Visuo-motor Coordination Ability Predicts Performance With Brain-computer Interfaces Controlled By Modulation Of Sensorimotor Rhythms (SMR). - Info and Reading Options

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This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28Frontiers%20in%20Human%20Neuroscience%29" rel="nofollow">Frontiers in Human Neuroscience</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28Frontiers%20in%20Human%20Neuroscience%29%20AND%20volume%3A%288%29" rel="nofollow">volume 8</a>.<h2>Abstract</h2>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-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.

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