Analyzing trends in brain interface technology: a method to compare studies.
Continued progress in the field of Brain Interface (BI) research has encouraged the rapid expansion of the BI community over the last two decades. As the number of BI researchers and organizations steadily increases, newer and more advanced technologies are constantly produced, evaluated, and reported. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the diversity of the field has hindered the development of objective methods of comparison. This paper introduces a new method for directly comparing studies of BI technology based on the theoretical models and taxonomy proposed by Mason, Moore, and Birch. The effectiveness of the proposed method was demonstrated by interpreting and comparing a representative set of 21 BI studies. The method allowed us to 1) identify the salient aspects of a specific BI study, 2) identify what has been reported and what has been omitted, 3) facilitate a complete and objective comparison with other studies, and 4) characterize overall trends, areas of inactivity, and reporting practices.
Jackson
M M Moore
MM
College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA. mooremelody@bellsouth.net
Mason
S G
SG
Birch
G E
GE
D000465:Algorithms; D001709:Biotechnology / Q000379:methods* / Q000639:trends*; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D012107:Research Design; D013673:Technology Assessment, Biomedical / Q000379:methods*; D014584:User-Computer Interface