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1.
Proc Natl Acad Sci U S A ; 112(13): 3920-5, 2015 Mar 31.
Article in English | MEDLINE | ID: mdl-25775550

ABSTRACT

Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain-computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain-computer interface and elicitation of an event-related potential (P300 wave).


Subject(s)
Brain-Computer Interfaces , Ear, External , Electroencephalography/instrumentation , Electroencephalography/methods , Cognition , Computers , Electrodes , Electronics , Equipment Design , Event-Related Potentials, P300 , Fractals , Humans , Signal Processing, Computer-Assisted , Signal-To-Noise Ratio
2.
IEEE Trans Neural Syst Rehabil Eng ; 23(5): 857-66, 2015 Sep.
Article in English | MEDLINE | ID: mdl-25474810

ABSTRACT

This paper presents a brain-computer interface for text entry using steady-state visually evoked potentials (SSVEP). Like other SSVEP-based spellers, ours identifies the desired input character by posing questions (or queries) to users through a visual interface. Each query defines a mapping from possible characters to steady-state stimuli. The user responds by attending to one of these stimuli. Unlike other SSVEP-based spellers, ours chooses from a much larger pool of possible queries-on the order of ten thousand instead of ten. The larger query pool allows our speller to adapt more effectively to the inherent structure of what is being typed and to the input performance of the user, both of which make certain queries provide more information than others. In particular, our speller chooses queries from this pool that maximize the amount of information to be received per unit of time, a measure of mutual information that we call information gain rate. To validate our interface, we compared it with two other state-of-the-art SSVEP-based spellers, which were re-implemented to use the same input mechanism. Results showed that our interface, with the larger query pool, allowed users to spell multiple-word texts nearly twice as fast as they could with the compared spellers.


Subject(s)
Brain-Computer Interfaces , Electroencephalography/methods , Evoked Potentials, Visual , Natural Language Processing , Pattern Recognition, Automated/methods , Word Processing/methods , Adolescent , Child , Communication Aids for Disabled , Female , Humans , Information Storage and Retrieval/methods , Machine Learning , Male , Reproducibility of Results , Sensitivity and Specificity , Young Adult
3.
IEEE Trans Neural Syst Rehabil Eng ; 21(2): 306-18, 2013 Mar.
Article in English | MEDLINE | ID: mdl-23268384

ABSTRACT

This paper presents an interface for navigating a mobile robot that moves at a fixed speed in a planar workspace, with noisy binary inputs that are obtained asynchronously at low bit-rates from a human user through an electroencephalograph (EEG). The approach is to construct an ordered symbolic language for smooth planar curves and to use these curves as desired paths for a mobile robot. The underlying problem is then to design a communication protocol by which the user can, with vanishing error probability, specify a string in this language using a sequence of inputs. Such a protocol, provided by tools from information theory, relies on a human user's ability to compare smooth curves, just like they can compare strings of text. We demonstrate our interface by performing experiments in which twenty subjects fly a simulated aircraft at a fixed speed and altitude with input only from EEG. Experimental results show that the majority of subjects are able to specify desired paths despite a wide range of errors made in decoding EEG signals.


Subject(s)
Aircraft , Electroencephalography/methods , Imagination/physiology , Man-Machine Systems , Robotics/methods , Signal Processing, Computer-Assisted , User-Computer Interface , Algorithms , Brain Mapping/methods , Humans
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