Design requirements and potential target users for brain-computer interfaces–recommendations from rehabilitation professionals

It is an implicit assumption in the field of brain-computer interfacing (BCI) that BCIs can be satisfactorily used to access augmentative and alternative communication (AAC) methods by people with severe physical disabilities. A one-day workshop and focus group interview was held to investigate this assumption. Rehabilitation professionals (N = 28) were asked to critically assess current BCI technology, recommend design requirements and identify target users. The individual answers were analyzed using the theoretical framework of grounded theory. None of the participants expressed a perception of added value of current BCIs over existing alternatives. A major criticism (and requirement) was that the usability of BCI systems should significantly improve. Target users are only those who can hardly or not at all use alternative access technologies. However, such persons often have concurrent physical, sensory, and cognitive problems, which could complicate BCI use. If successful BCI use continues to require a user to sit motionlessly and have intact cognition, then–as previously implicitly assumed–people in the locked-in state (resulting from late-stage amyotrophic lateral sclerosis, multiple sclerosis, spinal muscular atrophy type II or classic or total locked-in syndrome) and people with high spinal cord injury (C1/C2) could be target users.</p

Using stealth marketing techniques to increase physical activity and decrease sedentary time in the workplace: A feasibility study investigating the spill-overs of employee pro-environmental behaviour

Sedentary lifestyles have adverse effects on health and wellbeing and are especially prevalent amongst office-based employees. This project goes above and beyond currently existing physical activity initiatives in the workplace, by examining the feasibility of using a “Bait-and-Tease” stealth marketing intervention promoting increased physical activity and reduction of sedentary behaviour in the workplace amongst office-based employees. The intervention focused on promoting employee pro-environmental behaviour in the workplace (i.e., energy saving and recycling). This was the “Bait” part of the technique, which made no reference to physical activity. The spillovers of employee pro-environmental behaviour change on employee physical activity and sedentary behaviour were then evaluated. This was followed by a reveal stage, the “Tease” part of the technique, where the link between health and the environment was made explicit (e.g., taking the stairs instead of the elevator saves energy while also increasing walking time) and participants were informed of the true purpose of the intervention. Initial employee focus groups, grounded on the Behaviour Change Wheel framework, fed into an intervention codevelopment workshop. The developed intervention, which included an informational campaign and a green champion, was piloted within a Higher Education Institution and targeted academics, professional service members, and postgraduate research students as university employees with office-based jobs. The pilot involved an intervention and a control-group, with a “before” and “after” research design. Both self-reported (i.e., employee surveys measuring pro-environmental behaviour) and observational (i.e., tracking walking and standing time via a mobile application, recording sedentary time and counting stairs via trained observers) data were collected. Results indicate that the intervention was found feasible and the pilot study shows potential for large-scale implementation, even though the pilot sample size was small. The goals of the study were achieved and problems in relation to recruitment, adherence and measurements were identified with multiple future research directions

Towards a novel monitor of intraoperative awareness: Selecting paradigm settings for a movement-based brain-computer interface

Contains fulltext : 103039.pdf (publisher's version ) (Open Access)During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.9 p

Stimulus sequence.

<p>Each sequence started with an auditory sequence instruction, i.e. ‘No Movement’ or ‘Both Arms Movement’. Following the instruction were nine trials consisting of a cue (task) and their corresponding baseline periods used for analysis.</p

Cumulative probability of true positive monitor output after start of movement.

<p>Movement was assumed to start at trial 1 (t = 0 s) with a trial duration of 8 seconds. As in this paradigm 4 positive classifications in a row are needed for a positive monitor output, the first possible positive monitor output is at trial 4 i.e. 32 seconds after movement onset. For each subject plus the average of all subjects, the solid line shows the output for the recorded sequences. The dashed lines show the interpolation of this output for another 9 trials using a Monte Carlo simulation.</p

Classification rates using ten-fold cross-validation (10-fold) versus using only the 1^st experimental block (1^st block) for training.

<p>Classification rates using ten-fold cross-validation (10-fold) versus using only the 1^st experimental block (1^st block) for training.</p

Single trial classification rates per subject using different frequency ranges.

<p>Calculations were done using 9 channels and the combined ERD and ERS periods.</p

Single trial classification rates per subject using different frequency resolutions.

<p>Calculations were done using 9 channels and the combined ERD and ERS periods.</p