What we think before a voluntary movement

Abstract

A central feature of voluntary movement is the sense of volition, but when this sense arises in the course of movement formulation and execution is not clear. Many studies have explored how the brain might be actively preparing movement before the sense of volition; however, because the timing of the sense of volition has depended on subjective and retrospective judgments, these findings are still regarded with a degree of scepticism. EEG events such as beta event-related desynchronization and movement-related cortical potentials are associated with the brain's programming of movement. Using an optimized EEG signal derived from multiple variables, we were able to make real-time predictions of movements in advance of their occurrence with a low false-positive rate. We asked participants what they were thinking at the time of prediction: Sometimes they were thinking about movement, and other times they were not. Our results indicate that the brain can be preparing to make voluntary movements while participants are thinking about something else.

Figure 1. Calibration session model for subject # 5

a, A comparison between the 1.5 seconds preceding a movement and non-movement, taken during the first rotation of the clock, over frequency and the 29 EEG channels. The separability of each frequency-channel feature, say channel 6 (C3) in bin 4 (20-24Hz) is used to discriminate the above two datasets, with the error indexed by the distance to the upper-left corner of perfect discrimination in the Relative Operating Characteristic (ROC) curve (Panel B). A smaller distance represents better discrimination, indicated by the dark blue color. The ROC curve shows the ratio of true positives (TP) to false positives (FP) based on a single best feature determined from the calibration session, which happens to be found in the low β band (frequency bin 4, in Panel A) of electrode C3 (channel index 6, in Panel A). However, the final model would be constructed from the 4 best features and a threshold would subsequently be researcher-chosen ensuring <10% of all trials being false positives at the time of model optimization for online prediction.

Figure 2. Validation of the model for subject # 3

The model is developed from a calibration data set and is then compared, off-line, to a different calibration data set from the same subject in order to validate its predictive robustness. a, Plot of True Positive-to-False Positive ratios (TP/FP) of the Relative Operating Characteristic (ROC) curve for the model formulated on a calibration data set under 200 discrimination threshold values. b, Histogram of the time from onset of the trial to the time of EMG onset in the validating data set. c, Display of the TP/FP in the ROC curve when using the model to perform an off-line analysis in order to validate the model. The working point was set here to minimize FP (to a level below 10% of total trials) and maximize TP (to a level above 45% of total trials); while unpredicted movements (false negatives - FN) would comprise the remainder of trials. d, Histogram of time after an off-line prediction that EMG onset was actually detected. Onset of EMG less than or equal to 1.5 seconds after a prediction was defined as a true positive according to our model.

Figure 3. Distribution of predictions for subjects (n=13)

Instances with intention (“w/”) comprise 55% of total predictions (43% with EMG-confirmed movement; 12% with no movement detected on EMG). The predictions without intention (“w/o”) comprise 45% of total predictions (13% with movement detected on EMG; 32% without detectable EMG activity). The without intention (“w/o”) group is of most interest in our descriptive analysis, which is 32%, and includes 14% of true predictions after taking into account the false positives of 18%.

Figure 4. Frequency histogram of prediction responses by percentage of total predictions

After categorization of subject responses, the distribution of response types subdivided according to prediction type revealed interesting thought patterns in the subjects. Responses of types not related to the movements or moving predominated in the predictions without intention (regardless of the presence of movement – red), whereas thoughts about moving and the movement expectedly predominated in the predictions with intention. The percentage of predictions with recorded responses within each prediction type category is indicated in parentheses.

Figure 5. Schematic of the timeline of movement and intention

A theoretical representation characterizing our predictions (modified from Matsuhashi and Hallett, with permission). P is the point of no return, W is the time after which the sense of volition is present, between time T and time W, there can be a sense of volition if the person is asked about volition, and prior to time T (but after the onset of BP1) the brain is preparing the movement, but the sense of volition will not be present even if the person is probed. The 55% of predictions with movement are either just not vetoed, or are occurring after P (the point of no return) at which point they cannot be vetoed. The portion of these movements without intention (13%) may well reflect predictions prior to the time T. When there is a prediction without intention, however, it is possible for this to be between T and W since we are not probing for the sense of intention, at least at the first instance.