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

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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

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Neurofeedback (NF) allows to exert self-regulation over specific aspects of one's own brain activity by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, most commonly electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). EEG-fMRI-neurofeedback (EEG-fMRI-NF) is a new approach that consists in providing a NF based simultaneously on EEG and fMRI signals. By exploiting the complementarity of these two modalities, EEG-fMRI-NF opens a new spectrum of possibilities for defining bimodal NF targets that could be more robust, flexible and effective than unimodal ones. Since EEG-fMRI-NF allows for a richer amount of information to be fed back, the question arises of how to represent the EEG and fMRI features simultaneously in order to allow the subject to achieve better self-regulation. In this work, we propose to represent EEG and fMRI features in a single bimodal feedback (integrated feedback). We introduce two integrated feedback strategies for EEG-fMRI-NF and compare their early effects on a motor imagery task with a between-group design. The BiDim group (n=10) was shown a two-dimensional (2D) feedback in which each dimension depicted the information from one modality. The UniDim group (n=10) was shown a one-dimensional (1D) feedback that integrated both types of information even further by merging them into one. Online fMRI activations were significantly higher in the UniDim group than in the BiDim group, which suggests that the 1D feedback is easier to control than the 2D feedback. However subjects from the BiDim group produced more specific BOLD activations with a notably stronger activation in the right superior parietal lobe (BiDim > UniDim, p < 0.001, uncorrected). These results suggest that the 2D feedback encourages subjects to explore their strategies to recruit more specific brain patterns. To summarize, our study shows that 1D and 2D integrated feedbacks are effective but also appear to be complementary and could therefore be used in a bimodal NF training program. Altogether, our study paves the way to novel integrated feedback strategies for the development of flexible and effective bimodal NF paradigms that fully exploits bimodal information and are adapted to clinical applications

EEG connectivity measures and their application to assess the depth of anaesthesia and sleep

General anaesthesia has been used for more than two centuries to guarantee unconsciousness, analgesia and immobility during surgery, yet our ability to evaluate the level of anaesthesia of the patient remains insufficient. This contributes on one hand to occasional episodes of intraoperative awareness and recall and on the other to ‘controlled’ drug over-dosage that increases hospital costs and patients recovery times. At present parameters used in clinical practice to monitor anaesthesia are indirect measures of the state of the brain, which is the target organ of anaesthetics. The lack of a reliable monitor of anaesthetic depth has led to considerable effort to develop new monitoring methods based on electrophysiological measurements. This progress has produced a series of depth of anaesthesia monitors based on various features of the electroencephalogram (EEG) signal. Even though these indexes are practically useful, their theoretical and physiological validity is poorly evidenced and they suffer from some practical limitations. As a result, their clinical uptake has been quite low. In recent years increasing attention has been given to brain connectivity as a powerful tool to investigate the complex behaviour of the brain. Theoretical and experimental findings have identified the disruption of brain connectivity as a crucial mechanism of anaesthetic-induced loss of consciousness. In this work a novel index of anaesthetic depth based on brain connectivity estimated from non-invasive scalp recordings (EEG) is proposed. Firstly, robust estimators of directed connectivity were identified in the framework of multivariate autoregressive (MVAR) models. With a series of simulation studies the performances of these methods in estimating causal connections were assessed in particular with respect to the deleterious effects of instantaneous connectivity due to volume conduction. Recently published solutions were also tested (and rejected). From a comparison of connectivity measurements in simulations, MVAR based estimators were most robust to the effects of volume conduction than conventional coherence measurements. Next the performances of directed connectivity estimators were tested in two experimental studies on NREM sleep and on anaesthesia. Features that exhibited the most robust changes with the individual level of consciousness were identified and their performances in discriminating wakefulness from anaesthesia tested on ten patients undergoing a slow induction of propofol anaesthesia. The performance of the proposed method were also compared with established depth of anaesthesia indexes such as Bispectral Index (BIS) or Auditory Evoked Potentials (AEP). Results suggest that EEG connectivity features are sensitive to the anaesthetic induced changes and that they have the potential to be integrated in future monitors of intra-operative awareness and anaesthetic adequacy

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

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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