Objective To use the neural signals preceding movement and motor imagery to predict which of four movements/motor imageries is about to occur, and to access this utility for brain-computer interface (BCI) applications. Methods Eight naive subjects performed or kinesthetically imagined four movements while electroencephalogram (EEG) was recorded from 29 channels over sensorimotor areas. The task was instructed with a specific stimulus (S1) and performed at a second stimulus (S2). A classifier was trained and tested offline at differentiating the EEG signals from movement/imagery preparation (the 1.5 seconds preceding movement/imagery execution). Results Accuracy of movement/imagery preparation classification varied between subjects. The system preferentially selected event related (de)synchronization (ERD/ERS) signals for classification, and high accuracies were associated with classifications that relied heavily on the ERD/ERS to discriminate movement/imagery planning. Conclusions The ERD/ERS preceding movement and motor imagery can be used to predict which of four movements/imageries is about to occur. Prediction accuracy depends on this signal’s accessibility. Significance The ERD/ERS is the most specific pre-movement/imagery signal to the movement/imagery about to be performed.

Background: Brain-computer interfaces (BCI) use electroencephalography (EEG) to interpret user intention and control an output device accordingly. We describe a novel BCI method to use a signal from five EEG channels (comprising one primary channel with four additional channels used to calculate its Laplacian derivation) to provide two-dimensional (2-D) control of a cursor on a computer screen, with simple threshold-based binary classification of band power readings taken over pre-defined time windows during subject hand movement.

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Patients that suffer from loss of motor control would benefit from a brain-computer interface (BCI) that would, optimally, be noninvasive, allow multiple dimensions of control, and be controlled with quick and simple means. Ideally, the control mechanism would be natural to the patient so that little training would be required; and the device would respond to these control signals in a predictable way and on a predictable time scale. It would also be important for such a device to be usable by patients capable and incapable of making physical movements. A BCI was created that used electroencephalography (EEG). Multiple dimensions of control were achieved through the movement or motor imagery of the right hand, left hand, tongue, and right foot. The movements were non-sustained to be convenient for the user. The BCI used the 1.5 seconds of the Bereitschaftspotential prior to movement or motor imagery for classification. This could allow the BCI to execute an action on a time scale anticipated by the user. To test this BCI, eight healthy participants were fitted with 29 EEG electrodes over their sensorimotor cortex and one bipolar electrooculography electrode to detect eye movement. Each participant completed six blocks of 100 trials. A trial included visual presentation of three stimuli: a cross, an arrow, and a diamond. Participants rested during the presentation of the cross. The arrow indicated the action that the participant should perform: right hand squeeze, left hand squeeze, press of the tongue against the roof of the mouth, or right foot toe curl. The diamond indicated that the participant should execute the movement during the first three blocks; and that the participant should imagine executing the movement during the last three blocks. Trials affected by motion artifacts, in particular face muscle activity, were removed. Of the remaining data, about 80% were used to train a Bayesian classification and about 20% were used to test this classification. Prediction of the four movements reached accuracies above 150% that of random classification for both real and imagined movements. This suggests a promising future for this BCI.