AIMC Topic: Electroencephalography

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Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.

Scientific reports
High inter-subject variability and the non-stationary nature of EEG signals pose significant challenges for subject-independent Brain-Computer Interfaces (BCIs) leading to poor model generalization. Differences in neural activity patterns, electrode ...

CTSSP: A temporal-spectral-spatial joint optimization algorithm for motor imagery EEG decoding.

Journal of neural engineering
Objective.Motor imagery brain-computer interfaces hold significant promise for neurorehabilitation, yet their performance is often compromised by electroencephalography (EEG) non-stationarity, low signal-to-noise ratios, and severe cross-session vari...

Explainable AI for pain perception: subject-independent EEG decoding using DeepSHAP and CNNs.

Biomedical physics & engineering express
Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learni...

ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.

Biomedical physics & engineering express
End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and affective brain-computer interfaces (aBCIs). However, most existing methods overlook cross-frequency in...

Stress detection using the phase controlled Bi-channel adaptive features from the brain EEG signals.

Computers in biology and medicine
This work proposes a stress classification system from the electroencephalogram (EEG) signals collected from the stress subjects. The scheme extracts the phase-controlled Bi-channel adaptive features using a pair of EEG signals. The proposed adaptive...

Hybrid BCI-based instruction set for dual robotic arm control using EEG and eye movement signals.

Biomedical physics & engineering express
A brain-computer interface (BCI) establishes a pathway for information transmission between a human (or animal) and an external device. It can be used to control devices such as prosthetic limbs and robotic arms, which in turn assist, rehabilitate, a...

An Innovative Method for Refractory Epilepsy Diagnosis Based on Microstate Analysis and Graph Convolutional Network.

Journal of medical systems
This study systematically investigates the alterations in electroencephalogram (EEG) microstates in patients with refractory epilepsy(RE) across different seizure stages. A novel EEG microstate analysis framework is proposed to address the limitation...

GenEEG: Improving epileptic EEG detection through patient-adaptive latent diffusion and continual learning.

Computers in biology and medicine
Automated seizure detection systems face significant challenges due to the limited availability of clinical EEG data, a substantial class imbalance between seizure and non-seizure recordings, considerable variability among patients, and the issue of ...

Epileptic spasm recognition: EEG classification using time-frequency features and machine learning.

Biomedical engineering online
Epileptic spasm (ES), characterized by sudden muscle contractions and loss of consciousness, poses significant challenges in early diagnosis and treatment, especially in infants and young children. Despite advances in EEG-based seizure detection, the...

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.

eLife
Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing the processing ...