Joe Wiart

Subject-Specific Channel Selection Using Time Information for Motor Imagery Brain–Computer Interfaces

Keeping a minimal number of channels is essential for designing a portable brain–computer interface system for daily usage. Most existing methods choose key channels based on spatial information without optimization of time segment for classification. This paper proposes a novel subject-specific channel selection method based on a criterion called F score to realize the parameterization of both time segment and channel positions. The F score is a novel simplified measure derived from Fisher’s discriminant analysis for evaluating the discriminative power of a group of features. The experimental results on a standard dataset (BCI competition III dataset IVa) show that our method can efficiently reduce the number of channels (from 118 channels to 9 in average) without a decrease in mean classification accuracy. Compared to two state-of-the-art methods in channel selection, our method leads to comparable or even better classification results with less selected channels.

Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels

Abstract The essential task of a motor imagery brain–computer interface (BCI) is to extract the motor imagery-related features from electroencephalogram (EEG) signals for classifying motor intentions. However, the optimal frequency band and time segment for extracting such features differ from subject to subject. In this work, we aim to improve the multi-class classification and to reduce the required EEG channel in motor imagery-based BCI by subject-specific time-frequency selection. Our method is based on a criterion namely Fisher discriminant analysis-type F-score to simultaneously select the optimal frequency band and time segment for multi-class classification. The proposed method uses only few Laplacian EEG channels (C3, Cz and C4) located around the sensorimotor area for classification. Applied to a standard multi-class BCI dataset (BCI competition III dataset IIIa), our method leads to better classification performance and smaller standard deviation across subjects compared to the state-of-art methods. Moreover, adding artifacts contaminated trials to the training dataset does not necessarily deteriorate our classification results, indicating that our method is tolerant to artifacts.

Patterns of cellular phone use among young people in 12 countries: Implications for RF exposure

Characterizing exposure to radiofrequency (RF) fields from wireless telecommunications technologies during childhood and adolescence is a research priority in investigating the health effects of RF. The Mobi-Expo study aimed to describe characteristics and determinants of cellular phone use in 534 young people (10-24years) in 12 countries. The study used a specifically designed software application installed on smartphones to collect data on the use of wireless telecommunications devices within this age group. The role of gender, age, maternal education, calendar period, and country was evaluated through multivariate models mutually adjusting for all variables. Call number and duration were higher among females compared to males (geometric mean (GM) ratio 1.17 and 1.42, respectively), among 20-24year olds compared to 10-14year olds (GM ratio 2.09 and 4.40, respectively), and among lowest compared to highest social classes (GM ratio 1.52 and 1.58, respectively). The number of SMS was higher in females (GM ratio 1.46) and the middle age group (15-19year olds: GM ratio 2.21 compared to 10-14year olds) and decreased over time. Data use was highest in the oldest age group, whereas Wi-Fi use was highest in the middle age group. Both data and Wi-Fi use increased over time. Large differences in the number and duration of calls, SMS, and data/Wi-Fi use were seen by country, with country and age accounting for up to 50% of the variance. Hands-free and laterality of use did not show significant differences by sex, age, education, study period, or country. Although limited by a convenience sample, these results provide valuable insights to the design, analysis, and interpretation of future epidemiological studies concerning the health effects of exposure resulting from cellular phone use in young people. In addition, the information provided by this research may be used to design strategies to minimize RF exposure.

OP VI – 2 Organ-specific integrative exposure assessment for radio-frequency electromagnetic fields: general population exposure and dose contribution of various sources

Background/aim The daily dose of radio-frequency electromagnetic fields (RF-EMF) received by the human body depends on source, use, and body characteristics. We developed a model capable of estimating total RF-EMF dose (J/kg) for 64 body tissues as well as the contribution of specific sources to total dose based on personal characteristics, source characteristics, and scenarios of use. Methods The Integrated Exposure Model (IEM) uses personal characteristics and scenarios of use to estimate daily RF-EMF dose from mobile phones, DECT phones, tablets, body area networks, laptops, on/near body devices, smartwatches, virtual reality headsets, WiFi routers, and far field sources. Specific absorption rates (SAR) in various tissues were calculated for each source using transfer algorithms based on source and body characteristics. These were then adjusted for scenarios of use. Lastly, the model calculated the integrative dose from all sources combined and the relative contribution of each source. To estimate population exposure levels, we used data from an online survey on use of mobile communication devices deployed in four countries (France, the Netherlands, Spain, Switzerland). Results The online survey resulted in a dataset of 1768 participants, with a mean age of 52 years. Preliminary results indicate an average whole body dose of 0.15u2009J/kg per day, and an average whole brain dose of 0.09u2009J/kg per day. Women tended to have slightly higher doses than men, particularly in the youngest age group, due to higher reported use of mobile phones for voice and data. Source specific contribution varied depending on tissue. For the brain, the highest contribution (32%) came from mobile phones. Phone, tablet, and WiFi use together account for 91% of total brain dose. For the whole body: phone data use, WiFi, tablet, and laptop use accounted for 97% of the average total dose in our population. Conclusion We developed a model capable of estimating integrative RF-EMF dose from both current and novel devices. Using survey data on device use we were able to estimate average whole brain (0.09u2009J/kg) and average whole body (0.15u2009J/kg) dose. Device output powers in various scenarios of use were found to strongly influence model results.

