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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Veröffentlicht in:	Results in control and optimization Jg. 11; S. 100231
Hauptverfasser:	Mane, Swaymprabha Alias Megha, Shinde, Arundhati
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.06.2023 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
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