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Omar, Cyrus; Akce, Abdullah; Johnson, Miles; Bretl, Timothy; Ma, Rui; Maclin, Edward; McCormick, Martin; Coleman, Todd P.
doi: 10.1080/10447318.2011.535749pmid: N/A
This article presents a new approach to designing brain–computer interfaces (BCIs) that explicitly accounts for both the uncertainty of neural signals and the important role of sensory feedback. This approach views a BCI as the means by which users communicate intent to an external device and models intent as a string in an ordered symbolic language. This abstraction allows the problem of designing a BCI to be reformulated as the problem of designing a reliable communication protocol using tools from feedback information theory. Here, this protocol is given by a posterior matching scheme. This scheme is not only provably optimal but also easily understood and implemented by a human user. Experimental validation is provided by an interface for text entry and an interface for tracing smooth planar curves, where input is taken in each case from an electroencephalograph during left- and right-hand motor imagery.
Randolph, Adriane B.; Jackson, Melody Moore; Karmakar, Saurav
doi: 10.1080/10447318.2011.535750pmid: N/A
Brain–computer interfaces (BCIs) offer users with severe motor disabilities a nonmuscular input channel for communication and control but require that users achieve a level of literacy and be able to harness their appropriate electrophysiological responses for effective use of the interface. There is currently no formalized process for determining a user's aptitude for control of various BCIs without testing on an actual system. This study presents how basic information captured about users may be used to predict modulation of mu rhythms, electrical variations in the motor cortex region of the brain that may be used for control of a BCI. Based on data from 55 able-bodied users, we found that the interaction of age and daily average amount of hand-and-arm movement by individuals correlates to their ability to modulate mu rhythms induced by actual or imagined movements. This research may be expanded into a more robust model linking individual characteristics and control of various BCIs.
Zander, Thorsten O.; Gaertner, Matti; Kothe, Christian; Vilimek, Roman
doi: 10.1080/10447318.2011.535752pmid: N/A
A Brain–Computer Interface (BCI) provides a new communication channel for severely disabled people who have completely or partially lost control over muscular activity. It is questionable whether a BCI is the best choice for controlling a device if partial muscular activity still is available. For example, gaze-based interfaces can be utilized for people who are still able to control their eye movements. Such interfaces suffer from the lack of a natural degree of freedom for the selection command (e.g., a mouse click). One workaround for this problem is based on so-called dwell times, which easily leads to errors if the users do not pay close attention to where they are looking. We developed a multimodal interface combining eye movements and a BCI to a hybrid BCI, resulting in a robust and intuitive device for touchless interaction. This system especially is capable of dealing with different stimulus complexities.
Li, Yueqing; Nam, Chang S.; Shadden, Barbara B.; Johnson, Steven L.
doi: 10.1080/10447318.2011.535753pmid: N/A
As a nonmuscular communication and control system for people with severe motor disabilities, brain–computer interface (BCI) has found several applications. Although a few empirical studies of BCI user performance do exist, little to no research has specifically evaluated the impact of contributing factors on user performance in the BCI applications. To that end, our within-subjects design compared the impact of two different types of interface (ABC interface vs. frequency-based interface) and three levels of screen size (computer monitor, global positioning system, and cell phone screen) of a P300-based BCI application, P300 Speller, on user performance (accuracy, information transfer rate, amplitude, and latency) and usage preference. Ten participants with neuromuscular disabilities such as amyotrophic lateral sclerosis and cerebral palsy and 10 nondisabled participants were asked to type six, 10-character phrases in the P300 Speller. The overall accuracy was 79.7% for the nondisabled participants and 28.7% for participants with motor disabilities. The results showed that interface type and screen size have significant effects on user performance and usage preference, with varying degree of impact to participants with and without motor disabilities. Specifically, participants typed significantly more accurately in frequency-based interface and computer monitor screen. The results of this study should provide invaluable insights to the future research of P300-based BCI applications.
Mehta, Nishant A.; Hameed, Sadhir Hussain S.; Jackson, Melody Moore
doi: 10.1080/10447318.2011.535755pmid: N/A
We evaluate the performance of 18 healthy subjects on a steady-state visually evoked potential brain–computer interface (BCI) under variation of two general control parameters. The BCI is a simple game amenable to performance measures such as the bitrate, decision accuracy, and optimality ratios based on an ideal human–machine system. The two parameters studied are the electroencephalography recording history length used to form a decision and the number of consecutive identical decisions that must be recognized before feedback is provided. To maximize the bitrate, it appears optimal to minimize the number of consecutive identical decisions required for feedback. When the task of interest often requires making the same decision multiple times in a row, a larger history of data seems preferable. When good performance on a task demands that decisions change rapidly, a smaller history seems optimal. Ultimately, we plan to connect this work to choosing appropriate control parameters for efficient wheelchair control by a BCI.
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This study compared a conventional P300 speller brain–computer interface (BCI) to one used in conjunction with a predictive spelling program. Performance differences in accuracy, bit rate, selections per minute, and output characters per minute (OCM) were examined. An 8 × 9 matrix of letters, numbers, and other keyboard commands was used. Participants (N = 24) were required to correctly complete the same 58 character sentence (i.e., correcting for errors) using the predictive speller (PS) and the nonpredictive speller (NS), counterbalanced. The PS produced significantly higher OCMs than the NS. Time to complete the task in the PS condition was 12 min 43 s as compared to 20 min 20 sec in the NS condition. Despite the marked improvement in overall output, accuracy was significantly higher in the NS paradigm. P300 amplitudes were significantly larger in the NS than in the PS paradigm, which is attributed to increased workload and task demands. These results demonstrate the potential efficacy of predictive spelling in the context of BCI.