Characteristics of Kinematic Parameters in Decoding Intended Reaching Movements Using Electroencephalography (EEG)

Abstract

The utility of premovement electroencephalography (EEG) for decoding movement intention during a reaching task has been demonstrated. However, the kind of information the brain represents regarding the intended target during movement preparation remains unknown. In the present study, we investigated which movement parameters (i.e., direction, distance, and positions for reaching) can be decoded in premovement EEG decoding. Eight participants performed 30 types of reaching movements that consisted of 1 of 24 movement directions, 7 movement distances, 5 horizontal target positions, and 5 vertical target positions. Event-related spectral perturbations were extracted using independent components, some of which were selected via an analysis of variance for further binary classification analysis using a support vector machine. When each parameter was used for class labeling, all possible binary classifications were performed. Classification accuracies for direction and distance were significantly higher than chance level, although no significant differences were observed for position. For the classification in which each movement was considered as a different class, the parameters comprising two vectors representing each movement were analyzed. In this case, classification accuracies were high when differences in distance were high, the sum of distances was high, angular differences were large, and differences in the target positions were high. The findings further revealed that direction and distance may provide the largest contributions to movement. In addition, regardless of the parameter, useful features for classification are easily found over the parietal and occipital areas.

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

“…Next, we cannot completely rule out the influence of direction on the decoding before grasping phase, since the position of five devices in our experiment setup was fixed. The study of Kim et al showed that direction and distance had the most contribution to the movement, while there was no significant difference observed for position (Kim et al, 2019 ). In our experiment, five devices were arranged in a fan shape to ensure equal distance.…”

Investigation of Delayed Response during Real-Time Cursor Control Using Electroencephalography

“…is can be used to construct intelligent BMI systems to update a decoder online when a BMI system experiences an error. So far, feedforward control has been employed in a BMI system that decodes neural signals to use them as an input command and obtain movement parameters [44]. When a BMI system works well and a user wants to move, feedforward control is sufficient; however, when the system experiences an error because it does not perceive correct user intention, a feedback controller is needed to perform calculations and correct for the delay.…”