Enhancing Visuospatial Attention Performance with Brain-Computer Interfaces

International audienceVisuospatial attention is often investigated with features related to the head or the gaze during Human-Computer Interaction (HCI). However the focus of attention can be dissociated from overt responses such as eye movements, and impossible to detect from behavioral data. Actually, Electroencephalography (EEG) can also provide valuable information about covert aspects of spatial attention. Therefore we propose a innovative approach in view of developping a Brain-Computer Interface (BCI) to enhance human reaction speed and accuracy. This poster presents an offline evaluation of the approach based on physiological data recorded in a visuospatial attention experiment. Finally we discuss about the future interface that could enhance HCI by displaying visual information at the focus of attention

Adaptive and Warning Displays with Brain-Computer Interfaces : Enhanced Visuospatial Attention Performance

Some parts of this work have been covered by a patent, application (n° 13 60563) at Institut National de la Propriété Intellectuelle (INPI)International audienceBrain-Computer Interfaces (BCI) can provide innovative solutions beyond the medical domain. In human research, visuospatial attention is often assessed from shifts in head or gaze orientation. However in some critical situations, these behavioral features can be dissociated from covert attention processes and brain features may indicate more reliably the spatial focus of attention. In this context, we investigate whether EEG signals could be used to enhance the behavioral performance of human subjects in a visuospatial attention task. Our results demonstrate that a BCI protocol based on adaptive or warning displays can be developed to shorten the reaction time and improve the accuracy of responses to complex visual targets

BRAIN COMPUTER INTERFACE (BCI) ON ATTENTION: A SCOPING REVIEW

Technological innovations are now an integral part of healthcare. Brain-computer interface (BCI) is a novel technological intervention system that is useful in restoring function to people disabled by neurological disorders such as attention deficit hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. This paper surveys the literature concerning the effectiveness of BCI on attention in subjects under various conditions. The findings of this scoping review are that studies have been made on ADHD, ALS, ASD subjects, and subjects recovering from brain and spinal cord injuries. BCI based neurofeedback training is seen to be effective in improving attention in these subjects. Some studies have also been made on healthy subjects.BCI based neurofeedback training promises neurocognitive improvement and EEG changes in the elderly. Different cognitive assessments have been tried on healthy adults.   From this review, it is evident that hardly any research has been done on using BCI for enhancing attention in post-stroke subjects. So there arises the necessity for making a study on the effects of BCI based attention training in post-stroke subjects, as attention is the key for learning motor skills that get impaired following a stroke. Currently, many researches are underway to determine the effects of a BCI based training program for the enhancement of attention in post-stroke subjects

Brain enhancement through cognitive training: A new insight from brain connectome

Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function

Simulating naturalistic instruction: the case for a voice mediated interface for assistive technology for cognition

A variety of brain pathologies can result in difficulties performing complex behavioural sequences. Assistive technology for cognition (ATC) attempts support of complex sequences with the aim of reducing disability. Traditional ATCs are cognitively demanding to use and thus have had poor uptake. A more intuitive interface may allow ATCs to reach their potential. Insights from psychological science may be useful to technologists in this area. We propose that an auditory-verbal interface is more intuitive than a visual interface and reduces cognitive demands on users. Two experiments demonstrate a novel ATC, the General User Interface for Disorders of Execution (GUIDE). GUIDE is novel because it simulates normal conversational prompting to support task performance. GUIDE provides verbal prompts and questions and voice recognition allows the user to interact with the GUIDE. Research with non-cognitively impaired participants and a single participant experiment involving a person with vascular dementia provide support for using interactive auditory-verbal interfaces. Suggestions for the future development of auditory-verbal interfaces are discussed

視空間支援のためのデバイスアート:人間の反響定位能力の拡張

この博士論文は全文公表に適さないやむを得ない事由があり要約のみを公表していましたが、解消したため、令和３(2021)年1月18日に全文を公表しました。筑波大学 (University of Tsukuba)201

Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges

We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention

Electroencephalography-Based Brain–Machine Interfaces in Older Adults: A Literature Review

The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain–machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users’ needs are considered

Framework for Electroencephalography-based Evaluation of User Experience

Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to estimate continuously the user's mental workload, attention and recognition of interaction errors during different interaction tasks. We validate these measures on a controlled virtual environment and show how they can be used to compare different interaction techniques or devices, by comparing here a keyboard and a touch-based interface. Thanks to such a framework, EEG becomes a promising method to improve the overall usability of complex computer systems.Comment: in ACM. CHI '16 - SIGCHI Conference on Human Factors in Computing System, May 2016, San Jose, United State