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.
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.
Authors: J R Wolpaw; N Birbaumer; W J Heetderks; D J McFarland; P H Peckham; G Schalk; E Donchin; L A Quatrano; C J Robinson; T M Vaughan Journal: IEEE Trans Rehabil Eng Date: 2000-06
Authors: Jonathan R Wolpaw; Niels Birbaumer; Dennis J McFarland; Gert Pfurtscheller; Theresa M Vaughan Journal: Clin Neurophysiol Date: 2002-06 Impact factor: 3.708
Authors: D B Ryan; G E Frye; G Townsend; D R Berry; S Mesa-G; N A Gates; E W Sellers Journal: Int J Hum Comput Interact Date: 2011-01-01 Impact factor: 3.353
Authors: Petra Kaufmann; Gilbero Levy; Jacquelina Montes; Richard Buchsbaum; Alexandra I Barsdorf; Vanessa Battista; Rachel Arbing; Paul H Gordon; Hiroshi Mitsumoto; Bruce Levin; John L P Thompson Journal: Amyotroph Lateral Scler Date: 2007-02
Authors: Ana C Silveira; Íbis A P Moraes; Giovanna P Vidigal; Amanda O Simcsik; Renata M Rosa; Francis M Favero; Susi M S Fernandes; David M Garner; Luciano V Araújo; Marcelo Massa; Luiz C M Vanderlei; Talita D Silva; Carlos B M Monteiro Journal: Biomed Res Int Date: 2022-02-09 Impact factor: 3.411