Towards an independent brain–computer interface using steady state visual evoked potentials
Introduction
Many people with motor disabilities cannot use conventional interfaces such as mice or keyboards. Although some of these users can use other interfaces such as eye trackers or EMG switches (Cook and Hussey, 2002), some severely disabled users require a means of communication that does not rely on motor control at all. Brain–computer interface (BCI) systems translate direct measures of brain activity into messages or commands. A variety of BCI systems have been described in the literature and typically are categorized according to the cognitive and neural activity needed for control (for review, see Kübler et al., 2001, Wolpaw et al., 2002, Allison, 2003, Kübler and Neumann, 2005, Jackson et al., 2006, Allison et al., 2007).
One type of BCI utilizes changes in steady state visual evoked potentials (SSVEPs). In this approach, a subject views one or more stimuli that each oscillate at a different constant frequency. When the subject focuses attention on one such stimulus, EEG activity may be detected over occipital areas at corresponding frequencies. Hence, an SSVEP BCI can infer user intent by measuring EEG activity at a specific frequency or frequencies over occipital areas. Although SSVEP BCIs work with healthy subjects (e.g., Middendorf et al., 2000, Cheng et al., 2002, Lalor et al., 2005) and subjects with moderate disabilities (Sutter, 19921; Wang et al., 2004), they have not been validated with subjects unable to control gaze.
The prevailing view in the BCI literature is that SSVEP BCIs would not work in such subjects. SSVEP BCI articles typically note that subjects were told to shift gaze (Sutter, 1992, Middendorf et al., 2000, Cheng et al., 2002, Gao et al., 2003). Two BCI reviews (Kübler et al., 2001, Wolpaw et al., 2002) define SSVEP BCIs as “dependent” BCIs, meaning that they use EEG features that depend on muscle activity and thus would not work in patients without control over that activity. SSVEP BCI development would then be less important, as other assistive technologies based on gaze direction might be more effective (Cook and Hussey, 2002).
However, strong evidence from the visual attention literature suggests that people can shift attention among visual stimuli without shifting gaze. This phenomenon, called covert attention, has been verified in many human studies in which gaze shifting was carefully measured (e.g., Van Voorhis and Hillyard, 1977, Regan, 1989, Mangun and Buck, 1998, Golla et al., 2005). It has also been shown in SSVEP studies in which covert attention to an oscillating region or regions resulted in increased SSVEP activity at corresponding frequencies (Müller et al., 1998, Müller et al., 2003, Müller and Hillyard, 2000). These SSVEP studies were designed to rule out the possibility that results could be explained by shifting gaze. MEG work also shows that humans can produce changes in brain activity by attending to one of two overlapping images (Chen et al., 2003). Thus, an independent BCI based on covert attention may be a viable communication system even for users without gaze control.
The main goal of the study was to determine whether selective attention to one of two overlapping images would produce enough change in SSVEP activity to control an online BCI. This study compares an SSVEP display using non-overlapping checkerboxes to displays using overlapping stimuli. To determine whether color would help distinguish overlapping stimuli, two types of overlapping stimuli were used: colored and black/white (BW).
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Subjects
Subjects were 14 healthy adults (8 women, 6 men; age range 18–29 years, mean = 19.7, SD = 2.9), 11 of whom were undergraduate students at Georgia State University. All subjects were free of neurological or psychiatric disorders or medications known to adversely affect EEG recording. None had prior experience with EEG recording or BCIs. All subjects signed a consent form and earned credit in a psychology course or $10/hour for their participation. The nature and purpose of the study was explained to
Statistical analysis
Table 2 summarizes results for all subjects for each condition.
Averaged across all subjects, the three conditions (checkerbox, BW linebox, and color linebox) produced maximum R2 values of .34, .10, and .12, respectively. Subjects in group two produced slightly greater differences to the checkerbox and color linebox display, and slightly smaller differences in the BW linebox condition, than subjects in group one. Female subjects produced greater differences than male subjects in all conditions.
Discussion
This study explored SSVEP activity elicited by attention to one of two images. In about half the subjects, selective attention to one of two overlapping images produced SSVEP differences robust enough to allow effective communication in an online BCI (Sheikh et al., 2003). Further research is warranted to validate an online adaptation of this BCI approach in a real world environment, ideally in typical users’ homes.
Acknowledgements
This work was supported in part by National Institutes of Health Grant EB00856 (National Institute of Biomedical Imaging and Bioengineering and National Institute of Neurological Disorders and Stroke). The authors thank Christopher Agocs, Dan Ratanasit, Luke McCampbell, and Steve Hillyard for technical advice, and Theresa Vaughan for comments on design and writing.
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