Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities

BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase the amount of data available and hence obtain good generalization results. To this end, we introduce a spatial graph signal interpolation technique, that allows to interpolate efficiently multiple electrodes. We conduct a set of experiments with five BCI Motor Imagery datasets comparing the proposed interpolation with spherical splines interpolation. We believe that this work provides novel ideas on how to leverage graphs to interpolate electrodes and on how to homogenize multiple datasets.Comment: Submitted to the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

A Strong and Simple Deep Learning Baseline for BCI MI Decoding

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a very simple baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible

fluorescent g and c bands in mammalian chromosomes by using early brd u incorporation simultaneous to methotrexate treatment

Fluorescent G- and C-bands were obtained in human and cattle chromosomes of lymphocytes grown at 37.5D for 72–76 hours. 24 hours before the completion of the culture, 5-bromodeoxyuridine (BrdU) in a final concentration of 20 μg/ml and increasing doses of methotrexate (MTX) were added. After 17 hours the cells were washed and allowed to recover for 6 hours in a medium containing thymidine. Colcemid treatment lasted 1.5 hours. The air dried slides were stained with acridine orange and observed under fluorescence microscopy. Compared to the control (without MTX), it was possible by increasing the MTX doses to increase the number of cells in the first cycle of replication in the presence of BrdU (G-bands in both chromatids) with a concomitant reduction of the number of cells in the second cycle of replication (G-bands in one chromatid), which also allows demonstration of SCEs. The advantages of this technique and the different cellular responses between the two species are discussed

Changes in functional brain connectivity in the transition from wakefulness to sleep in different EEG bands

The reorganization of brain connectivity patterns due to changes in conscious state is poorly understood. The aim of this study is to assess methods for characterizing brain connectivity changes in different sleep stages as compared to wakefulness, and hence identify possible biomarkers of the level of consciousness based on the topography of connectivity networks. Polysomnographic recordings were collected during a sleep experiment from five healthy young subjects. Functional coupling between electroencephalographic (EEG) signals was estimated in different EEG bands with Partial Directed Coherence (PDC) and Directed Coherence (DC), which provide a frequency domain description of directed causal dependencies among time series. Results indicate that in the theta (?) and alpha (?) bands the number of significant connections increases in the transition from sleep to wakefulness. Moreover connectivity patterns elicited in sleep are dominated by short-range connections, in contrast to long range links connecting distant areas, which are elicited in wakefulness in the ? and ? bands. An inversion in the direction of information flow from anterior-posterior to posterior-anterior is noticeable in the transition from sleep to wakefulness in the ? and ? bands. The drop of the number of long-range posterior-frontal links in the ? band may be a promising indicator for the descent into sleep and perhaps anesthesia

L'imagerie cérébrale au service de la rééducation

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Few-Shot Decoding of Brain Activation Maps

International audienceFew-shot learning addresses problems for which a limited number of training examples are available. So far, the field has been mostly driven by applications in computer vision. Here, we are interested in adapting recently introduced few-shot methods to solve problems dealing with neuroimaging data, a promising application field. To this end, we create a neuroimaging benchmark dataset for few-shot learning and compare multiple learning paradigms, including meta-learning, as well as various backbone networks. Our experiments show that few-shot methods are able to efficiently decode brain signals using few examples, which paves the way for a number of applications in clinical and cognitive neuroscience, such as identifying biomarkers from brain scans or understanding the generalization of brain representations across a wide range of cognitive tasks

The use of haptic feedback in Brain-Computer Interfaces and Neurofeedback

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L'imagerie cérébrale au service de la rééducation

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