Mental workload during brain–computer interface training

It is not well understood how people perceive the difficulty of performing brain–computer interface (BCI) tasks, which specific aspects of mental workload contribute the most, and whether there is a difference in perceived workload between participants who are able-bodied and disabled. This study evaluated mental workload using the NASA Task Load Index (TLX), a multi-dimensional rating procedure with six subscales: Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, and Frustration. Able-bodied and motor disabled participants completed the survey after performing EEG-based BCI Fitts' law target acquisition and phrase spelling tasks. The NASA-TLX scores were similar for able-bodied and disabled participants. For example, overall workload scores (range 0–100) for 1D horizontal tasks were 48.5 (SD = 17.7) and 46.6 (SD 10.3), respectively. The TLX can be used to inform the design of BCIs that will have greater usability by evaluating subjective workload between BCI tasks, participant groups, and control modalities. Practitioner Summary: Mental workload of brain–computer interfaces (BCI) can be evaluated with the NASA Task Load Index (TLX). The TLX is an effective tool for comparing subjective workload between BCI tasks, participant groups (able-bodied and disabled), and control modalities. The data can inform the design of BCIs that will have greater usability.

[1]  Alan F. Blackwell,et al.  Dasher: A Gesture-Driven Data Entry Interface for Mobile Computing , 2002, Hum. Comput. Interact..

[2]  Susan G. Hill,et al.  Traditional and raw task load index (TLX) correlations: Are paired comparisons necessary? In A , 1989 .

[3]  E. Curran,et al.  Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems , 2003, Brain and Cognition.

[4]  F Cincotti,et al.  Workload measurement in a communication application operated through a P300-based brain–computer interface , 2011, Journal of neural engineering.

[5]  H. Lüders,et al.  American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[6]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

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

[8]  A. K. Blangsted,et al.  The effect of mental stress on heart rate variability and blood pressure during computer work , 2004, European Journal of Applied Physiology.

[9]  Michael E. Smith,et al.  Monitoring Task Loading with Multivariate EEG Measures during Complex Forms of Human-Computer Interaction , 2001, Hum. Factors.

[10]  Neil D. Lawrence,et al.  Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning , 2004 .

[11]  S.A. Wills,et al.  DASHER-an efficient writing system for brain-computer interfaces? , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Chris Ball,et al.  Efficient Communication by Breathing , 2004, Deterministic and Statistical Methods in Machine Learning.

[13]  David J. Ward,et al.  Artificial intelligence: Fast hands-free writing by gaze direction , 2002, Nature.

[14]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

[15]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[16]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[17]  R G Radwin,et al.  Evaluation of a modified Fitts law brain–computer interface target acquisition task in able and motor disabled individuals , 2009, Journal of neural engineering.

[18]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[19]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[20]  H S Vitense,et al.  Multimodal feedback: an assessment of performance and mental workload , 2003, Ergonomics.

[21]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[22]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[23]  J. C. Byers,et al.  Comparison of Four Subjective Workload Rating Scales , 1992 .

[24]  P R Kennedy,et al.  Direct control of a computer from the human central nervous system. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[25]  Peter A. Hancock,et al.  ADAPTIVE CONTROL OF MENTAL WORKLOAD. , 2001 .

[26]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[27]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[28]  J. A. Wilson,et al.  Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. , 2007, Journal of neurosurgery.

[29]  R.G. Radwin,et al.  Neural Signal Based Control of the Dasher Writing System , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.