Abstract
Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI.
Highlights
Brain-machine interfaces (BMIs) are designed to decode neural commands from the brain and use them as input commands for external devices (Wolpaw et al, 2002; Höhne et al, 2014)
Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine
We confirmed that EEG signals occurring during movement preparation can be used to control a BMI
Summary
Brain-machine interfaces (BMIs) are designed to decode neural commands from the brain and use them as input commands for external devices (Wolpaw et al, 2002; Höhne et al, 2014). Understanding brain activity associated with human intention leading to a movement task could further advance the effectiveness of BMIs as assistive devices. There are many different methods for measuring brain activity, such as magnetoencephalography (MEG), electroencephalography (EEG), electrocorticography (ECoG), functional near-infrared spectroscopy Many researchers have tried to decode brain activity to understand human motor intention from EEG signals. Classification using discrete information has been performed for individual finger movements using a support vector machine (Liao et al, 2014), analytic movement tasks with the dominant upper limb (Ibáñez et al, 2015), as well as motor imagery for cursor control (Huang et al, 2009). Combining EEG and fNIRS signals has been performed for early detection (Khan et al, 2018)
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