Summary: Attention is defined as the cognitive process to detect a particular internal or external stimulus, and maintaining focus are closely related to mood. The orientation of the attention resource (Attention allocation) is shaped by the mood of the person. Focusing more on negative and threatening stimuli than neutral and/or positive stimuli in the outside world is called “Attentional Bias”. This article emphasizes that attentional bias is linked with the level of depressive mood state, between a low level of depressive mood and a high level of depression. Aim: This research aimed to examine if there is an attentional bias toward negative stimuli among individuals with depressive symptoms. Materials and Methods: The Hamilton Depression Rating Scale and the Point Locating Task were administered to the participants. The research consists of a sample of 90 undergraduate and graduate students selected by random sampling method. Results: Based on the research findings, there is a positive and significant relationship between the level of depression and attentional orientation. The result of the study indicated that there is a significant negative relationship between depression scores and attentional orientation. When the attentional bias of the participants was examined according to their depression levels, it was found that the attentional bias of the participants with moderate depression symptoms was significantly higher than those without depression symptoms. Conclusion: These results indicated that relationship between attentional bias and depression level. Further studies are needed to examine depression levels and attentional bias levels in a larger sample size.

Aim: Some of the systems that use brain–computer interfaces (BCIs) that translate brain activity patterns into commands for an interactive application make use of samples produced by motor imagery. This study focuses on processing electroencephalogram (EEG) signals using convolutional neural network (CNN). It is aimed to analyze EEG signals using Python, convert data to spectrogram, and classify them with CNN in this article. Materials and Methods: EEG data used were sampled at a sampling frequency of 128 Hz, in the range of 0.5–50 Hz. The EEG file is processed using Python programming language. Spectrogram images of the channels were obtained with the Python YASA library. Results: The success of the CNN model applied to dataset was found to be 89.58%. Conclusion: EEG signals make it possible to detect diseases using various machine learning methods. Deep learning-based CNN algorithms can also be used for this purpose.

Introduction: Human-machine interfaces (HMIs) can improve the quality of life for physically disabled users. This study proposes a noninvasive BMI design methodology to control a robot arm using MEG signals acquired during the user's imagined wrist movements in four directions. Methods: The BMI uses the partial directed coherence measure and a time-varying multivariate adaptive autoregressive model to extract task-dependent features for mental task discrimination. An extreme learning machine is used to generate a model with the extracted features, which is used to control the robot arm for rehabilitation or assistance tasks for motor-impaired individuals. Results: The classification results show that the proposed BMI methodology is a feasible solution with good performance and fast learning speed. Discussion: The proposed BMI methodology is a promising solution for rehabilitation or assistance systems for motor-impaired individuals. The BMI provides satisfactory classification performance at a fast learning speed.

Aim: In real-time control of prosthesis, orthosis, and human–computer interface applications, the displacement of surface electrodes may cause a total disruption or a decline in the classification rates. In this study, a constrained independent component analysis (cICA) was used as an alternative method for addressing the displacement problem of surface electrodes. Materials and Methods: The study was tested by classifying six-hand gestures offline and in real-time to control a robotic arm. The robotic arm has five degrees of freedom, and it was controlled using surface electromyography (sEMG) signals. The classification of sEMG signals is realized using artificial neural networks. cICA algorithm was utilized to improve the performance of classifiers due to the negative effect of electrode displacement issues. Results: In the study, the classification results of the cICA applied and unapplied sEMG signals were compared. The results showed that the proposed method has provided an increase between 4% and 13% in classifications. The average classification rates for six different hand gestures were calculated as 96.66%. Conclusions: The study showed that the cICA method enhances classification rates while minimizing the impact of electrode displacement. The other advantage of the cICA algorithm is dimension reduction, which is important in real time applications. To observe the performance of the cICA in the real-time application, a robotic arm was controlled using sEMG signals.

Aim: The framework of this research is to examine the effects of autonomous sensory meridian responses (ASMRs) sensory/impulse circularity, psychological infrastructure, and the effects of brand advertisements using this technique on consumer behaviors and physiological outcomes such as product attitude, purchase intention, advertisement taste, and perceived visual advertisement esthetics. Materials and Methods: Mixed research method was used in the study, which consisted of consumers with high depressive mood and anxiety level (experimental group) and consumers with low depressive mood and anxiety level (control group). Electrodermal activity measurement and facial reading (facial coding) analysis are two specific neuromarketing research techniques utilized in this research. In addition, consumer attitude scales and psychological scales were employed. Results: According to the results obtained from the findings of the study, the physiological and attitudinal effects of ASMR advertisements do not show significant differences between the experimental and control groups. This is due to the fact that ASMR varies from person to person and has an atypical physiological pattern. Conclusion: The fact that ASMR is an ambiguous and contradictory experience with different physiological profiles due to factors such as causality, connectivity and relativity is consistent with the findings of this research.