Using Humanoid Robots to Obtain High-Quality Motor Imagery Electroencephalogram Data for Better Brain–Computer Interaction

Abstract

The electroencephalogram (EEG) signal from motor imagery (MI) is used to drive brain–computer interaction (BCI). However, users usually are not adept at performing MI, which leads to low-quality EEG signals and decreases the performance of BCI applications. The humanoid robot stimulation approach can guide users in performing MI more proficiently by increasing the cortico-spinal excitability and improving the discrimination of event-related desynchronization patterns during MI tasks. Compared to the traditional stimulation modes, our proposed humanoid robot stimulation mode can activate higher quality MI EEG signals. We use convolutional neural network and long short-term memory algorithm for the extraction of EEG features and classification. The results showed that the CNN-LSTM can achieve the highest classification accuracy (93.7% ±1.7%) in the humanoid robot stimulation mode, and it outperformed all other classifier-stimulation mode combinations. This demonstrates the effectiveness and feasibility of using a humanoid robot in real-scene MI-BCI applications, such as service robots or rehabilitation system for person with motor disabilities.