  Motor brain-computer interfaces (BCIs) are a promising technology that may
enable motor-impaired people to interact with their environment. Designing
real-time and accurate BCI is crucial to make such devices useful, safe, and
easy to use by patients in a real-life environment. Electrocorticography
(ECoG)-based BCIs emerge as a good compromise between invasiveness of the
recording device and good spatial and temporal resolution of the recorded
signal. However, most ECoG signal decoders used to predict continuous hand
movements are linear models. These models have a limited representational
capacity and may fail to capture the relationship between ECoG signal and
continuous hand movements. Deep learning (DL) models, which are
state-of-the-art in many problems, could be a solution to better capture this
relationship. In this study, we tested several DL-based architectures to
predict imagined 3D continuous hand translation using time-frequency features
extracted from ECoG signals. The dataset used in the analysis is a part of a
long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was
acquired during a closed-loop experiment with a tetraplegic subject. The
proposed architectures include multilayer perceptron (MLP), convolutional
neural networks (CNN), and long short-term memory networks (LSTM). The accuracy
of the DL-based and multilinear models was compared offline using cosine
similarity. Our results show that CNN-based architectures outperform the
current state-of-the-art multilinear model. The best architecture exploited the
spatial correlation between neighboring electrodes with CNN and benefited from
the sequential character of the desired hand trajectory by using LSTMs.
Overall, DL increased the average cosine similarity, compared to the
multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249
for the left and right hand, respectively.
