Decoding Coordinated Directions of Bimanual Movements From EEG Signals

Abstract

Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35%. The binary classification accuracies achieved 80.24 ± 6.25, 82.62 ± 7.82, and 86.28 ± 5.50% for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50%, 75.67 ± 7.18%, and 77.79 ± 5.65%, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.

ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury

“…[23] Ofner et al [22] conducted a study where they observed that the averaged signal of the central electrode Cz exhibited the characteristic MRCP pattern during movement attempts. Moreover, they identified discriminative information, such as positive and negative peaks, within MRCPs that could be indicative of the movement class, a finding also supported by the research conducted by Zhang et al [24] This observation suggests that MRCP patterns contain valuable information that can be used to preprocess signals before feeding them into the classifier model, ultimately enhancing its performance. Therefore, in our proposed methodology, we partitioned the entire dataset into a training set, consisting of 80% of the data, and a test set, comprising the remaining 20% of the data.…”

“…The extraction of MRCPs was conducted to investigate their discriminative patterns, as highlighted by Ofner et al [ 22 ] However, accurately estimating these patterns needs a substantial dataset. On the other hand, Zhang et al [ 24 ] performed a statistical analysis, revealing significant differences in negative peak amplitude across certain motions, while no such distinction was observed for positive peak amplitude. Consequently, an additional processing step is imperative to enhance the performance of the classification model.…”

MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements

“…There are also several studies exploring bimanual movement decoding. In [22], 7-class reach-and-grasp unimanual and bimanual movements' decoding accuracy peaked at 38.6% with the time window of [0.1, 1.1] s. In [17], 6-class unimanual and bimanual movement directions' decoding accuracy peaked at 69.02% with the time window of [0.3, 1.3] s. In [23], 3-class bimanual coordinated directions decoding accuracy peaked at 70.1% using a window of [0, 1] s. Though using different datasets, our proposed method showed superior performance in the unimanual and bimanual movement decoding. To further validate the effectiveness of our proposed method, we also compared with several state-of-art models on our dataset.…”

“…Several studies [17] [22] [23] have strode the first step toward decoding the unimanual and bimanual movements. In [22], Schwarz et al discriminated 7-class reach-and-grasp movements using the EEG time-domain features, and averaged classification accuracies reached 38.6% for a combination of six movements and one rest condition.…”

“…In [17], Wang et al decoded single-hand and both-hand movement directions using the center-our paradigm and the temporal features of EEG signals and support vector machine classifier, and the six-class classification accuracy peaked at 69.02% using the time window of [0.3, 1.3] s corresponding to movement onset. In [23], Zhang et al decoded bimanual coordinated movements by using temporal features and a combined deep learning model of convolution neural network and bidirectional long short-term memory network. The classification accuracy of three bimanual coordinated directions peaked at 70.1% using a window of [0, 1] s corresponding to movement onset.…”