EEG Signal Processing for Biomedical Applications
This reprint focuses on electroencephalography (EEG) signal processing in biomedical engineering applications. EEG signals are used widely in clinical and research settings to provide cognitive and emotional state information. In addition to capturing complex neural patterns at high speeds, EEG sign...
Format: | eBook |
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Language: | English |
Published: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
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Subjects: | |
Online Access: | Open Access: DOAB: description of the publication Open Access: DOAB, download the publication |
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520 | |a This reprint focuses on electroencephalography (EEG) signal processing in biomedical engineering applications. EEG signals are used widely in clinical and research settings to provide cognitive and emotional state information. In addition to capturing complex neural patterns at high speeds, EEG signals are a reliable and non-invasive way of measuring the electrical activity in the brain. By examining various novel analysis and signal processing methods, this collection of papers provides a better understanding of cognitive states and brain activity. | ||
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546 | |a English | ||
650 | 7 | |a History of engineering and technology |2 bicssc | |
650 | 7 | |a Technology: general issues |2 bicssc | |
653 | |a acupuncture | ||
653 | |a adaptive threshold | ||
653 | |a ALS | ||
653 | |a attractor | ||
653 | |a BCI | ||
653 | |a brain activity | ||
653 | |a canonical correlation analysis (CCA) | ||
653 | |a cerebral cortex stimulation | ||
653 | |a classification | ||
653 | |a classifier | ||
653 | |a coherence | ||
653 | |a complex Pearson correlation coefficients | ||
653 | |a conductive material | ||
653 | |a connectivity | ||
653 | |a connectivity network | ||
653 | |a cross-participant | ||
653 | |a cross-task | ||
653 | |a data analysis | ||
653 | |a decoding | ||
653 | |a deep learning | ||
653 | |a dimensionality | ||
653 | |a e-textile | ||
653 | |a EEG | ||
653 | |a electroencephalogram | ||
653 | |a electroencephalogram (EEG) | ||
653 | |a electroencephalography | ||
653 | |a electromagnetic influence | ||
653 | |a epilepsy | ||
653 | |a ERP | ||
653 | |a feature extraction | ||
653 | |a feature selection | ||
653 | |a features | ||
653 | |a fractal dimension | ||
653 | |a frequency-specific | ||
653 | |a functional connectivity | ||
653 | |a functional connectivity network | ||
653 | |a functional near-infrared spectroscopy (fNIRS) | ||
653 | |a head phantom | ||
653 | |a latent variables | ||
653 | |a machine learning | ||
653 | |a machine Learning | ||
653 | |a mental fatigue | ||
653 | |a mental stress | ||
653 | |a motion artifact | ||
653 | |a multilayer network | ||
653 | |a n/a | ||
653 | |a neural | ||
653 | |a neural subspace | ||
653 | |a neuropathic pain | ||
653 | |a neurostimulation | ||
653 | |a otsu | ||
653 | |a phase connectivity | ||
653 | |a phase locking value | ||
653 | |a review | ||
653 | |a ridge | ||
653 | |a segmentation | ||
653 | |a seizure detection | ||
653 | |a speech discrimination | ||
653 | |a spinal cord injury | ||
653 | |a sustained attention | ||
653 | |a task-generic | ||
653 | |a time-frequency features | ||
653 | |a transcranial magnetic stimulation | ||
653 | |a transfer learning | ||
653 | |a traumatic brain injury | ||
653 | |a vigilance decrement | ||
653 | |a virtual reality | ||
653 | |a wavelet packet decomposition (WPD) | ||
653 | |a wavelet spectrum | ||
653 | |a weighted phase lag index | ||
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856 | 4 | 0 | |u https://directory.doabooks.org/handle/20.500.12854/98745 |7 0 |z Open Access: DOAB: description of the publication |
856 | 4 | 0 | |u https://mdpi.com/books/pdfview/book/6752 |7 0 |z Open Access: DOAB, download the publication |