VR for Vocational and Ecological Rehabilitation of Patients With Cognitive Impairment: A Survey.

Cognitive impairment arises from various brain injuries or diseases, such as traumatic brain injury, stroke, schizophrenia, or cancer-related cognitive impairment. Cognitive impairment can be an obstacle for patients to the return-to-work. Research suggests various interventions using technology for cognitive and vocational rehabilitation. The present work offers an overview of sixteen vocational or ecological VR-based clinical studies among patients with cognitive impairment. The objective is to analyze these studies from a VR perspective focusing on the VR apparatus and tasks, adaptivity, transferability, and immersion of the interventions. Our results highlight how a higher level of immersion could bring the participants to a deeper level of engagement and transferability, rarely assessed in current literature, and a lack of adaptivity in studies involving patients with cognitive impairments. From these considerations, we discuss the challenges of creating a standardized yet adaptive protocol and the perspectives of using immersive technologies to allow precise monitoring, personalized rehabilitation and increased commitment.

Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration.

Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. This represents one of the first simultaneous recording of EEG and fMRI for NF and here we present the first open access bi-modal NF dataset integrating EEG and fMRI. We believe that it will be a valuable tool to (1) advance and test methodologies for multi-modal data integration, (2) improve the quality of NF provided, (3) improve methodologies for de-noising EEG acquired under MRI and (4) investigate the neuromarkers of motor-imagery using multi-modal information.