Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent neurodevelopmental disorder among children. It affects patients’ lives in many ways: inattention, difficulty with stimuli inhibition or motor function regulation. Different treatments exist today, but these can present side effects or are not effective for all subgroups. Neurofeedback (NF) is an innovative treatment consisting of brain activity display. NF training could consist of a virtual reality (VR) video-game in which the participant’s attention affects the game. Attention being assessed through physiological signals, one of the main steps is to design an estimator for the attention state. We present a novel framework able to record physiological signals in specific attention states and able to estimate the corresponding attention state. We propose a database composed of electroencephalography signals (EEG), and an eye-tracker labelled with a score representing the attention span for 32 healthy participants. Different features are extracted from the signals and machine learning (ML) algorithms are proposed. Our approach exhibits high accuracy for attention estimation, which corroborates a correlation between attention state and physiological signals (i.e. EEG, eye-tracking signals). The dataset has been made publicly available to promote research in the domain and we encourage other scientists to use their own approach for attention estimation.

Among the different modalities to assess emotion, electroencephalogram (EEG), representing the electrical brain activity, achieved motivating results over the last decade. Emotion estimation from EEG could help in the diagnosis or rehabilitation of certain diseases. In this paper, we propose a dual model considering two different representations of EEG feature maps: 1) a sequential based representation of EEG band power, 2) an image-based representation of the feature vectors. We also propose an innovative method to combine the information based on a saliency analysis of the image-based model to promote joint learning of both model parts. The model has been evaluated on four publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results outperform results from state-of-the-art approaches for three of the proposed datasets with a lower standard deviation that reflects higher stability. For sake of reproducibility, the codes and models proposed in this paper are available at https://github.com/VDelv/Emotion-EEG.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder affecting a certain amount of children and their way of living. A novel method to treat this disorder is to use Brain-Computer Interfaces (BCI) throughout the patient learns to self-regulate his symptoms by herself. In this context, researches have led to tools aiming to estimate the attention toward these interfaces. In parallel, the democratization of virtual reality (VR) headset, and the fact that it produces valid environments for several aspects: safe, flexible and ecologically valid have led to an increase of its use for BCI application. Another point is that Artificial Intelligence (AI) is more and more developed in different domain among which medical application. In this paper, we present an innovative method aiming to estimate attention from the measurement of physiological signals: Electroencephalogram (EEG), gaze direction and head movement. This framework is developed to assess attention in VR environments. We propose a novel approach for feature extraction and a dedicated Machine Learning model. The pilot study has been applied on a set of volunteer and our approach presents a lower error rate in comparison with the state of the art methods.

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. Image-based saliency estimation being participant independent, the estimation from EEG could take into account the subject specificity. For this purpose, a set of signals has been recorded, and different models have been developed to study the relationship between visual attention and brain activity. The results are encouraging and comparable with other approaches estimating attention with other modalities. Being able to predict a visual saliency map from EEG could help in research studying the relationship between brain activity and visual attention. It could also help in various applications: vigilance assessment during driving, neuromarketing, and also in the help for the diagnosis and treatment of visual attention-related diseases. For the sake of reproducibility, the codes and dataset considered in this paper have been made publicly available to promote research in the field.

Among the different modalities to assess emotion, electroencephalogram (EEG), representing the electrical brain activity, achieved motivating results over the last decade. Emotion estimation from EEG could help in the diagnosis or rehabilitation of certain diseases. In this paper, we propose a dual model considering two different representations of EEG feature maps: 1) a sequential based representation of EEG band power, 2) an image-based representation of the feature vectors. We also propose an innovative method to combine the information based on a saliency analysis of the image-based model to promote joint learning of both model parts. The model has been evaluated on four publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results outperform results from state-of-the-art approaches for three of the proposed datasets with a lower standard deviation that reflects higher stability. For sake of reproducibility, the codes and models proposed in this paper are available at https://github.com/VDelv/Emotion-EEG.

For many years now, understanding the brain mechanism has been a great research subject in many different fields. Brain signal processing and especially electroencephalogram (EEG) has recently known a growing interest both in academia and industry. One of the main examples is the increasing number of Brain-Computer Interfaces (BCI) aiming to link brains and computers. In this paper, we present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals. While previous methods often consider the spatial relationship in EEG through electrodes and process them in recurrent or convolutional based architecture, we propose here to also exploit the spatial and temporal information with a transformer-based network that has already shown its supremacy in many machine-learning (ML) related studies, e.g. machine translation. In addition to this novel architecture, an extensive study on the feature extraction methods, frequential bands and temporal windows length has also been carried out. The proposed network has been trained and validated on two public datasets and achieves higher results compared to state-of-the-art models. As well as proposing better results, the framework could be used in real applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD) symptoms or vigilance during a driving assessment.