TY - JOUR T1 - A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening. JF - J Neural Eng Y1 - 2014 A1 - Jeremy Jeremy Hill A1 - Ricci, Erin A1 - Haider, Sameah A1 - McCane, Lynn M A1 - Susan M Heckman A1 - Jonathan Wolpaw A1 - Theresa M Vaughan KW - Adult KW - Aged KW - Algorithms KW - Auditory Perception KW - brain-computer interfaces KW - Communication Aids for Disabled KW - Electroencephalography KW - Equipment Design KW - Equipment Failure Analysis KW - Female KW - Humans KW - Male KW - Man-Machine Systems KW - Middle Aged KW - Quadriplegia KW - Treatment Outcome KW - User-Computer Interface AB - OBJECTIVE: Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words 'yes' and 'no') without loss of performance, and whether the system could be used by people in the locked-in state. APPROACH: We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions. MAIN RESULTS: The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects' performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects. SIGNIFICANCE: Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in. VL - 11 UR - http://www.ncbi.nlm.nih.gov/pubmed/24838278 IS - 3 ER - TY - JOUR T1 - Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential. JF - J Neural Eng Y1 - 2009 A1 - Martens, S M M A1 - Jeremy Jeremy Hill A1 - Farquhar, Jason A1 - Schölkopf, B KW - Algorithms KW - Brain KW - Cognition KW - Computer Simulation KW - Electroencephalography KW - Event-Related Potentials, P300 KW - Humans KW - Models, Neurological KW - Pattern Recognition, Automated KW - Photic Stimulation KW - Semantics KW - Signal Processing, Computer-Assisted KW - Task Performance and Analysis KW - User-Computer Interface KW - Writing AB -

We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.

VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/19255462 IS - 2 ER - TY - JOUR T1 - Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Jeremy Jeremy Hill A1 - Lal, T.N A1 - Schröder, Michael A1 - Hinterberger, T. A1 - Wilhelm, Barbara A1 - Nijboer, F A1 - Mochty, Ursula A1 - Widman, Guido A1 - Elger, Christian A1 - Schölkopf, B A1 - Kübler, A. A1 - Niels Birbaumer KW - Algorithms KW - Artificial Intelligence KW - Cluster Analysis KW - Computer User Training KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Paralysis KW - Pattern Recognition, Automated KW - User-Computer Interface AB -

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792289 IS - 2 ER -