Usability and Cost-effectiveness in Brain-Computer Interaction: Is it User Throughput or Technology Related?

In recent years, Brain-Computer Interfaces (BCIs) have been steadily gaining ground in the market, used either as an implicit or explicit input method in computers for accessibility, entertainment or rehabilitation. Past research in BCI has heavily neglected the human aspect in the loop, focusing mostly in the machine layer. Further, due to the high cost of current BCI systems, many studies rely on low-cost and low-quality equipment with difficulties to provide significant advancements in physiological computing. Open-Source projects are offered as alternatives to expensive medical equipment. Nevertheless, the effectiveness of such systems over their cost is still unclear, and whether they can deliver the same level of experience as their more expensive counterparts. In this paper, we demonstrate that effective BCI interaction in a Motor-Imagery BCI paradigm can be accomplished without requiring high-end/high-cost devices, by analyzing and comparing EEG systems ranging from open source devices to medically certified systems.

[1]  C. Neuper,et al.  The effect of distinct mental strategies on classification performance for brain-computer interfaces. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[2]  Daniel Afergan,et al.  Dynamic difficulty using brain metrics of workload , 2014, CHI.

[3]  Guillaume Gibert,et al.  OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments , 2010, PRESENCE: Teleoperators and Virtual Environments.

[4]  Thierry Dutoit,et al.  Performance of the Emotiv Epoc headset for P300-based applications , 2013, Biomedical engineering online.

[5]  Brendan Z. Allison,et al.  Could Anyone Use a BCI? , 2010, Brain-Computer Interfaces.

[6]  Yoshifumi Kitamura,et al.  Things happening in the brain while humans learn to use new tools , 2003, CHI '03.

[7]  Johan Eriksson,et al.  Effects of interactivity and 3D-motion on mental rotation brain activity in an immersive virtual environment , 2010, CHI.

[8]  D. L. Schomer,et al.  Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields , 2012 .

[9]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[10]  J. Manuel Cano Izquierdo,et al.  Are low cost Brain Computer Interface headsets ready for motor imagery applications? , 2016, Expert Syst. Appl..

[11]  Benjamin Blankertz,et al.  Towards a Cure for BCI Illiteracy , 2009, Brain Topography.

[12]  Guillaume Gibert,et al.  xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface , 2009, IEEE Transactions on Biomedical Engineering.

[13]  E. Sellers,et al.  How many people are able to control a P300-based brain–computer interface (BCI)? , 2009, Neuroscience Letters.

[14]  Anton Nijholt,et al.  Usability of Three Electroencephalogram Headsets for Brain-Computer Interfaces: A Within Subject Comparison , 2015, Interact. Comput..

[15]  D. Orlikowski,et al.  A comparison of recording modalities of P300 event-related potentials (ERP) for brain-computer interface (BCI) paradigm , 2013, Neurophysiologie Clinique/Clinical Neurophysiology.

[16]  Fabien Lotte,et al.  On the need for alternative feedback training approaches for BCI , 2012 .

[17]  Yijun Wang,et al.  Brain-Computer Interfaces Based on Visual Evoked Potentials , 2008, IEEE Engineering in Medicine and Biology Magazine.

[18]  Anthony J. Ries,et al.  Usability of four commercially-oriented EEG systems , 2014, Journal of neural engineering.

[19]  Lopes Da Silva Fh,et al.  Analysis of EEG non-stationarities. , 1978 .

[20]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[21]  M. Molinari,et al.  Brain–computer interface boosts motor imagery practice during stroke recovery , 2015, Annals of neurology.

[22]  Sandra G. Hart,et al.  Nasa-Task Load Index (NASA-TLX); 20 Years Later , 2006 .

[23]  Dennis J. McFarland,et al.  Should the parameters of a BCI translation algorithm be continually adapted? , 2011, Journal of Neuroscience Methods.

[24]  Roel Vertegaal,et al.  Using mental load for managing interruptions in physiologically attentive user interfaces , 2004, CHI EA '04.

[25]  G. Pfurtscheller,et al.  ERD/ERS patterns reflecting sensorimotor activation and deactivation. , 2006, Progress in brain research.

[26]  H. Jasper Report of the committee on methods of clinical examination in electroencephalography , 1958 .

[27]  Sergi Bermúdez i Badia,et al.  Optimizing Performance of Non-Expert Users in Brain-Computer Interaction by Means of an Adaptive Performance Engine , 2015, BIH.

[28]  G. Pfurtscheller,et al.  Graz-BCI: state of the art and clinical applications , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  Sergi Bermúdez i Badia,et al.  RehabNet: A distributed architecture for motor and cognitive neuro-rehabilitation , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[30]  G. Prasad,et al.  Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study , 2010, Journal of NeuroEngineering and Rehabilitation.

[31]  Desney S. Tan,et al.  Using a low-cost electroencephalograph for task classification in HCI research , 2006, UIST.

[32]  Christian Mühl,et al.  Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design , 2013, Front. Hum. Neurosci..