AIMC Topic: Electroencephalography

Clear Filters Showing 71 to 80 of 2295 articles

A deep learning-enriched framework for analyzing brain functional connectivity.

Scientific reports
Cognitive and motor functions require a coordinated communication among brain regions, with the directionality of interactions playing a key role, as the brain relies on functional asymmetries of reciprocal connections. Predictive models based on dee...

Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury.

Scientific reports
Neurogenic bladder (NB) dysfunction in individuals with complete spinal cord injury (SCI) is a condition that significantly affects quality of life. Despite the prevalence of interventions, there is a substantial gap in effective treatments for this ...

Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces.

Scientific reports
Motor imagery-based Brain-Computer Interfaces (BCIs) hold transformative potential for individuals with severe motor impairments, yet their clinical deployment remains constrained by the inherent complexity of electroencephalographic (EEG) signal dec...

Creative experiences and brain clocks.

Nature communications
Creative experiences may enhance brain health, yet metrics and mechanisms remain elusive. We characterized brain health using brain clocks, which capture deviations from chronological age (i.e., accelerated or delayed brain aging). We combined M/EEG ...

Neural oscillation mechanisms of repetitive subconcussive impacts: a network study of microstate-specific cross-frequency coupling.

The journal of headache and pain
BACKGROUND: Repetitive subconcussive impacts are linked to headache pathophysiology, yet the role of electroencephalography (EEG) microstates and cross-frequency coupling in repetitive subconcussive (SC) neural alterations remains unclear. This study...

TFDISNet: Temporal-frequency domain-invariant and domain-specific feature learning network for enhanced auditory attention decoding from EEG signals.

Biomedical physics & engineering express
Auditory Attention Decoding (AAD) from Electroencephalogram (EEG) signals presents a significant challenge in brain-computer interface (BCI) research due to the intricate nature of neural patterns. Existing approaches often fail to effectively integr...

Machine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.

Biomedical physics & engineering express
Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to ...

Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform.

Scientific reports
Excessive electrical activity in the brain causes epileptic seizures which can be detected through Electroencephalogram (EEG) signals. The research aims to identify epileptic seizures using EEG records automatically. Firstly, EEG bands are extracted ...

EEG Microstates Signatures of rTMS Response Over the lDLPFC: A Band-Specific Analysis.

Brain topography
Transcranial Magnetic Stimulation (TMS), particularly Theta Burst Stimulation (TBS), is a non-invasive, non-convulsive neuromodulation technique that induces clinically relevant network modulations with long-term effects. Two TBS protocols- continuou...

Introduction of sub-band augmentation with machine learning to develop an insomnia classification model using single-channel EEG signals.

Physiological measurement
. Biological signals can be used to record sleep activities and can be used to identify sleep disorders. Insomnia is a sleep disorder that can be detected using supervised learning models developed using biological signal analysis. The baseline insom...