A note on brain actuated spelling with the Berlin brain-computer interface

Brain-Computer Interfaces (BCIs) are systems capable of decoding neural activity in real time, thereby allowing a computer application to be directly controlled by the brain. Since the characteristics of such direct brain-tocomputer interaction are limited in several aspects, one major challenge in BCI research is intelligent front-end design. Here we present the mental text entry application ‘Hex-o-Spell’ which incorporates principles of Human-Computer Interaction research into BCI feedback design. The system utilises the high visual display bandwidth to help compensate for the extremely limited control bandwidth which operates with only two mental states, where the timing of the state changes encodes most of the information. The display is visually appealing, and control is robust. The effectiveness and robustness of the interface was demonstrated at the CeBIT 2006 (world’s largest IT fair) where two subjects operated the mental text entry system at a speed of up to 7.6 char/min

Investigating five key predictive text entry with combined distance and keystroke modelling

This paper investigates text entry on mobile devices using only five-keys. Primarily to support text entry on smaller devices than mobile phones, this method can also be used to maximise screen space on mobile phones. Reported combined Fitt's law and keystroke modelling predicts similar performance with bigram prediction using a five-key keypad as is currently achieved on standard mobile phones using unigram prediction. User studies reported here show similar user performance on five-key pads as found elsewhere for novice nine-key pad users

Pickup usability dominates: a brief history of mobile text entry research and adoption

Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shrank below laptop size: mobile devices are simply too small to have a traditional full-size keyboard. There has been a profusion of research into text entry techniques for smaller keyboards and touch screens: some of which have become mainstream, while others have not lived up to early expectations. As the mobile phone industry moves to mainstream touch screen interaction we will review the range of input techniques for mobiles, together with evaluations that have taken place to assess their validity: from theoretical modelling through to formal usability experiments. We also report initial results on iPhone text entry speed

Comparison Among Ambiguous Virtual Keyboards For People With Severe Motor Disabilities

This paper presents an exhaustive study on the different topologies of ambiguous soft keyboards, analyzing the text entry average time per character and the average number of user inputs necessary for its creation. Various topologies and design criteria are investigated. In addition, an analytical model is also proposed. This model allows one to compare among different topologies and estimate the sensitivity that different keyboards offer when compared with dictionary hit rates. It has been found that ambiguous keyboards, with six keys, are better to us

Measuring Performance of Virtual Keyboards Based on Cyclic Scanning

This paper presents an exhaustive study into the different topologies of virtual ambiguous keyboards that operate by scanning techniques, analyzing the text entry average time (tc) and the average number of user inputs (UIc) per character. An mathematical model shows that in comparison with unambiguous one, text entry, in multi-tap mode, doesn’t offers better performance,because both tc and UIc are greater in them. Another method of text entry, called Tnk (Text in n keys), offers improvement with respect to unambiguous keyboards. But solely highly ambiguous keyboard (4-keys keyboards) shows a jointly reduction in tc and UIc . Results obtained with the model do to focus on highly ambiguous keyboard. This paper demonstrate, using simulation with extensive text, that character prediction with TnK mode only have better performance than unambiguous keyboard with character prediction in UIc parameter. Another techniques of text entry are also studied.Junta de Andalucía p08-TIC-363

Fast and precise touch-based text entry for head-mounted augmented reality with variable occlusion

We present the VISAR keyboard: An augmented reality (AR) head-mounted display (HMD) system that supports text entry via a virtualised input surface. Users select keys on the virtual keyboard by imitating the process of single-hand typing on a physical touchscreen display. Our system uses a statistical decoder to infer users’ intended text and to provide error-tolerant predictions. There is also a high-precision fall-back mechanism to support users in indicating which keys should be unmodified by the auto-correction process. A unique advantage of leveraging the well-established touch input paradigm is that our system enables text entry with minimal visual clutter on the see-through display, thus preserving the user’s field-of-view. We iteratively designed and evaluated our system and show that the final iteration of the system supports a mean entry rate of 17.75wpm with a mean character error rate less than 1%. This performance represents a 19.6% improvement relative to the state-of-the-art baseline investigated: A gaze-then-gesture text entry technique derived from the system keyboard on the Microsoft HoloLens. Finally, we validate that the system is effective in supporting text entry in a fully mobile usage scenario likely to be encountered in industrial applications of AR HMDs.Per Ola Kristensson was supported in part by a Google Faculty research award and EPSRC grants EP/N010558/1 and EP/N014278/1. Keith Vertanen was supported in part by a Google Faculty research award. John Dudley was supported by the Trimble Fund