Schlagwörter

• convolutional neural nets, diseases, electroencephalography, emotion recognition, independent component analysis, medical signal processing, neurophysiology, signal classification, unsupervised learning, acute stages, Alzheimer's disease, high-fidelity emotion recognition systems, EEG data, Signal-to-noise ratio, subject-to-subject variability, integrated framework, semigeneric emotion detection, convolutional neural network, EEG-based emotion recognition, testing data, publicly available repositories, CNN classifier, transfer learning approach, subject-independent, unsupervised transfer learning, emotion classification, EEG signal processing, neurological disorders, Amyotrophic Lateral Sclerosis, ALS, standard international affective picture system, Electroencephalography, Training, Task analysis, Manuals, Unsupervised learning, Feature extraction