Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries

Abstract

Objective: To use the neural signals preceding movement and motor imagery to predict which of the four movements/motor imageries is about to occur, and to access this utility for brain-computer interface (BCI) applications.

Methods: Eight naïve 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-s 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 the 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.

Fig. 1

Trial stimuli. Stimuli presented during a single trial included a cross, an arrow (S1), and a diamond (S2). Subjects were asked to blink and swallow only during the cross presentation. S1 indicated the movement or imagery the subject should perform immediately upon S2 appearance.

Fig. 2

CNV visualization examples. CNV visualizations from C3 (channel 5) associated with subject 7 right hand movement (a) and motor imagery (b); and from CP2 (channel 19) associated with subject 5 tongue movement (c) and motor imagery (d). Time is measured relative to S2 appearance. Dashed lines represent the 95% confidence interval. All visualizations, except subject 7’s motor imagery visualization, reveals a CNV waveform, a negative shift in potential beginning about 1.5 seconds before S2 appearance.

Fig. 3

ERD/ERS visualization examples. ERD/ERS visualizations from C3 (channel 5) associated with subject 7 right hand movement (a) and motor imagery (b); and from CP2 (channel 19) associated with subject 5 tongue movement (c) and motor imagery (d). Colors depict the relative amplitudes, or power, of the frequencies in the signals’ power spectra. Time is measured relative to S2 appearance. For subject 7, the movement visualization, but not the motor imagery visualization, reveals an ERD, a sustained reduction of power, for frequencies around 10 Hz beginning about 1.5 seconds before S2 appearance. For subject 5, both visualizations reveal an ERS, a sustained increase of power, for frequencies around 12 Hz beginning about 1.0 seconds before S2 appearance.

Fig. 4

4-movement/imagery results. Average training and testing accuracies for 4-movement (a) and 4-imagery (b) classification. Error bars indicate standard deviation. Chance accuracy for 4-movement/imagery classification was 25%.

Fig. 5

2-movement/imagery results from subjects 1-6 (a) and subject 6 (b). Average training and testing accuracies for 2-movement/imagery classification. Error bars indicate standard deviation. Pairs of letters indicate the movements/imageries: R for right hand squeeze, L for left hand squeeze, T for tongue press on the roof of the mouth, and F for right foot toe curl. Chance accuracy for 2-movement/imagery classification was 50%.

Fig 6

ICA topography examples. ICA topographies for subject 6 2-movement/imagery classification (a) 4-movement/imagery classification (b). Colors indicate the ICA weights of the associated channels. Weights of large magnitude (-1 or 1) are associated with signal amplification. Weights of small magnitude (0) are associated with signal attenuation. Relative amplification of motor-related channels were detected in many of the plots and circled.

Fig. 7

Feature visualization examples. Feature visualizations for subject 6 movement (a) and motor imagery (b) classification. Colors indicate the Bhattacharyya distance of the associated feature. Scalp plots (a1 and b1) are for the 3.9 Hz frequency bin centered at 19.5 Hz. Bhattacharyya distance matrices (a2 and b2) are scaled to match the associated scalp plot.