Data S1: Data

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device

Changes in the EEG Spectrum of a Child with Severe Disabilities in Response to Power Mobility Training

Literature suggests that self-generated locomotion in infancy and early childhood enhances the development of various cognitive processes such as spatial awareness, social interaction, language development and differential attentiveness. Thus, having access to a power mobility device may play a crucial role for the overall development, mental health, and quality of life of children with multiple, severe disabilities who have limited motor control. This study investigates the feasibility of using electroencephalography (EEG) as an objective measure to detect changes in brain activity in a child due to power mobility training. EEG data was collected with a modified wireless neuroheadset using a single-subject A-B-A-B design consisting of two baseline phases (A) and two intervention phases (B). One trial consisted of three different activities during baseline phase; resting condition at the beginning (Resting 1) and at the end (Resting 2) of the trial, interaction with adults, and passive mobility. The intervention phase included a forth activity, the use of power mobility, while power mobility training was performed on another day within the same week of data collection. The EEG spectrum between 2.0 and 12.0 Hz was analyzed for Resting 1 and Resting 2 condition in each phase. We found significant increase of theta power and decrease in alpha power during all three phases following the first baseline. In respect of previous findings, these observations may be related to an increase in alertness and/or anticipation. Analysis of the percentage change from Resting 1 to Resting 2 condition revealed decrease in theta and increasing alpha power during the first intervention phase, which could be associated with increasing cognitive capacity immediately after the use of power mobility. Overall, no significant difference between baseline phase and intervention phase was observed. Thus, whether the observed changes may have been influenced or enhanced by power mobility training remains unclear and warrants further investigation

Quantitative and Qualitative Trade-Off Analysis of Drowsy Driver Detection Methods: Single Electrode Wearable EEG Device, Multi-Electrode Wearable EEG Device, and Head-Mounted Gyroscope

Drowsy driving impairs motorists’ ability to operate vehicles safely, endangering both the drivers and other people on the road. The purpose of the project is to find the most effective wearable device to detect drowsiness. Existing research has demonstrated several options for drowsiness detection, such as electroencephalogram (EEG) brain wave measurement, eye tracking, head motions, and lane deviations. However, there are no detailed trade-off analyses for the cost, accuracy, detection time, and ergonomics of these methods. We chose to use two different EEG headsets: NeuroSky Mindwave Mobile (single-electrode) and Emotiv EPOC (14- electrode). We also tested a camera and gyroscope-accelerometer device. We can successfully determine drowsiness after five minutes of training using both single and multi-electrode EEGs. Devices were evaluated using the following criteria: time needed to achieve accurate reading, accuracy of prediction, rate of false positives vs. false negatives, and ergonomics and portability. This research will help improve detection devices, and reduce the number of future accidents due to drowsy driving

Development and gamification of a neurofeedback application to support anxiety treatment

Anxiety disorder is one of the most common mental illness, affecting 264 million people worldwide. Current treatments are limited because they cause side effects and/or require long therapeutic periods. Neurofeedback (NFB) has been associated with the reduction of anxious symptomatology but typically uses unappealing therapeutic interventions. In this work a game was developed, Anxiety Destroyer, using procedures of gamification adapted to the NFB, with the goal of providing a guided, immerse and efficient treatment, to be used at home. The lean method was applied to the development process by construction-measurement-learning of value propositions. Thirty people were studied, which performed up to 8 NFB sessions for 35 minutes, totalling 185 sessions, 108 hours. Three groups of 10 individuals were created, one group with frontal alpha asymmetry (Alpha Asymmetry (ALAY)) protocol (F4-F3), another with asymmetry between AF4 and AF3 channels (AF) for hairless skin usage and another control group, with increased alpha in F4 and F3 (F+). The Beck Anxiety Inventory (BAI) was applied to assess anxiety. The Game Experience Questionnaire (GEQ) was used to evaluate game experience in different versions, which were adapted to the needs of the user. The ALAY group significantly increased the asymmetry (p <0.05) (reduction of the anxiety biomarker), with a corresponding significant reduction (p <0.01) in the anxiety reported by BAI after NFB training. The AF group showed a decrease in the frontal alpha asymmetry, and the F+ a slight increase, respectively confirmed by the increase and decrease of the BAI. The GEQ did not show better gaming experience throughout sessions. An increase of the sample size is required to confirm these results, inclusively since some individuals in the AF and F+ groups abandoned treatment because of no perceived effects. However, this study suggests the effectiveness of the gamified application in the treatment of anxiety

Study and experimentation of cognitive decline measurements in a virtual reality environment

