Quantitative electroencephalographic biomarker of pharmacological treatment response in patients with anxiety disorder: a retrospective study

Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM

In humans, epilepsy is diagnosed through electroencephalography (EEG) signals. Epileptic seizures (ESs) arise due to anxiety. The detection of anxiety-based seizures is challenging for radiologists, and there is a limited availability of anxiety-based EEG signals. Data augmentation methods are required to increase the number of novel samples. An epileptic seizure arises due to anxiety, which manifests as variations in EEG signal patterns consisting of changes in the size and shape of the signal. In this study, anxiety EEG signals were synthesized by applying data augmentation methods such as random data augmentation (RDA) to existing epileptic seizure signals from the Bonn EEG dataset. The data-augmented anxiety seizure signals were processed using three algorithms-(i) fuzzy C-means-particle swarm optimization-long short-term memory (FCM-PS-LSTM), (ii) particle swarm optimization-long short-term memory (PS-LSTM), and (iii) parrot optimization LSTM (PO-LSTM)-for the detection of anxiety ESs via EEG signals. The predicted accuracies of detecting ESs through EEG signals using the proposed algorithms-namely, (i) FCM-PS-LSTM, (ii) PS-LSTM, and (iii) PO-LSTM-were about 98%, 98.5%, and 96%, respectively.

Efficacy of Transcranial Direct Current Stimulation for Controlling of Food Craving in Subjects With Overweight or Obesity: A Pilot Study

OBJECTIVE

We aim to explore the effects of transcranial direct current stimulation (tDCS) on food craving improvement and changes in brain function associated with craving by conducting a total of 10 sessions of tDCS over a period of 2 weeks on overweight and obese subjects.

METHODS

A total of 15 patients who were overweight or obese (body mass index [BMI] ≥23 kg/m2) were included. Weight, BMI, neuropsychological variables, and food craving-related variables were assessed. We measured absolute and relative power in 19 channels and analyzed quantitative electroencephalography (qEEG) according to the following frequency ranges: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-25 Hz), high beta (25-30 Hz), and gamma (30-80 Hz).

RESULTS

After the application of tDCS, there was no significant reduction observed in weight and BMI. However, all measures related to food and eating showed a decrease in the intensity of cravings, and there was also a significant reduction in depression, anxiety, and perceived stress. In qEEG analysis, an increase in theta waves was observed in the left frontal area (F7 and F3), an increase in alpha waves in the right parietal area (P4), and a decrease in beta waves in the frontal area (FP2) and occipital area (O1).

CONCLUSION

This study confirmed the beneficial effects of tDCS on food craving regulation in overweight or obese individuals and observed improved scores in psychological factors such as depression and anxiety. Furthermore, neurophysiological changes related to food craving were observed using qEEG.