Toward Domain-Free Transformer for Generalized EEG Pre-Training.

Electroencephalography (EEG) signals are the brain signals acquired using the non-invasive approach. Owing to the high portability and practicality, EEG signals have found extensive application in monitoring human physiological states across various domains. In recent years, deep learning methodologies have been explored to decode the intricate information embedded in EEG signals.

Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities.

A new kind of sequence-to-sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG-based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery-based brain-computer interface (BCI) have been limited to classifying EEG signals within one type of imagery.

Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals.

Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface has been a promising hope for reconstructing the neural signals of speech production. However, studies in the EEG-based imagined speech domain still have some limitations due to high variability in spatial and temporal information and low signal-to-noise ratio.

Noise-induced hearing loss in zebrafish model: Characterization of tonotopy and sex-based differences.

Hearing loss caused by frequent and persistent exposure to loud noise is one of the most common diseases in modern society. Many studies have demonstrated the characteristics of noise-induced hearing loss in human and non-human vertebrate models, including frequency-specific noise-induced hearing loss and sex-biased differences. Zebrafish (Danio rerio) is a useful hearing research model because its lateral line is easy to access and because of its detailed perception of sound.

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions.

Background: Non-invasive brain-computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control.

Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals.

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot's mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.

Engineering Titanium Dioxide Nanostructures for Enhanced Lithium-Ion Storage.

Various kinds of nanostructured materials have been extensively investigated as lithium ion battery electrode materials derived from their numerous advantageous features including enhanced energy and power density and cyclability. However, little is known about the microscopic origin of how nanostructures can enhance lithium storage performance. Herein, we identify the microscopic origin of enhanced lithium storage in anatase TiO nanostructure and report a reversible and stable route to achieve enhanced lithium storage capacity in anatase TiO.