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Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach

  • Bioengineering


Our goal is to develop an algorithm for feature extraction and classification to be used in building brain-computer interfaces. In this paper, we present preliminary results for classifying EEG data of imaginary wrist movements. We have developed an algorithm based on the spatio-temporal features of the recorded EEG signals. We discuss the differences between the feature vectors selected for both actual and imaginary wrist movements and compare classification results.

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