AIMC Topic: Electroencephalography
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CNNs improve decoding of selective attention to speech in cochlear implant users.
Journal of neural engineering
Jun 10, 2025
. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory...
Theta-Beta/Gamma Coupling Identifies Bothersome Tinnitus Induced by Thalamocortical Dysrhythmia.
Brain and behavior
Jun 1, 2025
BACKGROUND: The phantom sound of tinnitus can be an extremely debilitating condition. The thalamocortical dysrhythmia (TCD) hypothesis is a feature of subjective tinnitus; however, its consistency in characterizing different types of tinnitus remains...
CRT: A Convolutional Recurrent Transformer for Automatic Sleep State Detection.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Sleep is a crucial period of rest necessary for optimal cognitive function, psychological well-being, and execution of everyday tasks. In the field of sleep healthcare, the primary objective is to identify and classify the various sleep states. Imple...
EEG-ConvoBLSTM: A novel hybrid model for efficient EEG signal classification.
The Review of scientific instruments
Jun 1, 2025
Electroencephalogram (EEG) signals pose a challenge to emotion recognition (ER) tasks due to their complexity and individual differences. Conventional machine learning methods usually rely on handcrafted feature extraction and perform poorly in cross...
An explainable machine learning framework for predicting driving states using electroencephalogram.
Medical engineering & physics
Jun 1, 2025
OBJECTIVES: Understanding drivers' cognitive load is essential for enhancing road safety, as cognitive demands fluctuate across different driving scenarios, potentially impacting performance, and safety, particularly for drivers with neurological dis...
Exploring the diagnostic potential of EEG theta power and interhemispheric correlation of temporal lobe activities in Alzheimer's Disease through random forest analysis.
Computers in biology and medicine
Jun 1, 2025
BACKGROUND: Considering the prevalence of Alzheimer's Disease (AD) among the aging population and the limited means of treatment, early detection emerges as a crucial focus area whereas electroencephalography (EEG) provides a promising diagnostic too...
Spatio-temporal CNN-BiLSTM dynamic approach to emotion recognition based on EEG signal.
Computers in biology and medicine
Jun 1, 2025
In this paper, a hybrid CNN-BiLSTM model for EEG-based emotion detection system is presented. The proposed technique is developed by extracting features using Power Spectral Density (PSD) signal. The proposed approach is carried out by combining CNN ...
Unsupervised detection of sub-sequence anomalies in epilepsy EEG.
Computers in biology and medicine
Jun 1, 2025
Seizures in electroencephalogram (EEG) data constitute a special case of sub-sequence anomalies in multivariate data with numerous challenges. These challenges include the irregular patterns exhibited even by the same individual, making seizures diff...
Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.
Computers in biology and medicine
Jun 1, 2025
The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting in inconsistent and imprecise evaluations. To address this issue and facilitate early intervention, t...
A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging.
Computer methods and programs in biomedicine
Jun 1, 2025
BACKGROUND AND OBJECTIVES: Electrophysiological source imaging (ESI) is a challenging technique for noninvasively measuring brain activity, which involves solving a highly ill-posed inverse problem. Traditional methods attempt to address this challen...
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