Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training

Abstract

Technological progress in computer science and neuroimaging has resulted in many approaches that aim to detect brain states and translate them to an external output. Studies from the field of brain-computer interfaces (BCI) and neurofeedback (NF) have validated the coupling between brain signals and computer devices; however a cognitive model of the processes involved remains elusive. Psychological parameters usually play a moderate role in predicting the performance of BCI and NF users. The concept of a locus of control, i.e., whether one's own action is determined by internal or external causes, may help to unravel inter-individual performance capacities. Here, we present data from 20 healthy participants who performed a feedback task based on EEG recordings of the sensorimotor rhythm (SMR). One group of 10 participants underwent 10 training sessions where the amplitude of the SMR was coupled to a vertical feedback bar. The other group of ten participants participated in the same task but relied on sham feedback. Our analysis revealed that a locus of control score focusing on control beliefs with regard to technology negatively correlated with the power of SMR. These preliminary results suggest that participants whose confidence in control over technical devices is high might consume additional cognitive resources. This higher effort in turn may interfere with brain states of relaxation as reflected in the SMR. As a consequence, one way to improve control over brain signals in NF paradigms may be to explicitly instruct users not to force mastery but instead to aim at a state of effortless relaxation.

Keywords: EEG; locus of control; neurofeedback; performance prediction; sensorimotor rhythm.

Figure 1

Changes of SMR power during training. (A) Mean absolute SMR power (12–15 Hz) across sessions during six runs of neurofeedback training for the experimental group using real feedback (EG, n = 10 participants) and the control group using sham feedback (CG, n = 10 participants). Dotted line indicates a significant slope of 0.023 µV² per run. (B,C) Comparison of subgroups (n = 5 participants) obtained by median-split according to the individual control beliefs of low and high KUT scores. Dotted line indicates a significant slope of 0.035 µV² per run. Note that all error bars represent the standard error of the mean (SEM).

Figure 2

Changes of SMR power during baseline. (A) Mean absolute SMR power across 10 training sessions during the baseline condition. Participants of the EG were watching a visual feedback of their own brain activations without trying to gain control, while participants of the CG were watching a pre-recorded video (B,C) comparison according to the individual control beliefs (same conventions as in Figure 1).

Figure 3

SMR power correlates with control belief. (A) Scatter plot of individual KUT scores against overall SMR power during feedback training (total n = 60 runs per participant). (B) Same as in (A) for baseline runs (total n = 10 runs per participant). For details of the relationships please see subsection “Overall Correlation of KUT and SMR Power” in Results.