Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques
This paper describes a discrete wavelet transform-based feature extraction scheme for the
classification of EEG signals. In this scheme, the discrete wavelet transform is applied on …
classification of EEG signals. In this scheme, the discrete wavelet transform is applied on …
Empirical mode decomposition and its extensions applied to EEG analysis: a review
Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–
frequency analysis, yielding components from which local amplitude, phase, and frequency …
frequency analysis, yielding components from which local amplitude, phase, and frequency …
The sample size matters: to what extent the participant reduction affects the outcomes of a neuroscientific research. A case-study in neuromarketing field
The sample size is a crucial concern in scientific research and even more in behavioural
neurosciences, where besides the best practice it is not always possible to reach large …
neurosciences, where besides the best practice it is not always possible to reach large …
An EEG-based cognitive load assessment in multimedia learning using feature extraction and partial directed coherence
Assessing cognitive load during a learning phase is important, as it assists to understand the
complexity of the learning task. It can help in balancing the cognitive load of postlearning …
complexity of the learning task. It can help in balancing the cognitive load of postlearning …
Analysis of gamma-band activity from human EEG using empirical mode decomposition
C Amo, L De Santiago, R Barea, A López-Dorado… - Sensors, 2017 - mdpi.com
The purpose of this paper is to determine whether gamma-band activity detection is
improved when a filter, based on empirical mode decomposition (EMD), is added to the pre …
improved when a filter, based on empirical mode decomposition (EMD), is added to the pre …
One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram
Cognitive/mental task classification using single/limited channel (s) electroencephalogram
(EEG) signals in real-time play an important role in designing portable brain-computer …
(EEG) signals in real-time play an important role in designing portable brain-computer …
State-of-the-art mental tasks classification based on electroencephalograms: a review
Electroencephalograms (EEGs) play an important role in analyzing different mental tasks
and neurological disorders. Hence, they are a critical component for designing various …
and neurological disorders. Hence, they are a critical component for designing various …
Graph signal processing based cross-subject mental task classification using multi-channel EEG signals
Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in
designing various brain-computer interface (BCI) applications. Most of the current …
designing various brain-computer interface (BCI) applications. Most of the current …
Automatic focal and non-focal EEG detection using entropy-based features from flexible analytic wavelet transform
Y You, W Chen, M Li, T Zhang, Y Jiang… - … Signal Processing and …, 2020 - Elsevier
Surgical treatment is one of the most important methods to cure or control drug-resistant
epilepsy, and preoperative localization of epileptic lesions plays an important role in the …
epilepsy, and preoperative localization of epileptic lesions plays an important role in the …
Analysis of EEG spectrum bands using power spectral density for pleasure and displeasure state
A Ameera, A Saidatul, Z Ibrahim - IOP conference series …, 2019 - iopscience.iop.org
The technology of reading human mental states is a leading innovation in the biomedical
engineering field. EEG signal processing is going to help us to explore the uniqueness of …
engineering field. EEG signal processing is going to help us to explore the uniqueness of …