À l’heure où le numérique s’est totalement imposé dans notre quotidien, nous pouvons nous demander comment évolue notre bien-être. La réalité virtuelle hautement immersive permet de développer des environnements propices à la relaxation qui peuvent améliorer les capacités cognitives et la qualité de vie de nombreuses personnes. Le premier objectif de cette étude est de réduire les émotions négatives et améliorer les capacités cognitives des personnes souffrant de déclin cognitif subjectif (DCS). À cette fin, nous avons développé un environnement de réalité virtuelle appelé Savannah VR, où les participants ont suivi un avatar à travers une savane. Nous avons recruté dix-neuf personnes atteintes de DCS pour participer à l’expérience virtuelle de la savane. Le casque Emotiv Epoc a capturé les émotions des participants pendant toute l’expérience virtuelle. Les résultats montrent que l’immersion dans la savane virtuelle a réduit les émotions négatives des participants et que les effets positifs ont continué par la suite. Les participants ont également amélioré leur performance cognitive. La confusion se manifeste souvent au cours de l’apprentissage lorsque les élèves ne comprennent pas de nouvelles connaissances. C’est un état qui est également très présent chez les personnes atteintes de démence à cause du déclin de leurs capacités cognitives. Détecter et surmonter la confusion pourrait ainsi améliorer le bien-être et les performances cognitives des personnes atteintes de troubles cognitifs. Le deuxième objectif de ce mémoire est donc de développer un outil pour détecter la confusion. Nous avons mené deux expérimentations et obtenu un modèle d’apprentissage automatique basé sur les signaux du cerveau pour reconnaître quatre niveaux de confusion (90% de précision). De plus, nous avons créé un autre modèle pour reconnaître la fonction cognitive liée à la confusion (82 % de précision).At a time when digital technology has become an integral part of our daily lives, we can ask ourselves how our well-being is evolving. Highly immersive virtual reality allows the development of environments that promote relaxation and can improve the cognitive abilities and quality of life of many people. The first aim of this study is to reduce the negative emotions and improve the cognitive abilities of people suffering from subjective cognitive decline (SCD). To this end, we have developed a virtual reality environment called Savannah VR, where participants followed an avatar across a savannah. We recruited nineteen people with SCD to participate in the virtual savannah experience. The Emotiv Epoc headset captured their emotions for the entire virtual experience. The results show that immersion in the virtual savannah reduced the negative emotions of the participants and that the positive effects continued afterward. Participants also improved their cognitive performance. Confusion often occurs during learning when students do not understand new knowledge. It is a state that is also very present in people with dementia because of the decline in their cognitive abilities. Detecting and overcoming confusion could thus improve the well-being and cognitive performance of people with cognitive impairment. The second objective of this paper is, therefore, to develop a tool to detect confusion. We conducted two experiments and obtained a machine learning model based on brain signals to recognize four levels of confusion (90% accuracy). In addition, we created another model to recognize the cognitive function related to the confusion (82% accuracy)

Applications of electroencephalography in construction

A wearable electroencephalogram (EEG) is considered a means for investigating psychophysiological conditions of individuals in the workplace in order to ameliorate occupational health and safety. Following other sectors, construction scholars have adopted this technology over the past decade to strengthen evidence-based practices to improve the wellbeing of workers. This study presents the state-of-the-art hardware, algorithms, and applications of EEG as a platform that assists in dealing with the risk-prone and complex nature of construction tasks. After summarizing the background of EEG and its research paradigms in different sectors, a comprehensive review of EEG-enabled construction research is provided. First, through a macro-scale review aided by bibliometric analysis a big picture of the research streams is plotted. Second, a micro-scale review is conducted to spot the gaps in the literature. The identified gaps are used to classify the future research directions into theoretical, application, and methodological developments

Innovative Man Machine Interfaces In Aeronautics

The research activity focused on the study, design and evaluation of innovative human-machine interfaces based on virtual three-dimensional environments. It is based on the brain electrical activities recorded in real time through the electrical impulses emitted by the brain waves of the user. The achieved target is to identify and sort in real time the different brain states and adapt the interface and/or stimuli to the corresponding emotional state of the user. The setup of an experimental facility based on an innovative experimental methodology for “man in the loop" simulation was established. It allowed involving during pilot training in virtually simulated flights, both pilot and flight examiner, in order to compare the subjective evaluations of this latter to the objective measurements of the brain activity of the pilot. This was done recording all the relevant information versus a time-line. Different combinations of emotional intensities obtained, led to an evaluation of the current situational awareness of the user. These results have a great implication in the current training methodology of the pilots, and its use could be extended as a tool that can improve the evaluation of a pilot/crew performance in interacting with the aircraft when performing tasks and procedures, especially in critical situations. This research also resulted in the design of an interface that adapts the control of the machine to the situation awareness of the user. The new concept worked on, aimed at improving the efficiency between a user and the interface, and gaining capacity by reducing the user’s workload and hence improving the system overall safety. This innovative research combining emotions measured through electroencephalography resulted in a human-machine interface that would have three aeronautical related applications: • An evaluation tool during the pilot training; • An input for cockpit environment; • An adaptation tool of the cockpit automation