Exploration of the effect of EEG Levels in experienced archers

This preliminary study aims to record the brainwaves of two experienced archers, whist undertaking the process of aiming and shooting arrows at a target. The brainwaves are then analysed for repeatability and dominant characteristics within individual EEG activity. Images of the archers are also recorded to establish reference points within the shot cycle for correlating the EEG data sets

Data Imputation in EEG Signals for Brainprint Identification

Electroencephalograms (EEG) signals have very low signal-to-noise ratio, thus can be easily affected and changes over milliseconds. Normally, trials with excessive body movements or other types of artefacts with amplitude more than 100 µV should be discarded to reduce the noise stains. Scrapping the affected features is not advisable. Therefore, missing values imputation is essential to avoid incomplete data that may deteriorate the computational modelling performance. Hence, this paper proposes a similarity matching method to replace the missing values in the EEG trials. The main idea of the missing values imputation is based on the similarity measure between the trials. The trials with the highest similarity is taken to replace the missing values for the related EEG channels. Statistical evaluation and classification evaluation are used to evaluate the reliability of the proposed similarity matching method. The mean, variance and standard deviation are used for statistical evaluation. For the classification evaluation, the dataset is classified for brainprint identification by using the Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN). The statistical evaluation proved that the proposed similarity matching imputation method is promising when the missing values are not come from the same channels. The classification results achieved the excellent performance with 98.19% in accuracy and 0.998 in AUC

Effect of Mycophenolate Mofetil on Plasma Bioelements in Renal Transplant Recipients

The proper concentrations of plasma bioelements may favorably reduce the incidence of metabolic disorders, which often occur during immunosuppressive therapy. Mycophenolate mofetil (MMF) is currently one of the most frequently administered immunosuppressive agents; however, MMF treatment is often related to gastrointestinal side effects. The aim of this study was thus to verify whether the MMF treatment itself, or its metabolite pharmacokinetics, has an effect on the concentrations of plasma bioelements. To determine this, the effect of MMF on the levels of both major (sodium [Na], potassium [K], calcium [Ca], magnesium [Mg]), and trace (iron [Fe], zinc [Zn], copper [Cu]) plasma bioelements in 61 renal transplant recipients was assessed in comparison to a control group (n = 45). The pharmacokinetic parameters of mycophenolic acid were determined by the high-performance liquid chromatography method. All patients filled out a 24-h diet history questionnaire. The results showed high plasma concentrations of Fe and low plasma concentrations of Mg and Zn as compared with diagnostic norms. The patients treated with MMF had significantly lower plasma Na (P < 0.001) and significantly higher plasma Zn (P = 0.030) and Cu concentrations (P < 0.001). In conclusion, MMF treatment was found to affect plasma Fe, Zn, and Cu levels by increasing their concentrations while decreasing the plasma Na concentration. Mg and Zn deficiencies, as well as excessive Fe levels, are frequently observed irrespective of the immunosuppressive regimen applied, which suggests that monitoring of these bioelements may be favorable

An approach to emotion recognition in single-channel EEG signals: a mother child interaction

In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology -- Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains -- Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness20th Argentinean Bioengineering Society Congress, SABI 2015 (XX Congreso Argentino de Bioingeniería y IX Jornadas de Ingeniería Clínica)28–30 October 2015, San Nicolás de los Arroyos, Argentin

kNN and SVM classification for EEG: a review

This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances