1 Basics of EEG: Generation, Acquisition, and Applications of EEG
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circuits are implemented in the EEG amplifier to remove or reduce the noise. Analog
filters can also be used to remove specific noise components and increase signal-tonoise ratio (SNR). High-pass and band-reject (notch) filters can be used optionally
to reject low-frequency physiological noise (e.g., respiration artifact) and AC power
noise, respectively. All EEG devices should include an analog low-pass filter with
a cutoff frequency less than half of the sampling rate to prevent aliasing, unwanted
distortion in the sampled EEG signal. This type of analog low-pass filter is generally
referred to as the anti-aliasing filter. This will be dealt with in a more detailed manner
in Chap. 3. ADC converts the amplified and filtered analog signals to digital EEG
signals using sampling and encoding procedures [28].
1.3 Computational EEG Analysis
Once the digital EEG signals have been stored in storage media, a variety of forms
of information characterizing the underlying brain activities can be extracted from
the numeric data. In this book, four major computational EEG analysis methods are
introduced: EEG spectral analysis (Chap. 3), event-related potential (ERP) analysis (Chap. 4), EEG source imaging (Chap. 5), and functional connectivity analysis
(Chap. 6).
1.3.1 EEG Spectral Analysis
One of the main advantages of EEG over the other hemodynamics- or
neurochemistry-based neuroimaging modalities, such as fMRI and PET, is its superior temporal resolution that makes it possible to investigate neuronal activities changing on the order of tens of milliseconds. Thanks to the high temporal resolution of
EEG, a large amount of useful information can also be obtained from frequency
domain (or spectral) analysis. It is well known that changes in the EEG power
spectrum are directly or indirectly associated with a variety of ongoing brain activities, e.g., mu-band (8–12 Hz) event-related desynchronization (ERD) and beta-band
(18–22 Hz) event-related synchronization (ERS) associated with motor execution
[11] and alpha-band (8–13 Hz) ERD associated with visual encoding [16]. EEG
spectral analysis can also provide useful biomarkers to help diagnose and characterize various psychiatric diseases and neurological disorders. For example, reduced
frontal gamma-band (30–50 Hz activity may indicate declined cognitive function [3]
and increased midline beta-band (13–30 Hz) activity may be an indicator of restlessleg syndrome [8, 14]. Spectral analysis can also be used to implement various types
of brain–computer interfaces (BCIs) and neurofeedback systems [12].
7
circuits are implemented in the EEG amplifier to remove or reduce the noise. Analog
filters can also be used to remove specific noise components and increase signal-tonoise ratio (SNR). High-pass and band-reject (notch) filters can be used optionally
to reject low-frequency physiological noise (e.g., respiration artifact) and AC power
noise, respectively. All EEG devices should include an analog low-pass filter with
a cutoff frequency less than half of the sampling rate to prevent aliasing, unwanted
distortion in the sampled EEG signal. This type of analog low-pass filter is generally
referred to as the anti-aliasing filter. This will be dealt with in a more detailed manner
in Chap. 3. ADC converts the amplified and filtered analog signals to digital EEG
signals using sampling and encoding procedures [28].
1.3 Computational EEG Analysis
Once the digital EEG signals have been stored in storage media, a variety of forms
of information characterizing the underlying brain activities can be extracted from
the numeric data. In this book, four major computational EEG analysis methods are
introduced: EEG spectral analysis (Chap. 3), event-related potential (ERP) analysis (Chap. 4), EEG source imaging (Chap. 5), and functional connectivity analysis
(Chap. 6).
1.3.1 EEG Spectral Analysis
One of the main advantages of EEG over the other hemodynamics- or
neurochemistry-based neuroimaging modalities, such as fMRI and PET, is its superior temporal resolution that makes it possible to investigate neuronal activities changing on the order of tens of milliseconds. Thanks to the high temporal resolution of
EEG, a large amount of useful information can also be obtained from frequency
domain (or spectral) analysis. It is well known that changes in the EEG power
spectrum are directly or indirectly associated with a variety of ongoing brain activities, e.g., mu-band (8–12 Hz) event-related desynchronization (ERD) and beta-band
(18–22 Hz) event-related synchronization (ERS) associated with motor execution
[11] and alpha-band (8–13 Hz) ERD associated with visual encoding [16]. EEG
spectral analysis can also provide useful biomarkers to help diagnose and characterize various psychiatric diseases and neurological disorders. For example, reduced
frontal gamma-band (30–50 Hz activity may indicate declined cognitive function [3]
and increased midline beta-band (13–30 Hz) activity may be an indicator of restlessleg syndrome [8, 14]. Spectral analysis can also be used to implement various types
of brain–computer interfaces (BCIs) and neurofeedback systems [12].