StressNet: Hybrid model of LSTM and CNN for stress detection from electroencephalogram signal (EEG)

Everyday tasks can cause stress, which can lead to serious medical conditions, such as depression. EEG signal processing can assist medical professionals in managing the emotional state of patients. To detect stress states in EEG signals, we propose a new architecture, StressNet, which is a combinat...

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Vydané v:	Results in control and optimization Ročník 11; s. 100231
Hlavní autori:	Mane, Swaymprabha Alias Megha, Shinde, Arundhati
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.06.2023 Elsevier
Predmet:	Accuracy Azimuthal projection Brain–computer interfaces (BCIs) Convolutional neural network (CNN) Deep learning Electroencephalography (EEG) Human stress detection Long short-term memory (LSTM) Stress Deep learning Human stress detection Accuracy Electroencephalography (EEG) Brain–computer interfaces (BCIs) Convolutional neural network (CNN) Long short-term memory (LSTM) Stress Azimuthal projection
ISSN:	2666-7207, 2666-7207
On-line prístup:	Získať plný text
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