in the horizontal plane by the number of pixels in the vertical plane. The AASM-recommended resolution is 1600 1200, primarily because this was a standard screen resolution of the larger monitors available when the AASM manual was released in 2007. Higher resolutions are preferable, since screen resolution limits the number of data points that can be displayed. The EEG signal is one of the more demanding signals in terms of clarity on the screen, due to its inherent complexity. Other signals, such as EMG signals, have higher frequency content and thus have more data points to display, but the morphology of the individual waves of the EMG signal is not typically salient. The ability to change time scale and amplitude scale are essential to allow clear viewing of the necessary detail of the EEG.
Montages An EEG montage is an array of derivations or inputs into differential amplifiers. The EEG in sleep montages is typically monopolar (referential), rather than bipolar. Monopolar recordings have active electrode sites compared to a relatively inactive reference electrode site. Examples of the monopolar derivation include F4-M1, C4-M1, and O2-M1. A bipolar derivation consists of two inputs that are both located over the brain (e.g., C4-O2 and C3-O1). They compare central nervous system (CNS) activity in two locations and are typically organized so that multiple channels cover a large area of the brain. This provides the ability to visually inspect the similarities between the right and left hemispheres. Bipolar montages are often used to localize the focus of seizure activity or other CNS abnormalities (e.g., stroke). Most sleep systems record each EEG channel as an individual electrode site referenced to a common reference, allowing the user to change derivations in software programs. Various combinations of derivations can be created during the recording or postrecording.
Types of Quantitative EEG Analysis
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Wavelets Wavelets are similar to FFTs but are more flexible, as they allow multiple types of waves (sine waves, triangles, etc.) to reproduce the original signal. It has the major advantage of having temporal resolution that matches the frequency range, such that faster frequencies have more temporal resolution.
Short-Time Frequency Transformation Short-time frequency transformation has similarities with both FFTs and wavelets. It is essentially the FFT, but so that it more closely resembles the temporal resolution of wavelets.
Hilbert–Huang Transform The Hilbert–Huang transform is a method that attempts to decompose a time series using empirical mode decomposition into multiple time series that represent riding waves. By grouping similar frequencies into separate time series, the instantaneous frequency of the signals can be calculated with increased integrity.
Spectral Coherence Spectral coherence is a measure of the similarity of frequency distribution of two different signals. This is typically performed on signals that represent the same portion of time but different scalp locations. The coherence across hemispheres is one such common measure.
Source Decomposition Independent component analysis (ICA) is known as a blindsource separation technique. It attempts to extract underlying signals that, when combined, produce the resulting EEG. It operates on the assumption that there are underlying signals that are linearly mixed to produce the EEG. The ICA, through iteration, attempts to unmix signals that are mutually exclusive to each other. This technique can produce remarkable results, but it can be challenging to know a priori how many underlying signals are present. It has been used very successfully in removing unwanted artifact such as electrocardiogram (EKG) and eye blink contamination.
Spectral Analysis Spectral analysis is the transformation of any time series into the frequency domain. There are many tools to do this, but all the tools essentially attempt to reduce the signal into the various frequency components present in the signal and a measure of their amplitude.
Fast Fourier Transformation Fast Fourier transformation (FFT) is the most commonly employed type of analysis. It is a computationally efficient method for performing spectral analysis, particularly for signals with a sampling rate of a power of 2 (i.e., 64, 128, 256, and 512). The FFT reduces the signal into sine waves at various frequencies.
Event-Related Potentials An event-related potential (ERP; also called an evoked potential) is an electrophysiological response that occurs in response to a stimulus. Stimuli include experimentally induced triggers (e.g., audio pulses), events that are presumed to be similar in nature (e.g., periodic leg movements), or even spontaneous EEG events themselves (e.g., K-complexes). One way of analyzing ERPs is to average multiple segments of EEG that are time locked to specific events. The resulting averaged signal produces one or more ERPs, which may have negative and positive components. These components are often given names that designate the polarity and delay from trigger (e.g., the P500 is a positive deflection at a time delay of 500 ms.)
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Instrumentation and Methodology | Electroencephalography
Sleep EEG across the Lifespan The early development of the CNS is reflected in the EEG patterns seen during sleep in neonates and infants; maturational changes can be seen in early infancy. The EEG patterns of healthy children are characterized by an abundance of highamplitude slow-wave sleep. The amplitude and quantity of delta activity decrease throughout the lifespan, starting in young adulthood. This change in delta activity, especially in terms of amplitude, is most dramatic in older adults. Other changes in sleep EEG are associated with CNS diseases, such as Parkinson’s disease and Alzheimer’s disease.
EEG Research EEG research is moving at a rapid pace. New techniques are continually being developed and combined with other analyses. EEG has excellent temporal resolution; thus, combining the EEG with imaging measures, which have better spatial resolution (magnetic resonance imaging, positron emission tomography, etc.), provides a wealth of information. Neural pathways involved in producing EEG phenomena are being investigated with the use of optogenetics, a method that employs a viral attack vector to introduce genes into specific cells that can then be selectively excited or inhibited by exposing the neurons to a specific wavelength of light. This may allow the ability to initiate EEG phenomena and determine the pathways that are required. The pathway involved with arousal from sleep has been investigated using this method to selectively excite and inhibit neurons in the locus coeruleus and hypocretin neurons. Machine learning (neural networks, support vector machines, cluster analysis, etc.) is another area that is helping researchers to understand the complex dynamical nature of the EEG and allowing the ability to extract features. There are
many features of the EEG that appear to be highly involved with specific brain function, such as spindles with memory. The EEG has also been shown to be highly heritable in twin studies and other genetic studies. Spectral analysis of the sleep EEG has been shown to be highly repeatable from night to night; it has been likened to an EEG fingerprint. Moreover, features of the EEG have been proposed to change with psychiatric disorders such as depression, schizophrenia, alcoholism, and dementia. The genetics and epigenetics of the EEG are certainly intertwined, but work is underway to find genes that are involved in producing EEG phenomena, as well as to potentially elucidate new pathways and markers of pathophysiology.
See also: Instrumentation and Methodology: Adult Polysomnography; Monitoring and Staging Human Sleep; Neuroimaging and Sleep; Pediatric Polysomnography; Intrinsic Factors Affecting Sleep Loss/Deprivation: Electroencephalographic and Neurophysiological Changes; Neurologic Disorders: Epilepsy and Sleep; Pediatric Sleep: Electrophysiological Changes in Sleep During Childhood; Sleep and the Nervous System: Electrophysiology of Sleep–Wake Systems.
Further Reading Niedermeyer E and Lopes da Silva F (1987) Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 2nd edn. Baltimore, MD: Urban & Schwarzenberg. Nunez PL and Srinivasan R (2006) Electric Fields of the Brain: The Neurophysics of EEG, 2nd edn. Oxford: Oxford University Press. Tyner FS, Knott JR, and Mayer WB Jr. (1983) Fundamentals of EEG Technology: Basic Concepts and Methods, vol. 1. New York: Raven Press. Wong PKH (1996) Digital EEG in Clinical Practice. Philadelphia, PA: Lippincott-Raven.