Identifying motor imagery (MI) electroencephalogram (EEG) is an important way to achieve brain–computer interface (BCI), but its applicability heavily dependent on the performance of feature extraction procedure. In this paper, a method based generalized maximum fuzzy membership difference entropy (GMFMDE) and discrete wavelet transform (DWT) was proposed for EEG signals. The influence differen...

Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted corrupt information contained in signals degrade performance of qualitative analysis clinical applications well EEG-based brain–computer interfaces (BCIs). The wavelet transform denoising are increasing day by due to its capability handling non-stationary ...

Despite achieving success in many domains, deep learning models remain mostly black boxes, especially electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial assessing trust and performance promotion EEG-related In this work, we explore use of representative interpretable to analyze behavior convolutional neural networks (CNN) EEG...

The purpose of this study is to verify whether machine learning using electroencephalogram (EEG) and electrocardiogram (ECG) as inputs improves accuracy. participants, 16 healthy adults, were given two stimulations: resting unpleasant stimuli. Their EEG was measured 180 s immediately after the beta band LF, HF, LF/HF ECG calculated. accuracy neural network then compared an EEG-only, ECG-only, c...

Background: Epilepsy is considered as one of the most important disorders in neurology. Temporal lobe epilepsy is a form of epilepsy including two main types of mesial and lateral (neocortex). Objectives: Determination and comparison of electroencephalogram (EEG) pattern in the ictal and interictal phases of mesial and lateral temporal lobe epilepsy. Materials and Methods: This cross-sectiona...

The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...

Over 30% epileptic patients are refractory to medication, who are amenable to neurosurgical treatment. Non-invasive brain imaging technologies including video-electroencephalogram (EEG), magnetic resonance imaging (MRI), and magnetoencephalography (MEG) are widely used in presurgical assessment of epileptic patients. This review mainly discussed the current development of clinical MEG imaging a...

Objective: In this research, a new approach termed as “evolutionary-based brain map” is presented as a diagnostic tool to classify schizophrenic and control subjects by distinguishing their electroencephalogram (EEG) features.Methods: Particle swarm optimization (PSO) is employed to find discriminative frequency bands from different EEG channels. By deploying the energy of those selected fr...

background: this paper proposes a new emotional stress assessment system using multi-modal bio-signals. electroencephalogram (eeg) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. methods: we design an efficient acquisition protocol to acquire the eeg signals in five channels (fp1, fp2, t3, t4 and pz) and peripheral signals such as blood volu...