trends in brain computer interfaces (bci)

Annals of Biomedical Engineering , Vol. 34, No. 5, May 2006 ( C 2006) pp. 859–878 DOI: 10.1007/s104 39-005-9055- 7 Analyzing Trends in Brain Interface Technology: A Method to Compare Studies M. M. MOORE JACKSON, 1 S. G. MASON, 2 and G. E . BIRCH 2, 3 1 College of Computing, Georgia Institute of T echnology, Atlanta, Georgia; 2 Neil Squire Foundation, 220-2250 Boundary Road, Burnab y, BC , Canada V5M 4L9 ; and 3 Department of Electrical and Computer Engineering, The University of British Columbia, 2356 Main Mall, Vancouver, Canada V6T 1Z4 (Received 6 May 2005; accepted 13 October 2005; published online: 20 April 2006) Abstract—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 techno logies are const antly produ ced, eva luated, and repor ted. 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 compar ison. Thi s paper int rod ucesa newmethodfor dir ect ly comparing studies of BI technology based on the theoretical models and taxonomy proposed by Mason, Moore, and Birch. The effec- ti ven ess of the propos ed met hod was demons tra ted by int erpret ing and comparing a repres entati ve 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 repor ted and what has been omitted, 3) facilitate a complete and objective comparison with other studies, and4) cha rac ter ize overa ll tre nds , areas of ina cti vit y, and report ing practices. Keywords—Brain interf ace, Brain –compu ter interf ace, Brain– machine interface, Direct brain interface, BI, BCI, BMI, DBI, Brain interface comparison, Taxonomy, Models, Framework. INTRODUCTION The field of Brain Interface (BI) technology 1 is truly int erdisc ipl ina ry , inc orp ora ting res earche rs from neu - roscience, psych ology , compu ter scien ce, engin eerin g, medicine, and other technical and health care fields. This pronounced diversity strengthens the BI field with a myriad of perspectives and approaches, which encourages inno- vation and invention. However, the lack of common per- spective and terminology has hindered the development of objective methods of comparison. This issue, which is a recognized deficiency in the field, 15,16,26 is exacerbated by Addr esscorre sponde nceto M. M. Moor e Jac kson, Coll egeof Comp ut- ing, Georgia Institute of T echnology, Atlanta, Geor gia. Electronic mail: [email protected] 1 In thi s wor k, theterms Bra in–Comp ute r Inte rfa ce (BCI ) res ear ch, Bra in– Machine Interface (BMI) research, and Direct Brain Interface (DBI) research are included under the umbrella term Brain Interface (BI) research. the rapid growth in the ranks of BI researchers and the field’s dependence on collaborations due to limited funding and small, specialized subject populations. Consequently, obj ect iv e met hod s of compar iso n bas ed on common mod els and langua ge are cri tic al to the ev olution of app ropria te and effectiv e BI technology . This paper presents and illustrates a method to objec- tively classify and compare the salient features of BI tech- nol ogy studies. We demons tra te tha t the met hod is ef fec ti ve and, if a sufficient number of studies are compared, the method can be used to detect research trends and areas of inactivity in the BI field. The method employs a classification template that is based on the theoretical taxonomy proposed by Mason, Moore, and Birch, 15 which is summa- rized in Section 2. Section 3 describes the application of the method to a representative set of BI studies. The paper concludeswith the res ult s of thi s compar ison andcomments on observed research trends and reporting practices. CLASSIFICA TION TEMPLATE The proposed method uses a classification template to assist people in characterizing individual studies, presented in Table 1. The elements of Mason, Moore, and Birch’s BI study taxonomy, which compose the left side of template, are grouped into three main categories: Synopsis, Data Collection, and Analysis. The study attributes, shown in bold on the left side of Table 1, represent the basic items required to describe a study of BI technology. Attributes defined under Synopsis identify key aspects of an evaluation that are used to characterize and compare studies. Attributes listed under Data Collection relate to detailed methods for collecting data from subjects, and Analysis attributes describe the analysis methods and outcomes. For a complete description and discussion of the categories and attributes, refer to. 15 The right side of the template provides attribute values, which are the specific details of a BI study that is being class ified. Attribut e valu es may consi st of eithe r attribute 859 0090-6964/06/0500-0859/0 C 2006 Biomedical Engineering Society

trends in brain computer interfaces (bci)

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