Predicting Reaching Targets from Human EEG

Paul S. Hammon, Scott Makeig, Howard Poizner, Emanuel Todorov, Virginia R. de Sa

Abstract: Extracting reach information from brain signals is of great interest to the fields of brain-computer interfaces (BCIs) and human motor control. To date, most work in this area has focused on invasive intracranial recordings; however, successful decoding of reach targets from noninvasive electroencephalogram (EEG) signals would be of great interest. In this article, we show that EEG signals contain sufficient information to decode target location during a reach (Experiment 1) and during the planning period before a reach (Experiment 2). We discuss the application of independent component analysis and dipole fitting for removing movement artifacts. With this technique we get similar classification accuracy for classifying EEG signals during a reach (Experiment 1) and during the planning period before a reach (Experiment 2). To the best of our knowledge, this is the first demonstration of decoding (planned) reach targets from EEG. These results lay the foundation for future EEG-based BCIs based on decoding of planned reaches.