1 Basics of EEG: Generation, Acquisition, and Applications of EEG
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tive impairment [26], and post-traumatic stress disorder [13]. In particular, functional
connectivity analysis is useful to study epilepsy because epilepsy is thought to be
one of the most representative brain network disorders [18]. Detailed descriptions
of the functional connectivity measures can be found in Chap. 6.
1.4 Applications of EEG
In the early stage of development of EEG, visual inspection of EEG waveforms was
the only way to use EEG in practical applications. Indeed, visual inspection of EEG
waveforms is still useful in studying sleep and diagnosing some neurological disorders, such as epilepsy. Dissemination of digital EEGs expanded the application
fields of EEGs from limited research and diagnostic applications to more-extensive
applications, including cognitive neuroscience study, diagnosis of psychiatric diseases, neuromarketing, neuroergonomics, sports science, and human brain mapping.
Recently, owing to the rapid development of digital engineering, EEGs can be applied
to real-time applications, such as BCI and neurofeedback.
The use of EEG in practical applications has steadily increased and is expected
to continue to increase. Indeed, EEG has many advantages over the other methods
to study brain functions, as follows:
• EEG is perfectly noninvasive, without any exposure to radiation or high magnetic
field
• EEG is economical
• EEG devices can be made small and portable
• EEG has high temporal resolution
• EEG devices do not generate any noise
• EEG can be recorded in an open environment
• EEG can be acquired without active response from subjects.
Traditionally, EEG data were acquired in laboratory or clinical environments,
where there are high-end EEG recording devices with a large number of channels
and well-motivated participants who have agreed to participate in experiments with
long durations. Recently, however, the advancement of wireless technology and highperformance biosensors enabled the development of wearable EEG devices that are
easy to wear and comfortable for long-term use, expediting the development of
novel applications of EEG that do not necessarily require laboratory settings, e.g.,
monitoring the brain activity of healthy persons during daily life [5, 19, 29].
Despite the recent development of EEG technology, EEG still has some intrinsic
limitations that need to be overcome, examples of which include low spatial resolution and low SNR. Therefore, development of new computational EEG analysis
methods is still necessary to enhance the reliability and usability of EEG.
9
tive impairment [26], and post-traumatic stress disorder [13]. In particular, functional
connectivity analysis is useful to study epilepsy because epilepsy is thought to be
one of the most representative brain network disorders [18]. Detailed descriptions
of the functional connectivity measures can be found in Chap. 6.
1.4 Applications of EEG
In the early stage of development of EEG, visual inspection of EEG waveforms was
the only way to use EEG in practical applications. Indeed, visual inspection of EEG
waveforms is still useful in studying sleep and diagnosing some neurological disorders, such as epilepsy. Dissemination of digital EEGs expanded the application
fields of EEGs from limited research and diagnostic applications to more-extensive
applications, including cognitive neuroscience study, diagnosis of psychiatric diseases, neuromarketing, neuroergonomics, sports science, and human brain mapping.
Recently, owing to the rapid development of digital engineering, EEGs can be applied
to real-time applications, such as BCI and neurofeedback.
The use of EEG in practical applications has steadily increased and is expected
to continue to increase. Indeed, EEG has many advantages over the other methods
to study brain functions, as follows:
• EEG is perfectly noninvasive, without any exposure to radiation or high magnetic
field
• EEG is economical
• EEG devices can be made small and portable
• EEG has high temporal resolution
• EEG devices do not generate any noise
• EEG can be recorded in an open environment
• EEG can be acquired without active response from subjects.
Traditionally, EEG data were acquired in laboratory or clinical environments,
where there are high-end EEG recording devices with a large number of channels
and well-motivated participants who have agreed to participate in experiments with
long durations. Recently, however, the advancement of wireless technology and highperformance biosensors enabled the development of wearable EEG devices that are
easy to wear and comfortable for long-term use, expediting the development of
novel applications of EEG that do not necessarily require laboratory settings, e.g.,
monitoring the brain activity of healthy persons during daily life [5, 19, 29].
Despite the recent development of EEG technology, EEG still has some intrinsic
limitations that need to be overcome, examples of which include low spatial resolution and low SNR. Therefore, development of new computational EEG analysis
methods is still necessary to enhance the reliability and usability of EEG.