Lightweight Machine Learning with Brain Signals

Electroencephalography(EEG) signals are gaining popularity in Brain-Computer Interface(BCI) systems and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks(CNNs) or graph neural networks(GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account; 3) navigating and training deep learning model with the most critical EEG channels; 4) detecting most similar EEG segments with target subject to reduce the cost of computation as well as inter-subject variability. Specifically, we construct a task-adaptive graph representation of brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. Lastly, contributory EEG segments are detected by several similarity estimation metrics, we then evaluate and train our proposed framework upon detected EEG segments to compare the performance of different metrics in EEG BCI tasks. We empirically show that our proposed approach, SIFT-EEG, outperforms state-of-the-art, with around 4% and 7% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data. Moreover, the best-performed metric can achieve a high level of accuracy with less than 9% training data, suggesting a possible shift in direction for future works other than simply scaling up the model

Benzodiazepine-Receptor Agonist Utilization in Outpatients with Anxiety Disorder: A Retrospective Study Based on Electronic Healthcare Data from a Large General Tertiary Hospital

Benzodiazepine-receptor agonists (BZRAs), including benzodiazepines (BZDs) and drugs related to BZDs (Z-drugs), are commonly used for anxiety, but often have side effects. We retrospectively investigated the utilization and prescription characteristics of BZRAs for patients with anxiety disorders in a large tertiary care general hospital between 2018 and 2021, based on electronic healthcare records. We also examined the pattern of simultaneous consumption of multiple BZRA drugs, and the diseases coexisting with anxiety that are associated with this. The numbers of patients and BZRA prescriptions increased over the 4 years. Moreover, 7195 prescriptions from 694 patients contained two or more BZRAs, of which 78.08% contained both BZDs and Z-drugs, 19.78% contained multiple BZDs, and 2.14% contained multiple Z-drugs. For anxiety patients with concomitant Alzheimer’s disease or Parkinson’s disease, and dyslipidemia, they were more likely to consume multiple BZRAs simultaneously, whereas patients with concomitant insomnia, depression, hypertension, diabetes, or tumors were less likely to consume multiple BZRAs (all p < 0.05). Furthermore, older patients who consume multiple BZRAs simultaneously may have higher probabilities of long-term drug use. Better interventions supporting standardized BZD utilization may be needed to minimize the side effects of inappropriate BZRA administration