Attention Estimation in Virtual Reality with EEG based Image Regression

Abstract

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.

Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review

“…A brief description of each of the ML/AI technologies considered is provided in Table 11. ( Abdurrahman et al, 2021), (Bhalla et al, 2021), (De Bruyne et al, 2021), (Delvigne et al, 2022), (Delvigne et al, 2020), (Georgiou & Demiris, 2017), (Jones et al, 2020), (Liebers et al, 2021), (Leiker et al, 2016), (Migovich et al, 2021), (Noghabaei et al, 2021), (Porssut et al, 2022), (Shi et al, 2020), (Tsai et al, 2021), (Wilson et al, 2021), (Zhang et al, 2017) From (Porssut et (Granato et al, 2020), (Ihmig et al, 2020), (Islam et al, 2019), (Kim et al, 2019), (Luong et al, 2020), (Martin et al, 2020), (Migovich et al, 2021), (Sakib et al, 2020), (Sayis et al, 2022), (Tabbaa et al, 2022), (Tsai et al, 2021), (Wilson et al, 2021), (Zhang et al, 2017) From (Martin et al, 2020) (continued) (Bauer et al, 2019), (Bin Suhaimi et al, 2020), (Delvigne et al, 2022), (Delvigne et al, 2020), (Jones et al, 2020), (Kaur et al, 2019), (Kim et al, 2020), (McDermott et al, 2022), …”

PhyDAA: Physiological Dataset Assessing Attention

“…Each layer is composed of 4, 2 and 1 convolution stacks respectively, and the number of channels at the end of each layer was: 16, 64 and128 respectively. The figures were chosen after experimentation with different CNN architectures and after considering a preliminary study for attention regression from image representation of EEG, as presented in Delvigne et al [17].…”

Application of eye-tracking systems integrated into immersive virtual reality and possible transfer to the sports sector - A systematic review

“…With this setup, the impact of stressors on effective learning can be examined. The integration of physiological measurements within VR scenes has also been done in which EEG signals are used to examine participants' attention further [19]. Another research direction focused on user identification methods through human kinesiological movements and gaze data [63].…”