Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR)
Please always quote using this URN: urn:nbn:de:bvb:20-opus-113084
- Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80–100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCIModulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80–100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person’s visuo-motor control ability; and (2) subject’s “attentional impulsivity”. In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors.…
Author: | Andrea Kübler, Benjamin Blankertz, Sonja C. Kleih, Tobias Kaufmann, Eva M. Hammer |
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URN: | urn:nbn:de:bvb:20-opus-113084 |
Document Type: | Journal article |
Faculties: | Fakultät für Humanwissenschaften (Philos., Psycho., Erziehungs- u. Gesell.-Wissensch.) / Institut für Psychologie |
Language: | English |
Year of Completion: | 2014 |
Source: | Frontiers in Human Neuroscience 8:574. doi: 10.3389/fnhum.2014.00574 |
DOI: | https://doi.org/10.3389/fnhum.2014.00574 |
Dewey Decimal Classification: | 1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie |
Tag: | attentional impulsivity; brain-computer interfaces; predictors; sensorimotor rhythms; visuo-motor coordination abilities |
Release Date: | 2015/05/19 |
EU-Project number / Contract (GA) number: | 224631 |
OpenAIRE: | OpenAIRE |
Collections: | Open-Access-Publikationsfonds / Förderzeitraum 2014 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung |