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.

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.

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.
Keywords: Brain–machine interface, extreme learning machine, functional.

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