Decoding Continual Muscle Movements Related to Complex Hand Grasping from EEG Signals
This addresses the challenge of interpreting user intentions in brain-computer interfaces for applications like prosthetics, though it is incremental with specific gains.
The study tackled the problem of decoding continual muscle movements for complex hand grasping from EEG signals by proposing a MAP-CNN method, achieving average classification accuracies of 63.6% in motor execution and 45.8% in motor imagery across subjects.
Brain-computer interface (BCI) is a practical pathway to interpret users' intentions by decoding motor execution (ME) or motor imagery (MI) from electroencephalogram (EEG) signals. However, developing a BCI system driven by ME or MI is challenging, particularly in the case of containing continual and compound muscles movements. This study analyzes three grasping actions from EEG under both ME and MI paradigms. We also investigate the classification performance in offline and pseudo-online experiments. We propose a novel approach that uses muscle activity pattern (MAP) images for the convolutional neural network (CNN) to improve classification accuracy. We record the EEG and electromyogram (EMG) signals simultaneously and create the MAP images by decoding both signals to estimate specific hand grasping. As a result, we obtained an average classification accuracy of 63.6($\pm$6.7)% in ME and 45.8($\pm$4.4)% in MI across all fifteen subjects for four classes. Also, we performed pseudo-online experiments and obtained classification accuracies of 60.5($\pm$8.4)% in ME and 42.7($\pm$6.8)% in MI. The proposed method MAP-CNN, shows stable classification performance, even in the pseudo-online experiment. We expect that MAP-CNN could be used in various BCI applications in the future.