A novel onset detection technique for brain?computer interfaces using sound-production related cognitive tasks in simulated-online system

Objective. Self-paced EEG-based BCIs (SP-BCIs) have traditionally been avoided due to two sources of uncertainty: (1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and (2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues. Approach. Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies?Bouldin index was used for feature selection. Classification was performed using linear discriminant analysis. Main results. Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive (TP) rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% TP rate, 4.05% false-positive rate). Significance. Results were promising when compared to previous IC onset detection studies using motor imagery, in which best TP rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in SP-BCIs

Multiresolution analysis over graphs for a motor imagery based online BCI game

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain–computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human–machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes

Inequalities in the prevalence, diagnosis awareness, treatment coverage and effective control of diabetes: a small area estimation analysis in Iran

Abstract Objective This study aims to assess geographic inequalities in the prevalence, awareness of diagnosis, treatment coverage and effective control of diabetes in 429 districts of Iran. Methods A modelling study by the small area estimation method, based on a nationwide cross-sectional survey, Iran STEPwise approach to surveillance (STEPS) 2016, was performed. The modelling estimated the prevalence, awareness of diagnosis, treatment coverage, and effective control of diabetes in all 429 districts of Iran based on data from available districts. The modelling results were provided in different geographical and socio-economic scales to make the comparison possible across the country. Results In 2016, the prevalence of diabetes ranged from 3.2 to 19.8% for women and 2.4 to 19.1% for men. The awareness of diagnosis ranged from 51.9 to 95.7% for women and 35.7 to 100% for men. The rate of treatment coverage ranged from 37.2 to 85.6% for women and 24.4 to 80.5% for men. The rate of effective control ranged from 12.1 to 63.6% for women and 12 to 73% for men. The highest treatment coverage rates belonged to Ardebil for women and Shahr-e-kord for men. The highest effective control rates belonged to Sanandaj for women and Nehbandan for men. Across Iran districts, there were considerable differences between the highest and lowest rates of prevalence, diagnosis awareness, treatment coverage, and effective control of diabetes. The concentration indices of diabetes prevalence, awareness of diagnosis, and treatment coverage were positive and significant for both sexes. Conclusion Findings of this study highlight the existence of inequalities in diagnosis awareness, treatment coverage, and effective control of diabetes in all Iran regions. More suitable population-wide strategies and policies are warranted to handle these inequalities in Iran