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 …
Cognitive load detection using circulant singular spectrum analysis and Binary Harris Hawks Optimization based feature selection
J Yedukondalu, LD Sharma - Biomedical Signal Processing and Control, 2023 - Elsevier
Cognitive load detection during the mental assignment of neural activity is necessary
because it helps to understand the brain's response to stimuli. An electroencephalogram …
because it helps to understand the brain's response to stimuli. An electroencephalogram …
A systematic review of machine learning models in mental health analysis based on multi-channel multi-modal biometric signals
J Ehiabhi, H Wang - BioMedInformatics, 2023 - mdpi.com
With the increase in biosensors and data collection devices in the healthcare industry,
artificial intelligence and machine learning have attracted much attention in recent years. In …
artificial intelligence and machine learning have attracted much attention in recent years. In …
A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment
Abstract In Internet of Medical Things (IoMT) environment, feature selection is an efficient
way of identifying the most discriminant health-related features from the original feature-set …
way of identifying the most discriminant health-related features from the original feature-set …
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 …
A multiwavelet-based time-varying model identification approach for time–frequency analysis of EEG signals
Y Li, ML Luo, K Li - Neurocomputing, 2016 - Elsevier
An efficient multiwavelet-based time-varying modeling scheme is proposed for time–
frequency analysis (TFA) of electroencephalogram (EEG) data. In the new multiwavelet …
frequency analysis (TFA) of electroencephalogram (EEG) data. In the new multiwavelet …
Phase space reconstruction for improving the classification of single trial EEG
Electroencephalogram (EEG) signals are nonlinear time series, which are generally very
noisy, nonstationary, and contaminated with artifacts that can deteriorate classification …
noisy, nonstationary, and contaminated with artifacts that can deteriorate classification …
Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface
Two main issues for event-related potential (ERP) classification in brain–computer interface
(BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may …
(BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may …
Extracting features from phase space of EEG signals in brain–computer interfaces
Y Fang, M Chen, X Zheng - Neurocomputing, 2015 - Elsevier
Conventional feature extraction methods based on autoregressive and amplitude–frequency
analysis assume stationarity in the Electroencephalogram signal along short time intervals …
analysis assume stationarity in the Electroencephalogram signal along short time intervals …
Exploring dimensionality reduction of EEG features in motor imagery task classification
PJ García-Laencina, G Rodríguez-Bermudez… - Expert Systems with …, 2014 - Elsevier
Abstract A Brain-Computer Interface (BCI) system based on motor imagery (MI) identifies
patterns of electrical brain activity to predict the user intention while certain movement …
patterns of electrical brain activity to predict the user intention while certain movement …