EEG signal
Research Papers
EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System
Purpose: Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals. Methods: We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects’ mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups. Results: After processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48 ± 1.20% and 88.60 ± 1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31 ± 1.97% and for anxiety subjects is 87.18 ± 3.51%. Conclusions: The results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.
View Full Paper →Efficacy Evaluation of Neurofeedback-Based Anxiety Relief
Anxiety disorder is a mental illness that involves extreme fear or worry, which can alter the balance of chemicals in the brain. This change and evaluation of anxiety state are accompanied by a comprehensive treatment procedure. It is well-known that the treatment of anxiety is chiefly based on psychotherapy and drug therapy, and there is no objective standard evaluation. In this paper, the proposed method focuses on examining neural changes to explore the effect of mindfulness regulation in accordance with neurofeedback in patients with anxiety. We designed a closed neurofeedback experiment that includes three stages to adjust the psychological state of the subjects. A total of 34 subjects, 17 with anxiety disorder and 17 healthy, participated in this experiment. Through the three stages of the experiment, electroencephalography (EEG) resting state signal and mindfulness-based EEG signal were recorded. Power spectral density was selected as the evaluation index through the regulation of neurofeedback mindfulness, and repeated analysis of variance (ANOVA) method was used for statistical analysis. The findings of this study reveal that the proposed method has a positive effect on both types of subjects. After mindfulness adjustment, the power map exhibited an upward trend. The increase in the average power of gamma wave indicates the relief of anxiety. The enhancement of the wave power represents an improvement in the subjects’ mindfulness ability. At the same time, the results of ANOVA showed that P < 0.05, i.e., the difference was significant. From the aspect of neurophysiological signals, we objectively evaluated the ability of our experiment to relieve anxiety. The neurofeedback mindfulness regulation can effect on the brain activity pattern of anxiety disorder patients.
View Full Paper →Visual Stimuli Generated by Biochemical Reactions Discrete Chaotic Dynamics as a Basis for Neurofeedback
Introduction. In this article a novel methodology for a neurofeedback system is proposed. It is based on the visual stimuli generated by the distributed biochemical reactions discrete chaotic dynamics (BRDCD) of brain neurons. These visual stimuli take the form of symmetrical colored images known as mandalas. Method. The proposed biofeedback system applies a BRDCD mathematical model to transform an on-line recording of EEG signals into a simulated time-series EEG and into computer generated series of mandala images. Thus, these images represent experimentally measured EEG and therefore reflect the subject's mental state. Results. It will be shown that good qualitative similarity between simulated and experimental EEG was achieved. The examples of generating series of mandala images using experimental EEG will be demonstrated. Conclusion. Based on Jung's theory of the healing power of the psychological phenomenon of mandala images, it is proposed that visual stimuli in the form of mandalas could facilitate fast and effective neurofeedback training, thereby providing a therapeutic effect.
View Full Paper →A New Method for Self-Regulation of Slow Cortical Potentials in a Timed Paradigm
A new method of slow cortical potential (SCP) biofeedback is described, in which subjects were presented with a sequence of two alternating tones. Subjects learned to adjust their SCPs with the 4-s rhythm of presented tones by producing directed SCP changes only in certain inter-tone intervals. Specifically, they learned to simultaneously produce two EEG signals: 1) positive or negative SCP shift at vertex, and 2) SCP asymmetry between the right and the left central area. After one training session, 13 healthy participants were able to differentiate significantly between the negativity and the positivity conditions; this differentiation was achieved within less than 300 ms after the discriminative signal, i.e. much faster than in previous studies employing traditional SCP biofeedback technique. However, these participants did not produce a significant hemispheric asymmetry in the first session. In the second experiment, five subjects participated in prolonged training (6 to 17 sessions). Highly significant control of SCP asymmetry over the precentral cortex was attained in four out of five participants. Advantages and disadvantages of the new method as compared with the “classical” SCP biofeedback technique are discussed.
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