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Brain–Computer Interfaces - Index of

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256 K.J. Miller and J.G. Ojemann<br />

areas are becoming more pronounced), and the overall scale (to the top right) is<br />

increasing. Similar one dimensional tasks were performed for tongue and hand. The<br />

hand movement was coupled to left-right cursor movement, in preparation for the<br />

combination <strong>of</strong> the two into a two dimensional task.<br />

Figure 7c, Two-Dimensional Cursor Feedback: In the last stage <strong>of</strong> the experiment,<br />

two one dimensional control signals are combined into a single cursor to<br />

target task. If the two signals are independent, as is the case here, then the transition<br />

between robust control in two one-dimensional tasks and robust control in<br />

one two-dimensional task is straightforward. The combination <strong>of</strong> hand and tongue<br />

linked features is good (as in (c)), because they are well demarcated on the precentral<br />

gyrus, but the pair chosen in any particular instance will be dependent on<br />

the coverage <strong>of</strong> the electrode array. The example shown in (c), with the frequency<br />

range 80–90 Hz demonstrates how robust, screened, features (left) can be used for<br />

robust control in one-dimensional tasks (center). The electrode for up-down control<br />

in both the one- and two-dimensional tasks was from the classic tongue area. The<br />

electrode for left-right control in both the one- and two-dimensional tasks was from<br />

the classic hand area. The one- and two-dimensional control tasks were successful<br />

(100% Left/Right 1D, 97% Up/Down 1D, and 84% 2D).<br />

7 Conclusion<br />

ECoG provides robust signals that can be used in a BCI system. Using different<br />

kinds <strong>of</strong> motor imagery, subjects can volitionally control the cortical spectrum in<br />

multiple brain areas simultaneously. The experimenter can identify salient brain<br />

areas and spectral ranges using a cue based screening task. These different kinds<br />

<strong>of</strong> imagery can be coupled to the movement <strong>of</strong> a cursor on a screen in a feedback<br />

process. By coupling them first separately, and later in concert, the subject can learn<br />

to control multiple degrees <strong>of</strong> freedom simultaneously in a cursor based task.<br />

Acknowledgements Special thanks to Gerwin Schalk for his consistent availability and insight.<br />

The patients and staff at Harborview Medical Center contributed invaluably <strong>of</strong> their time and<br />

enthusiasm. Author support includes NSF 0130705 and NIH NS07144.<br />

References<br />

1. J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan, Braincomputer<br />

interfaces for communication and control. Clin Neurophysiol, 113(6), 767–791,<br />

(2002).<br />

2. J.R. Wolpaw and D.J. McFarland, Control <strong>of</strong> a two-dimensional movement signal by a noninvasive<br />

brain-computer interface in humans. Proc Natl Acad Sci USA, 101(51), 17849–17854,<br />

(2004).<br />

3. L.R. Hochberg, et al., Neuronal ensemble control <strong>of</strong> prosthetic devices by a human with<br />

tetraplegia. Nature, 442(7099), 164–171, (2006).<br />

4. P.R. Kennedy and R.A. Bakay, Restoration <strong>of</strong> neural output from a paralyzed patient by a<br />

direct brain connection. Neuroreport, 9(8), 1707–1711, (1998).

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