Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment.

Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.

Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.