An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks.

This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision.

A low-cost and open-hardware portable 3-electrode sleep monitoring device.

To continue sleep research activities during the lockdown resulting from the COVID-19 pandemic, experiments that were previously conducted in laboratories were shifted to the homes of volunteers. Furthermore, for extensive data collection, it is necessary to use a large number of portable devices. Hence, to achieve these objectives, we developed a low-cost and open-source portable monitor (PM) device capable of acquiring electroencephalographic (EEG) signals using the popular ESP32 microcontroller.

Structural differences between non-lucid dreams, lucid dreams and out-of-body experience reports assessed by graph analysis.

Dreaming is a complex phenomenon that occurs during sleep, involving various conscious dream experiences. Lucid dreams (LDs) involve heightened awareness within the dream environment, while out-of-body experiences (OBEs) involve the sensation of being outside one's physical body. OBEs occur during sleep paralysis (SP), where voluntary movements are inhibited during sleep/wake transitions while remaining aware of the surroundings.

One-week sleep hygiene education improves episodic memory in young but not in older adults during social isolation.

Memory formation is a dynamic process that comprises different phases, such as encoding, consolidation and retrieval. It could be altered by several factors such as sleep quality, anxiety, and depression levels. In the last years, due to COVID-19 pandemic, there was a reduction in sleep quality, an increase in anxiety and depressive symptoms as well as an impairment in emotional episodic memory encoding, especially in young adults.

Histogram of Gradient Orientations of Signal Plots Applied to P300 Detection.

The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals.

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces.

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices.