Ear-EEG-based binaural speech enhancement (ee-BSE) using auditory attention detection and audiometric characteristics of hearing-impaired subjects.

Objective. Speech perception in cocktail party scenarios has been the concern of a group of researchers who are involved with the design of hearing-aid devices.Approach. In this paper, a new unified ear-EEG-based binaural speech enhancement system is introduced for hearing-impaired (HI) listeners. The proposed model, which is based on auditory attention detection (AAD) and individual hearing threshold (HT) characteristics, has four main processing stages. In the binaural processing stage, a system based on the deep neural network is trained to estimate auditory ratio masks for each of the speakers in the mixture signal. In the EEG processing stage, AAD is employed to select one ratio mask corresponding to the attended speech. Here, the same EEG data is also used to predict the HTs of listeners who participated in the EEG recordings. The third stage, called insertion gain computation, concerns the calculation of a special amplification gain based on individual HTs. Finally, in the selection-resynthesis-amplification stage, the attended speech signals of the target are resynthesized based on the selected auditory mask and then are amplified using the computed insertion gain.Main results. The detection of the attended speech and the HTs are achieved by classifiers that are trained with features extracted from the scalp EEG or the ear EEG signals. The results of evaluating AAD and HT detection show high detection accuracies. The systematic evaluations of the proposed system yield substantial intelligibility and quality improvements for the HI and normal-hearingaudiograms.Significance. The AAD method determines the direction of attention from single-trial EEG signals without access to audio signals of the speakers. The amplification procedure could be adjusted for each subject based on the individual HTs. The present model has the potential to be considered as an important processing tool to personalize the neuro-steered hearing aids.

Investigating the effect of seeing patients' pre-burn face photo on the quality of care and level of empathy of nurses with patients admitted to BICU.

BACKGROUND: One of the results of burn injuries is the appearance of scars and deformities in various organs of the body, which can cause many physical and psychological challenges to burn patients. Lack of proper communication between nurse and patient leads to inaccurate identification of psychological and social needs of these patients and thus affects the quality of care. AIM: This study aimed to investigate the effect of seeing the patient's pre-burn face photo on the quality of care and empathy of nurses with patients admitted to the burn intensive care unit. METHODS: The present study is a quasi-experimental interventional study conducted on 26 Burn Intensive Care Unit (BICU) nurses who were selected by census sampling method in 2018. Data were collected through QUALPAC Nursing Care Quality Questionnaire, Lumonica Empathy Questionnaire and Demographic Questionnaire, which were completed by nurses before and after the intervention. Pre-burn photos of patients with facial burns were displayed at the nursing station for one month and the data were compared before and after the intervention. Descriptive statistics and paired t-test and independent t-test, Mann-Whitney and Pearson correlation tests were used to analyze the data. FINDINGS: The results of present study showed that there was a statistically significant difference between the quality of nursing care (p = 0.001) and its psychological dimension (p < 0.001) before and after the intervention in the group of nurses. However, no significant relationship was found between nurses' empathy with patients before (p = 0.901) and after intervention (p = 0.001). CONCLUSION: Seeing the photo of the patient's pre-burn face and establishing a relationship between the nurse and the patient had an effect on the quality of nursing care and especially its psychological dimension. However, in order to generalize the results and implement this low-cost, convenient and low-complication intervention for all patients admitted to the intensive care unit, more studies are needed in this field.

The Effect of Creative Tasks on Electrocardiogram: Using Linear and Nonlinear Features in Combination with Classification Approaches.

Objective: Interest in the subject of creativity and its impacts on human life is growing extensively. However, only a few surveys pay attention to the relation between creativity and physiological changes. This paper presents a novel approach to distinguish between creativity states from electrocardiogram signals. Nineteen linear and nonlinear features of the cardiac signal were extracted to detect creativity states. Method: ECG signals of 52 participants were recorded while doing three tasks of Torrance Tests of Creative Thinking (TTCT/ figural B). To remove artifacts, notch filter 50 Hz and Chebyshev II were applied. According to TTCT scores, participants were categorized into the high and low creativity groups: Participants with scores higher than 70 were assigned into the high creativity group and those with scores less than 30 were considered as low creativity group. Some linear and nonlinear features were extracted from the ECGs. Then, Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to classify the groups. Results: Applying the Wilcoxon test, significant differences were observed between rest and each three tasks of creativity. However, better discrimination was performed between rest and the first task. In addition, there were no statistical differences between the second and third task of the test. The results indicated that the SVM effectively detects all the three tasks from the rest, particularly the task 1 and reached the maximum accuracy of 99.63% in the linear analysis. In addition, the high creative group was separated from the low creative group with the accuracy of 98.41%. Conclusion: the combination of SVM classifier with linear features can be useful to show the relation between creativity and physiological changes.