Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics.

Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve data efficiency. In addition, contrastive learning has recently been shown to hold great promise in learning effective representations without human supervision, which has the potential to improve the electroencephalogram-based recognition performance with limited labeled data.

EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network.

In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since EEG signals vary significantly among different subjects and recording sessions. Many efforts have been made to use deep learning methods for mental state recognition from EEG signals. However, existing work mostly treats deep learning models as black-box classifiers, while what have been learned by the models and to which extent they are affected by the noise in EEG data are still underexplored.

A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG.

Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects.

Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness.

Situation awareness (SA) has received much attention in recent years because of its importance for operators of dynamic systems. Electroencephalography (EEG) can be used to measure mental states of operators related to SA. However, cross-subject EEG-based SA recognition is a critical challenge, as data distributions of different subjects vary significantly.

Large-Scale Automated Sleep Staging.

Study Objectives: Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain.

Learning Polychronous Neuronal Groups Using Joint Weight-Delay Spike-Timing-Dependent Plasticity.

Polychronous neuronal group (PNG), a type of cell assembly, is one of the putative mechanisms for neural information representation. According to the reader-centric definition, some readout neurons can become selective to the information represented by polychronous neuronal groups under ongoing activity. Here, in computational models, we show that the frequently activated polychronous neuronal groups can be learned by readout neurons with joint weight-delay spike-timing-dependent plasticity.

Real-time mental arithmetic task recognition from EEG signals.

Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals.

EEG-based "serious" games and monitoring tools for pain management.

EEG-based "serious games" for medical applications attracted recently more attention from the research community and industry as wireless EEG reading devices became easily available on the market. EEG-based technology has been applied in anesthesiology, psychology, etc. In this paper, we proposed and developed EEG-based "serious" games and doctor's monitoring tools that could be used for pain management.

Human electroencephalograms seen as fractal time series: mathematical analysis and visualization.

The paper presents a novel technique of nonlinear spectral analysis, which has been used for processing encephalograms of humans. This technique is based on the concept of generalized entropy of a given probability distribution, known as the Rényi entropy that allows defining the set of generalized fractal dimensions of encephalogram (EEG) and determining fractal spectra of encephalographic signals. Unlike the Fourier spectra, the spectra of fractal dimensions contain information of both frequency and amplitude characteristics of EEG and can be used together with well-accepted techniques of EEG analysis as an enhancement of the latter.