Brain-Computer Interfaces (BCIs) are direct connections between the brain and a computer. They translate brain signals into operational commands for technical devices. With BCIs severely impaired people can communicate (Kübler et al., 2001; Nijboer et al., 2008) or control arm movement (Pfurtscheller et al., 2003). Different electroencephalogram (EEG) signals have been applied to control a BCI, e.g. slow cortical potentials, sensorimotor rhythms, event-related potentials in healthy and severely motor impaired individuals (Hinterberger et al., 2004; Kübler et al., 2005; Sellers et al., 2006). But also after about 30 years of research on BCIs and multiple improvement e.g., in signal acquisition and classification, there is little knowledge about the phenomenon, that some people – healthy as well as individuals with disease – are not able to learn BCI-control. We refer to this phenomenon as “BCI-illiteracy”. The number of users from whom we cannot record a classifiable brain signal varies between 10% to 30% depending on the input signal and health status of participants.
There is little research about the determinants and predictors of learning how to control a BCI within a neurofeedback paradigm. Daum and colleagues (1993) showed that memory span and attention were correlated to the regulation of slow cortical potentials (SCP) in a sample of epileptic patients. Holzapfel (1998) replicated these results partially; memory span, personality factors and “dealing with stressful situations” predicted best the BCI performance.
Neumann and Birbaumer (2003) showed that the initial performance could predict future performance in a BCI based on SCP in a sample of five severely paralysed patients. Burde and Blankertz (2006) reported a significant correlation of r = 0.59 between the SMR-BCI feedback performance and the variable “locus of control by dealing with technology”. Subjects who felt confident with controlling a technical device performed better in the SMR-BCI.
To elucidate the “BCI-illiteracy” phenomenon, we want to investigate whether physiological and psychological parameters, such as heart rate variability (HRV), imagery abilities, visual-motor coordination abilities, attention, intelligence, verbal and non-verbal learning abilities, personality traits, psychological well-being, motivation and mood, could predict BCI performance in a BCI controlled by modulation of sensorimotor rhythms (SMR) with motor imagery.
This work is supported by the DFG KU 1453/3-1.
- Nijboer F, Birbaumer N and Kübler A (2010) The influence of psychological state and motivation on brain–computer interface performance in patients with amyotrophic lateral sclerosis – a longitudinal study. Front. Neurosci. 4:55. doi: 10.3389/fnins.2010.00055
- Millán JdR, Rupp R, Müller-Putz GR, Murray-Smith R, Giugliemma C, Tangermann M, Vidaurre C, Cincotti F, Kübler A, Leeb R, Neuper C, Müller K-R and Mattia D (2010) Combining brain–computer interfaces and assistive technologies: state-of-the-art and challenges. Front. Neurosci. 4:161. doi:10.3389/fnins.2010.00161
- Burde, W., Blankertz, B., 2006. Is the locus of reinforcement a predictor of brain-computer interface performance? in Proceedings of the 3rd International Brain-Computer Interface Workshop and Training Course 2006. Verlag der Technischen Universität Graz, 76-77.
- Daum, I., Rockstroh, B., Birbaumer, N., Elbert, T., Lutzenberger, W., 1993. Behavioural treatment of slow cortical potentials in intractable epilepsy: neuropsychological predictors of outcome. J Neurol Neurosurg Psychiatry, 94-97.
- Hinterberger, T., Schmidt, S., Neumann, N., Mellinger, J., Blankertz, B., Curio, G., Birbaumer, N., 2004. Brain-computer communication and slow cortical potentials. IEEE Trans Biomed Eng 51, 1011-1018.
- Holzapfel, S., 1998. Prädiktoren der Selbstregulation der langsamen Hirnpotentiale bei Epilepsie. Eberhard-Karls-Universität Tübingen.
- Kübler, A., Neumann, N., Kaiser, J., Kotchoubey, B., Hinterberger, T., Birbaumer, N.P., 2001. Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. Arch Phys Med Rehabil 82, 1533-1539.
- Kübler, A., Nijboer, F., Mellinger, J., Vaughan, T. M., Pawelzik, H., Schalk, G., McFarland, D.J., Birbaumer, N., Wolpaw, J.R., 2005. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64, 1775-1777.
- Neumann, N., Birbaumer, N., 2003. Predictors of successful self control during brain-computer communication. J Neurol Neurosurg Psychiatry 74, 1117-1121.
- Nijboer, F., Sellers, E. W., Mellinger, J., Jordan, M.A., Matuz, T., Furdea, A., Halder, S., Mochty, U., Krusienski, D.J., Vaughan, T.M., Wolpaw, J.R., Birbaumer, N., Kübler, A., 2008. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 119, 1909-1916.
- Pfurtscheller, G., Muller, G. R., Pfurtscheller, J., Gerner, H. J., Rupp, R., 2003. 'Thought'-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience letters 351, 33-36.
- Sellers, E. W., Kübler, A., Donchin, E., 2006. Brain-computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: the P300 Speller. IEEE Trans Neural Syst Rehabil Eng 14, 221-224.