Predicting movement intent in real-time: From brain to subjective experience

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The readiness potential (RP) and the event-related desynchronization (ERD) are neural signals that build up over the motor cortex 1.5-2 seconds prior to movement onset. Bai et al. (2011) were amongst the first to reliably detect movement intent online based on these signals. Interestingly, these brain signals typically build up prior to the moment a person reports to consciously intend to act. However, how these subjective reports relate to these neural preparatory signals remains unclear. To investigate this, we developed a brain-computer interface (BCI), based on the Bai study, that predicts movement intent based on these brain signals and then feeds this prediction back by means of functional electrical stimulation (FES). Three experiments were conducted. In the first experiment we successfully replicated the Bai study offline. We found we could predict movement intent offline based on the ERD (-0.7±0.17s) and the RP (-0.43±0.84s) before movement onset. In the second experiment we investigated the effect of FES stimulation on EEG data. We showed FES stimulation mostly influences the EEG data on and after movement onset and was thus not an issue for our study. In our third experiment we used online classification to investigate if a person is aware of their intention to act when movement preparation is detected in the brain. The online classification did not work as expected due to a high false positive rate. Therefore, we could not answer the main question in this experiment. We believe the online classification was affected by an anticipation buildup over time. By using more time points for classifier training, building in trials that provide a measure of anticipation alone and creating more variance in action timing, we believe it will be possible to predict movement intent in real-time and investigate how the subjective experience of intending to act relate to the RP/ERD.
Faculteit der Sociale Wetenschappen