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138 Y. Wang et al. which has been employed in a steady-state visual evoked potential (SSVEP) based BCI [5]. More generally, evoked potentials can be considered to result partially from a reorganization of the phases of the ongoing EEG rhythms [6]. From the viewpoint of psychophysiology, EEG signals are divided into five rhythms in different frequency bands: delta rhythm (0.1–3.5 Hz), theta rhythm (4–7.5 Hz), alpha rhythm (8–13 Hz), beta rhythm (14–30 Hz), and gamma rhythm (>30 Hz) [7]. Although the rhythmic character of EEG has been observed for a long period, many new studies on the mechanisms of brain rhythms emerged after the 1980s. So far, the cellular bases of EEG rhythms are still under investigation. The knowledge of EEG rhythms is limited; however, numerous neurophysiologic studies indicate that brain rhythms can reflect changes of brain states caused by stimuli from the environment or cognitive activities. For example, EEG rhythms can indicate working state or idling state of the functional areas of the cortex. It is known that the alpha rhythm recorded over the visual cortex is considered to be an indicator of activities in the visual cortex. The clear alpha wave while eyes are closed indicates the idling state of the visual cortex, while the block of the alpha rhythm when eyes are open reflects the working state of the visual cortex. Another example is mu rhythm, which can be recorded over the sensorimotor cortex. A significant mu rhythm only exists during the idling state of the sensorimotor cortex. The block of mu rhythm accompanies activation of the sensorimotor cortex. Self control of brain rhythms serves an important function in brain–computer communication. Modulation of brain rhythms is used here to describe the detectable changes of EEG rhythms. BCIs based on the modulation of brain rhythms recognize changes of specific EEG rhythms induced by self-controlled brain activities. In our studies, we focus on EEG rhythms located in the frequency band of alpha and beta rhythms (8–30 Hz). Compared with the low-frequency evoked potentials, these components have several advantages as follows: (1) Affected less by artifacts. The lower frequency components are always easily affected by the artifacts introduced by electrooculogram (EOG) and electromyogram (EMG). For example, removal of the eye movement artifact is an important procedure in event-related potential (ERP) processing, whereas it can be omitted in analyzing mu/beta rhythms after specific band-pass filtering. (2) More stable and lasting, and thus with a higher signal-to-noise ratio (SNR). Modulation of these rhythms can last for a relatively long period (e.g., several seconds), while transient evoked potentials only occur within several 100 ms. Therefore, it is more promising to realize single-trial identification for highfrequency brain rhythms. Moreover, due to the phase-locked character of the evoked potentials, strict time precision is necessary for data alignment in the averaging procedure, while analysis of the power-based changes of the highfrequency rhythms permits a lower precision, and thus facilitates online designs of hardware and software. (3) More applicable signal processing techniques. For wave-based analysis of evoked potentials, analysis is always done in the temporal domain. However, for the high-frequency rhythms, various temporal and frequency methods (e.g.,
Practical Designs of BCI Based on the Modulation of EEG Rhythms 139 Data Recording Modulated Brain Rhythm Brain-Computer Interface Brain Rhythm Demodulation Feedback Fig. 1 Diagram of a BCI based on the modulation of brain rhythms Device Control power and coherence analysis methods in the temporal and frequency domains, respectively) can be applied to extract EEG features. Therefore, the BCI based on the high-frequency rhythms provides a better platform for studying signal processing techniques and has a bigger potential in system performance. Figure 1 is the block diagram of a BCI based on the modulation of brain rhythms. The control intention of the subject is embedded in the modulated brain rhythms through specific approaches, e.g., frequency coding or phase coding. According to the modulation approaches, the signal processing procedure aims to demodulate the brain rhythms and then extract the features, which will be translated into control signals to operate the output device. For instance, the SSVEP-based BCI and the motor imagery based BCI adopt typical approaches of brain rhythm modulation. The SSVEP system uses detection of frequency-coded or phase-coded SSVEPs to determine the gaze or spatial selective attention direction of the subject. Unlike the SSVEP BCI, the motor imagery BCI recognizes spatial distributions of amplitudemodulated mu/beta rhythms corresponding to the motor imagery states of different body parts. The methods for demodulating brain rhythms applied in BCIs will be introduced in the next section. 1.2 Challenges Confronting Practical System Designs The design and implementation of an online system plays an important role in current BCI research, with the purpose of producing practical devices for real-life clinical application [8]. Compared with offline data analysis, an online BCI has difficulties in fulfilling real-time processing, system practicability and brain–machine co-adaptation. In this chapter, the principle of an online BCI based on the rhythmic modulation of EEG signals will be proposed. In our studies, two rhythmic EEG components corresponding to brain activities from the sensorimotor cortex and the visual cortex, i.e., mu rhythm and SSVEP, have been investigated and employed in constructing different types of online BCI systems. Command
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THE FRONTIERS COLLECTION
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Bernhard Graimann · Brendan Alliso
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Preface It’s an exciting time to
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Contents Brain-Computer Interfaces:
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Contributors Brendan Allison Instit
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Contributors xi Femke Nijboer Insti
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List of Abbreviations ADHD Attentio
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Brain-Computer Interfaces: A Gentle
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30 J.R. Wolpaw and C.B. Boulay by b
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32 J.R. Wolpaw and C.B. Boulay 2 Br
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34 J.R. Wolpaw and C.B. Boulay Fig.
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36 J.R. Wolpaw and C.B. Boulay Sens
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38 J.R. Wolpaw and C.B. Boulay pote
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40 J.R. Wolpaw and C.B. Boulay EEG-
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42 J.R. Wolpaw and C.B. Boulay 29.
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82 G. Pfurtscheller et al. Therefor
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84 G. Pfurtscheller et al. A B C 1
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BCIs Based on Signals from Between
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Toward Ubiquitous BCIs Brendan Z. A
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Toward Ubiquitous BCIs 359 BCI Chal
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Toward Ubiquitous BCIs 361 communic
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Toward Ubiquitous BCIs 363 facial m
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Toward Ubiquitous BCIs 365 The best
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Toward Ubiquitous BCIs 367 Across a
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Toward Ubiquitous BCIs 369 the ques
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Toward Ubiquitous BCIs 371 controll
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Toward Ubiquitous BCIs 373 controls
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Toward Ubiquitous BCIs 375 hair in
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Toward Ubiquitous BCIs 377 Table 1
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Toward Ubiquitous BCIs 381 may down
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Toward Ubiquitous BCIs 383 physicis
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Toward Ubiquitous BCIs 385 31. F.H.
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Toward Ubiquitous BCIs 387 67. G. S
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390 Index 65, 157, 163, 217, 221-23
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392 Index 208, 236, 243, 245, 260-2
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