Matthieu Martin

Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac et al.

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.

Matthieu Martin, Bruno Sciolla, Michaël Sdika et al.

Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop into life-threatening hydrocephalus and is correlated with future neuro-developmental impairments. Consequently, it must be detected and monitored by physicians. In clinical routing, manual 2D measurements are performed on 2D ultrasound (US) images to estimate the CVS volume but this practice is imprecise due to the unavailability of 3D information. A way to tackle this problem would be to develop automatic CVS segmentation algorithms for 3D US data. In this paper, we investigate the potential of 2D and 3D Convolutional Neural Networks (CNN) to solve this complex task and propose to use Compositional Pattern Producing Network (CPPN) to enable the CNNs to learn CVS location. Our database was composed of 25 3D US volumes collected on 21 preterm nenonates at the age of $35.8 \pm 1.6$ gestational weeks. We found that the CPPN enables to encode CVS location, which increases the accuracy of the CNNs when they have few layers. Accuracy of the 2D and 3D CNNs reached intraobserver variability (IOV) in the case of dilated ventricles with Dice of $0.893 \pm 0.008$ and $0.886 \pm 0.004$ respectively (IOV = $0.898 \pm 0.008$) and with volume errors of $0.45 \pm 0.42$ cm$^3$ and $0.36 \pm 0.24$ cm$^3$ respectively (IOV = $0.41 \pm 0.05$ cm$^3$). 3D CNNs were more accurate than 2D CNNs in the case of normal ventricles with Dice of $0.797 \pm 0.041$ against $0.776 \pm 0.038$ (IOV = $0.816 \pm 0.009$) and volume errors of $0.35 \pm 0.29$ cm$^3$ against $0.35 \pm 0.24$ cm$^3$ (IOV = $0.2 \pm 0.11$ cm$^3$). The best segmentation time of volumes of size $320 \times 320 \times 320$ was obtained by a 2D CNN in $3.5 \pm 0.2$ s.