Prediction of specific hand movements using electroencephalographic signals

Abstract

Objective: To identify specific hand movements from electroencephalographic activity. Design: Proof of concept study. Setting: Rehabilitation hospital in Toronto, Canada. Participants: Fifteen healthy individuals with no neurological conditions. Intervention: Each individual performed six different hand movements, including four grasps commonly targeted during rehabilitation. All of them used their dominant hand and four of them repeated the experiment with their non-dominant hand. EEG was acquired from 8 different locations (C1, C2, C3, C4, CZ, F3, F4 and Fz). Time-frequency distributions (spectrogram) of the pre-movement EEG activity for each electrode were generated and each of the time-resolved spectral components (1 Hz to 50 Hz) was correlated with a hyperbolic tangent function to detect power decreases. The spectral components and time ranges with the largest correlation values were identified using a threshold. The resulting features were then used to implement a distance-based classifier. Outcome measures: Accuracy of classification. Results: A minimum of three different dominant hand movements were classified correctly with average accuracies between 65–75% across all 15 participants. Average accuracies between 67–85% for the same three movements were achieved across four of the 15 participants who were tested with their non-dominant hand. Conclusion: The results suggest that it may be possible to predict specific hand movements from a small number of electroencephalographic electrodes. Further studies including members of the spinal cord injury community are necessary to verify the suitability of the proposed process.