Recall of mobile phone usage and laterality in young people: The multinational Mobi-Expo study

Objective To study recall of mobile phone usage, including laterality and hands‐free use, in young people. Methods Actual mobile phone use was recorded among volunteers aged between 10 and 24 years from 12 countries by the software application XMobiSense and was compared with self‐reported mobile phone use at 6 and 18 months after using the application. The application recorded number and duration of voice calls, number of text messages, amount of data transfer, laterality (% of call time the phone was near the right or left side of the head, or neither), and hands‐free usage. After data cleaning, 466 participants were available for the main analyses (recorded vs. self‐reported phone use after 6 months). Results Participants were on average 18.6 years old (IQR 15.2–21.8 years). The Spearman correlation coefficients between recorded and self‐reported (after 6 months) number and duration of voice calls were 0.68 and 0.65, respectively. Number of calls was on average underestimated by the participants (adjusted geometric mean ratio (GMR) self‐report/recorded = 0.52, 95% CI = 0.47–0.58), while duration of calls was overestimated (GMR=1.32, 95%, CI = 1.15–1.52). The ratios significantly differed by country, age, maternal educational level, and level of reported phone use, but not by time of the interview (6 vs. 18 months). Individuals who reported low mobile phone use underestimated their use, while individuals who reported the highest level of phone use were more likely to overestimate their use. Individuals who reported using the phone mainly on the right side of the head used it more on the right (71.1%) than the left (28.9%) side. Self‐reported left side users, however, used the phone only slightly more on the left (53.3%) than the right (46.7%) side. Recorded percentage hands‐free use (headset, speaker mode, Bluetooth) increased with increasing self‐reported frequency of hands‐free device usage. Frequent (≥50% of call time) reported headset or speaker mode use corresponded with 17.1% and 17.2% of total call time, respectively, that was recorded as hands‐free use. Discussion These results indicate that young people can recall phone use moderately well, with recall depending on the amount of phone use and participants’ characteristics. The obtained information can be used to calibrate self‐reported mobile use to improve estimation of radiofrequency exposure from mobile phones. HighlightsLarge multinational study on recall of mobile phone use in young participants.Mobile phone use was recorded from the phone rather than from operator data.We observed substantial systematic and random errors in recall.Results can be used as input for future studies and RF exposure modelling.

Surrogate model based on polynomial chaos of indoor exposure induced from a WLAN source

This paper presents a statistical analysis of indoor exposure induced from a WLAN source. A surrogate model based on Polynomial Chaos method is proposed. The analyzed scenario consists of an anatomical model located in an unknowing position of a room of 3×4m² and a source located in a random position but near to the wall room. A computer experimental design based in a LHS method is proposed to cover at best the entire possible configurations. To calculate the exposure, a hybridize method combining the spherical wave description and the FDTD numerical method is used. 250 simulations had been performed and two surrogated models are presented.

Simplified Assessment Method for Population RF Exposure Induced by a 4G Network

This paper presents a simplified method, based on surrogate modeling, to evaluate the day-to-day global population exposure to radio frequency (RF) electromagnetic fields (EMF) induced by a fourth-generation (4G) network, from both uplink and downlink radio emissions in a typical urban city. The uncertainties of 4G-induced RF-EMF exposure of an entire population are characterized for the first time, taking into account the variability linked to urban propagation environment, information and communication technology usage, and EMF, respectively, from personal wireless devices and Evolved Node B (eNB), as well as uplink throughput. In addition, the study focuses on a sensitivity analysis in order to assess the influence of these parameters on RF-EMF exposure. Globally, results show that the 4G-induced RF-EMF exposure follows a generalized extreme value distribution with an average value of

Long-term spatio-temporal RF-EMF exposure assessment in sensor network

1.19 times 10^{-7}

Stochastic Dosimetry Based on Low Rank Tensor Approximations for the Assessment of Children Exposure to WLAN Source

W/kg. Moreover, it is shown that, contrary to what has been observed in the third-generation (3G)-induced RF-EMF exposure, that is, the exposure is dominated by uplink radio emissions, results have highlighted the importance of received power density from eNB to the issue of 4G-induced RF-EMF exposure. In 4G, the uplink exposure from mobiles accounts for only 25% of global exposure, resulting from the high speed of uplink throughput.