Psychological Factors Influencing Brain-Computer Interface (BCI) Performance

Brain activity can be influenced by a person's mental and emotional condition. Therefore, the psychological state should be taken into account when investigating applications which are controlled by or dependent on brain-activity, such as Brain-Computer Interfaces (BCI). In their Model of BCI control, Kübler and colleagues [1], summarized the components which most likely influence and predict BCI performance and presented psychological factors as one such component. Psychological factors that may influence BCI performance are attention, concentration, motivation, or visuo-motor coordination. We present the state of the art with respect to the influence of psychological factors on BCI performance and discuss the importance of the BCI training situation. We argue for considering psychological aspects in BCI research, development, and translation to end-users.

[1]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[2]  Eric W. Sellers,et al.  The effect of task based motivation on BCI performance: A preliminary outlook. , 2013 .

[3]  Tobias Kaufmann,et al.  Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: a case study. , 2015, Archives of physical medicine and rehabilitation.

[4]  E. John,et al.  Evoked-Potential Correlates of Stimulus Uncertainty , 1965, Science.

[5]  K. Müller,et al.  Psychological predictors of SMR-BCI performance , 2012, Biological Psychology.

[6]  N. Birbaumer,et al.  The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study , 2010, Front. Neuropharma..

[7]  A. Kübler,et al.  Motivation modulates the P300 amplitude during brain–computer interface use , 2010, Clinical Neurophysiology.

[8]  Andrea Kübler,et al.  Empathy, motivation, and P300 BCI performance , 2013, Front. Hum. Neurosci..

[9]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  M. Molinari,et al.  Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: design, acceptability, and usability. , 2015, Archives of physical medicine and rehabilitation.

[11]  M. Weinand,et al.  Heart Rate and Heart Rate Variability Changes in the Intracarotid Sodium Amobarbital Test , 2001, Epilepsia.

[12]  R. Rupp Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury , 2014, Front. Neuroeng..

[13]  A. Kübler,et al.  Training locked-in patients: a challenge for the use of brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Christa Neuper,et al.  Electroencephalographic characteristics during motor imagery , 2010 .

[15]  R. Lane,et al.  A model of neurovisceral integration in emotion regulation and dysregulation. , 2000, Journal of affective disorders.

[16]  T. Vaughan,et al.  Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users. , 2015, Archives of physical medicine and rehabilitation.

[17]  Christian Kothe,et al.  Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general , 2011, Journal of neural engineering.

[18]  Benjamin Blankertz,et al.  Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR) , 2014, Front. Hum. Neurosci..

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

[20]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

[21]  Andrea Kübler,et al.  Psychological Perspectives: Quality of Life and Motivation , 2014 .

[22]  F. Cincotti,et al.  Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis , 2013, Front. Hum. Neurosci..

[23]  Wolfgang Rosenstiel,et al.  Neural mechanisms of brain–computer interface control , 2011, NeuroImage.

[24]  Christa Neuper,et al.  Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training , 2013, Front. Hum. Neurosci..

[25]  Gernot R. Müller-Putz,et al.  Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain–computer interface , 2014, Biological Psychology.

[26]  Ryan Williams,et al.  Prevalence of depression after spinal cord injury: a meta-analysis. , 2015, Archives of physical medicine and rehabilitation.

[27]  Elisabeth Hildt,et al.  Brain-Computer-Interfaces in their ethical, social and cultural contexts , 2014 .

[28]  H. Heckhausen Achievement motivation and its constructs: A cognitive model , 1977 .

[29]  Anatole Lécuyer,et al.  Author manuscript, published in "IEEE Transactions on Computational Intelligence and AI in games (2013)" Two Brains, One Game: Design and Evaluation of a Multi-User BCI Video Game Based on Motor Imagery , 2022 .

[30]  J. Rotter,et al.  The Clinical Measurement of Personality. , 1954 .

[31]  Tobias Kaufmann,et al.  Effects of resting heart rate variability on performance in the P300 brain-computer interface. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.