EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression.

By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for classifying EEG signals related to visual objects, employing a combination of an LSTM network and nonlinear interval type-2 fuzzy regression (NIT2FR). Here, ResNet is utilized for feature extraction from images, the LSTM network for feature extraction from EEG signals, and NIT2FR for mapping image features to EEG signal features.

Assessing the Effects of Alzheimer Disease on EEG Signals Using the Entropy Measure: A Meta-analysis.

Introduction: Alzheimer disease (AD) is the most prevalent neurodegenerative disorder and a type of dementia. About 80% of dementia in older adults is due to AD. According to multiple research articles, AD is associated with several changes in EEG signals, such as slow rhythms, reduction in complexity and functional associations, and disordered functional communication between different brain areas.

Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

Background And Objective: We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems.

Methods: The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body.