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314 Y. Li et al. P300-based, and ERD/ERS-based BCIs. For more details on these brain patterns, please refer to Chapter “Brain signals for Brain–Computer Interfaces” of this book as well as the related references listed below. 3.1 SSVEP-based BCIs SSVEP is one of the neurophysiological signals suitable for BCIs. Figure 5 shows a stimulation paradigm for a SSVEP-based BCI in which each button flashes with a specific frequency labeled in the figure. The labeled frequencies do not appear in the real interface. The user can select a number by gazing at a corresponding button. The SSVEP elicited by the visual stimulus is composed of a series of components whose frequencies are exact integer multiples of the flickering frequency of the stimulus. Several SSVEP-based BCI systems have been developed [6, 25, 26]. The amplitude and phase of SSVEP are highly sensitive to stimulus parameters such as the flashing rate and contrast of stimulus modules [5]. Gaze shifting may improve performance with SSVEP-based BCIs, but is not required, at least for some users. The importance of gaze shifting depends heavily on the display and task. For example, if there are many targets, or if they are located outside the fovea, gaze shifting may be essential [25, 26]. One possible feature extraction method used in SSVEP-based BCIs is as follows [6]: First, two EEG channels are recorded from electrodes placed over the visual cortex. Next, the EEG signal is filtered using a band-pass filter for removing noise. The EEG signal is subsequently segmented using a Hamming window for estimating the dominant frequency that corresponds to the flashing rate of the button of interest. The spectra of each segment is estimated using a fast Fourier transformation (FFT). Finally, a feature vector is then constructed from the spectra for classification. 3.2 The P300-based BCI P300 is a positive displacement of EEG amplitude occurring around 300 ms after a stimulus appears. The P300, like SSVEP, is based on selective attention. This mental Fig. 5 A stimulation paradigm for eliciting SSVEP
Digital Signal Processing and Machine Learning 315 strategy does usually not require much of a training, in contrast to other mental strategies like motor imagery [3, 4]. As the amplitude of P300 is only a few micro volts compared to EEG, which is on the order of a few tens of microvolts, many trials must often be averaged to distinguish the P300 from other activity. Hence. a major challenge in P300-based BCIs is to classify the P300 response from the background EEG with a minimum number of averaging. One possible feature extraction method used in P300-based BCI is described as follows: Firstly, the EEG signals are filtered using a band pass-filter with the range 0.1–20 Hz. Segments of the filtered EEG signal are then extracted from the start of the stimulus. The feature vector is formed by concatenating the segments from all the electrodes. A smaller feature vector can be extracted by using a lowpass filter and downsampling. The reader is referred to the illustrative example in Sect. 7 for more details. 3.3 ERD/ERS-based BCI In ERD/ERS-based BCIs, the power or the amplitude of filtered brain signals is used as features that reflect ERD/ERS. For example, the amplitude of mu/beta rhythms is used in a cursor control BCI system in [27]. By modulating the amplitude of mu/beta rhythms, the user can move the cursor to the target. The following describes two feature extraction methods for ERD/ERS-based BCIs, namely, band power feature extraction and autoregressive model coefficients. Note that features describing the power of the signals are often used in combination with CSP. This is because CSP maximizes the power (variance) ratio between two conditions, and thus power features are ideally suited [22, 28, 29]. 3.3.1 Power Feature Extraction Based on Band-Pass Filter Since ERD/ERS is defined as the the change in power of a frequency band (mu/beta band) in brain signals, the band pass power feature is a simple method of extracting ERD/ERS features. This method generally comprises the following steps [30]: 1. Apply a spatial filter such as Common average reference, Laplacian, or CSP on subject-specific EEG channels. 2. Band-pass filter on subject-specific frequency ranges. 3. Square the EEG samples to obtain power samples. 4. Average over time samples to smooth the data and reduce variability. 5. Log transform to make the distribution more Gaussian. The subject-specific EEG channels and frequency ranges may be determined from training data, and there may be multiple band power features for each subject [30].
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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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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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98 E.W. Sellers et al. Fig. 1 Three
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108 E.W. Sellers et al. 5 SMR-Based
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Detecting Mental States by Machine
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138 Y. Wang et al. which has been e
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140 Y. Wang et al. After many studi
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142 Y. Wang et al. Left Hand Right
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144 Y. Wang et al. 0-degree 60-degr
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146 Y. Wang et al. 3.1.2 Stimulatio
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150 Y. Wang et al. be summarized as
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154 Y. Wang et al. 22. Y. Wang, R.
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Non Invasive BCIs for Neuroprosthes
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BCIs Based on Signals from Between
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Page 362 and 363: Toward Ubiquitous BCIs Brendan Z. A
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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 379 conversa
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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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