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Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
SPWVD-CNN for automated detection of schizophrenia patients using EEG signals
Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunctions,
hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis …
hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis …
Wavelet transforms for feature engineering in EEG data processing: An application on Schizophrenia
Abstract Applying Artificial Intelligence (AI) in the healthcare domain is getting benefitted day
by day with the advancement of approaches, one of them being Bio-Signal analysis. In Bio …
by day with the advancement of approaches, one of them being Bio-Signal analysis. In Bio …
Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal
Detection of mental disorders such as schizophrenia (SZ) through investigating brain
activities recorded via Electroencephalogram (EEG) signals is a promising field in …
activities recorded via Electroencephalogram (EEG) signals is a promising field in …
Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features
Schizophrenia is a mental disorder that causes adverse effects on the mental capacity of a
person, emotional inclinations, and quality of personal and social life. The official statistics …
person, emotional inclinations, and quality of personal and social life. The official statistics …
SchizoNET: a robust and accurate Margenau–Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals
Objective. Schizophrenia (SZ) is a severe chronic illness characterized by delusions,
cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking …
cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking …
[HTML][HTML] Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning
The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybrid-
BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to …
BCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to …
Automated Schizophrenia detection using local descriptors with EEG signals
Schizophrenia (SZ) is a severe mental disorder characterized by behavioral imbalance and
impaired cognitive ability. This paper proposes a local descriptors-based automated …
impaired cognitive ability. This paper proposes a local descriptors-based automated …