A plug-and-play brain-computer interface to operate commercial assistive technology

Abstract

Purpose: To determine if a brain-computer interface (BCI) could be used as a plug-and-play input device to operate commercial assistive technology (AT), and to quantify the performance impact of such operation.

Method: Using a hardware device designed in our lab, participants (11 with amyotrophic lateral sclerosis, 22 controls) were asked to operate two devices using a BCI. Results were compared to traditional BCI operation by the same users. Performance was assessed using both accuracy and BCI utility, a throughput metric. 95% confidence bounds on performance differences were developed using a linear mixed model.

Results: The observed differences in accuracy and throughput were small and not statistically significant. The confidence bounds indicate that if there is a performance impact of using a BCI to control an AT device, the impact could easily be overcome by the benefits of the AT device itself.

Conclusions: BCI control of AT devices is possible, and the performance difference appears to be very small. BCI designers are encouraged to incorporate standard outputs into their design to enable future users to interface with familiar AT devices.

Implications for rehabilitation: Brain-computer interface (BCI) control of assistive technology (AT) devices is possible. The performance impact of such control is low when BCIs are commercially available, AT providers can use a BCI as an input device to existing AT devices already in use by their clients.

Conflict of interest statement

The authors have no commercial interest in the work.

Declaration of Interest

This work was supported in part by the National Institute of Child Health and Human Development (NICHD), the National Institutes of Health (NIH) under Grant No. R21HD054697, the Department of Education’s (DoE) National Institute on Disability and Rehabilitation Research (NIDRR) under grant number H133G090005 and the National Science Foundation (NSF) Graduate Research Fellowship under Grant No. DGE 0718128. The content is solely the responsibility of the authors and does not necessarily represent the official views of NICHD, NIH, DoEd, NIDRR, or NSF.

Figures

Figure 1

A grid used to control a Dynawrite text-to-speech device. Users attend to the selection of their choice, noting when the selection flashes (currently, the fourth row is flashing).

Figure 2

Example sentence, actual letters selected, target text including error correction, and correctness of each selection (1 indicates correct, 0 indicates incorrect). Note that if no incorrect selections were made, Sentence and Target would be a perfect match and the backspace (◀) would not appear. In this example, 26 of 29 selections were correct, producing an accuracy of 90%.

Figure 3

Average accuracy by device for each participant. Top) Control participants; Bottom) Participants with ALS. Error bars shows standard deviation across sessions.

Figure 4

Average BCI accuracy vs. ALS-FRS-r.