BACKGROUND Electroencephalography (EEG) is widely used to assess neurological prognosis in patients who are comatose after cardiac arrest, but its value is limited by varying definitions of pathological patterns and by inter-rater variability. The American Clinical Neurophysiology Society (ACNS) has recently proposed a standardized EEG-terminology for critical care to address these limitations....

The Commission of European Affairs of the International League Against Epilepsy published 'Appropriate Standards for Epilepsy Care Across Europe' which contained recommendations for the use of electroencephalography (EEG) in the diagnosis of epilepsy (Brodie et al. Epilepsia 1997; 38:1245). The need for a more specific basic document of EEG methodology was recognized and the Subcommission on Eu...

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two imaging techniques used to study the dynamical activity of the human brain. Although complementary, i.e. EEG has a high temporal resolution while fMRI provides precise volumic information, the simultaneous use of both techniques introduces large artefacts in the EEG recordings. These artefacts are consequences...

Electroencephalography (EEG) has been widely used in the research of stress detection recent years; yet, how to analyze an EEG is important issue for upgrading accuracy detection. This study aims collect table tennis players by a test and it with machine learning identify models optimal accuracy. The methods are collecting using Stroop color word mental arithmetic, extracting features data prep...

Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems. MI tasks performed by imagining doing a specific task and classifying through EEG signal processing. However, it is challenging to classify accurately. In this study, we propose LSTM-based classification framework enhance accuracy of four-class signals. To obtain time-varying data signals, sliding window tec...

The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based is one of the most widely implemented methods, but it limited by spectral leakage caused finite length basic function that impacts time frequency resolutions. This paper proposes a new time-freque...

Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting patient’s brain and limbs, these contribute to restoration of motor functions, ultimately leading a significant improvement overall quality life. However, current BCI primarily relies on Electroencephalogram (EEG...

Abstract Electroencephalography (EEG) is the most commonly used method in diagnosis of epilepsy diseases. In order to identify EEG signals more effectively, an automatic identification based on improved empirical wavelet transform (EWT) proposed. Firstly, view difficulty spectral division signal processing by transform, improvement measure proposed, that is, average difference spectrum obtained...

Neuroplasticity is the ability of brain to change structurally and functionally in compensation for changes related age or disease. In elderly people, most common neuroplasticity problem mild cognitive impairment (MCI). MCI a syndrome defined as decrease function that not appropriate person's educational level. One way minimize progress deterioration by doing physical exercise, such walking. th...

BACKGROUND We investigate the potential usability of a novel in-the-ear electroencephalography recording device for sleep staging. METHODS In one healthy subject we compare simultaneous earelectroencephalography to standard scalp EEG visually and using power spectrograms. Hypnograms independently derived from the records are compared. RESULTS We find that alpha activity, K complexes, sleep ...