An estimate of an upper bound for the entropy of English
Computational Linguistics
Negative inertia: a dynamic pointing function
CHI '95 Conference Companion on Human Factors in Computing Systems
Dasher—a data entry interface using continuous gestures and language models
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
The entropy of English using PPM-based models
DCC '96 Proceedings of the Conference on Data Compression
Unbounded length contexts for PPM
DCC '95 Proceedings of the Conference on Data Compression
Semantic pointing: improving target acquisition with control-display ratio adaptation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Berlin brain-computer interface
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Hex: dynamics and probabilistic text entry
Switching and Learning in Feedback Systems
Simulating the feel of brain-computer interfaces for design, development and social interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
I did that! Measuring users' experience of agency in their own actions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Machine-learning-based coadaptive calibration for brain-computer interfaces
Neural Computation
Improving BCI performance after classification
Proceedings of the 14th ACM international conference on Multimodal interaction
Artificial Intelligence in Medicine
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Designing user interfaces which can cope with unconventional control properties is challenging, and conventional interface design techniques are of little help. This paper examines how interactions can be designed to explicitly take into account the uncertainty and dynamics of control inputs. In particular, the asymmetry of feedback and control channels is highlighted as a key design constraint, which is especially obvious in current non-invasive brain-computer interfaces (BCIs). Brain-computer interfaces are systems capable of decoding neural activity in real time, thereby allowing a computer application to be directly controlled by thought. BCIs, however, have totally different signal properties than most conventional interaction devices. Bandwidth is very limited and there are comparatively long and unpredictable delays. Such interfaces cannot simply be treated as unwieldy mice. In this respect they are an example of a growing field of sensor-based interfaces which have unorthodox control properties. As a concrete example, we present the text entry application ''Hex-O-Spell'', controlled via motor-imagery based electroencephalography (EEG). The system utilizes the high visual display bandwidth to help compensate for the limited control signals, where the timing of the state changes encodes most of the information. We present results showing the comparatively high performance of this interface, with entry rates exceeding seven characters per minute.