Annals of Biomedical Engineering

, Volume 35, Issue 2, pp 137–169 | Cite as

A Comprehensive Survey of Brain Interface Technology Designs

  • S. G. Mason
  • A. Bashashati
  • M. Fatourechi
  • K. F. Navarro
  • G. E. Birch


In this work we present the first comprehensive survey of Brain Interface (BI) technology designs published prior to January 2006. Detailed results from this survey, which was based on the Brain Interface Design Framework proposed by Mason and Birch, are presented and discussed to address the following research questions: (1) which BI technologies are directly comparable, (2) what technology designs exist, (3) which application areas (users, activities and environments) have been targeted in these designs, (4) which design approaches have received little or no research and are possible opportunities for new technology, and (5) how well are designs reported. The results of this work demonstrate that meta-analysis of high-level BI design attributes is possible and informative. The survey also produced a valuable, historical cross-reference where BI technology designers can identify what types of technology have been proposed and by whom.


Brain Interface Brain–Computer Interface Brain–Machine Interface Direct Brain Interface Adaptive Brain Interface BI BCI BMI DBI ABI Comparison Taxonomy Models Framework Design Meta-analysis 



acronym for assistive (or augmentative) technology

Attribute Sub-Class

sub-category of a design attribute. See Design Attribute. For examples, see Table 2

Assistive Device

the component of a Brain Interface (BI) that interacts directly with objects or people in the environment. For example, a speech synthesizer or an FES-based neuroprosthetic


acronym for Brain Interface


acronym for assistive (or augmentative) technology (AT) based on a Brain Interface (BI)

BI Transducer

a component of a Brain Interface that translates a person’s brain activity into usable control signals as shown in Fig. 1b–f. Functionally similar to other transducers like joysticks and switches

Bio-recording Technology

the class of equipment (sensors, amplifiers, converters and filters) used to measure a person’s brain activity in a BI Transducer

Continuous (fixed reference)

a signal classification; a signal of this class is a sequence of continuous amplitude values relative to a fixed reference value-like the adjustable level produced by an analog potentiometer. See other signal classes: Relative Continuous (no reference), Discrete (...) and Spatial Reference

Control Interface

a component that is added to a BI Transducer that produces a relatively low dimensional output in order to expand the control dimensionality to a level required by an Assistive Device as depicted in Fig. 1c and f. See Table  1 for examples.

Demonstration System

an experimental system (depicted in Fig.  1e and f) that demonstrates control of a BI Transducer but does not otherwise perform any useful function

Demo Device

a device used to test the controllability of a BI technology in a demonstration system (see Fig. 1e and f). For example, a model vehicle or table-mounted robotic arm

Design Attribute

an attribute of a Brain Interface technology design. See Table 1 for the list of design attributes

Discrete (with 1 NC state)

a signal classification; a signal of this class is a sequence of discrete states including one state that corresponds to the No Control state in the user. See No Control

Discrete (all IC states)

a signal classification; a signal of this class is a sequence of discrete states where all states corresponds to intentional control in the user. See Intentional Control

Discrete (with 1 unknown state)

a signal classification; a signal of this class is a sequence of discrete states where one state (the “unknown” state) is reserved for uncertain classifications

Endogenous Transducer

a BI Transducer design that responds to spontaneous control signals from the user

Exogenous Transducer

a BI Transducer design that responds to control signals evoked from the user using an external stimulator

Feature Extractor

a component of a BI Transducer that translates the input brain signal into a feature vector correlated to a neurological phenomenon. This component is sometimes referred to as noise reduction, filtering, preprocessing or spike detection/sorting

Feature Translator

a component of a BI Transducer that translates the feature vector into a useful control signal. This component is sometimes referred to as a Feature Classifier, Classifier or “decoding function” (or something similar)

Intentional Control

a user state when the user is attempting to affect the output of the Brain Interface


acronym for Intentional Control

Neurological Phenomenon

the phenomenon (or phenomena) used to control a BI Transducer. For example, a P300 response in EEG to an oddball stimulus is a well-studied phenomenon employed in several BI Transducers. Another well-known phenomenon is the increase in neural firing rates measured in microelectrodes as neural activity increases

No Control

a user state when the user is not attempting to affect the output of the Brain Interface. For example, resting, monitoring, thinking, and daydreaming are all possible No Control states


acronym for No Control

Relative Continuous (no reference)

a signal classification; a signal of this class is a sequence of changes to previous amplitude values-like the output of a mouse. See Continuous (fixed reference)

Spatial Reference

a signal classification; a signal of this class is a sequence of 2-D spatial positions (similar to signals output by an eye tracker, a touchscreen or stylus mechanism)



This work was conducted at the Neil Squire Society Brain Interface Laboratory in Vancouver, Canada, with support from the Canadian Institutes of Health Research grant MOP-62711, the Natural Sciences and Engineering Research Council of Canada, grant 90278-02. The authors would like to recognize Jaimie Borisoff and Adriane Randolph for their insightful feedback during the preparation of the manuscript. We would like to acknowledge that the second and third authors made equal contributions to this work.


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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • S. G. Mason
    • 1
  • A. Bashashati
    • 2
  • M. Fatourechi
    • 2
  • K. F. Navarro
    • 3
  • G. E. Birch
    • 1
    • 2
  1. 1.Neil Squire SocietyBrain Interface LaboratoryBurnabyCanada
  2. 2.Department of Electrical and Computer EngineeringThe University of British ColumbiaVancouverCanada
  3. 3.Department of Computer SystemsUniversity of TechnologySydneyAustralia

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