Signal Processing for Brain–Computer Interfaces
With the astounding ability to capture a wealth of brain signals, Brain–Computer Interfaces (BCIs) have the potential to revolutionize humans’ quality of life by processing these brain signals for controlling external devices. Being an emerging and innovative field, BCIs offer numerous applications...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Online pristup: | ONIX_20240514_9783725805204_376 |
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| collection | Directory of Open Access Books |
| description | With the astounding ability to capture a wealth of brain signals, Brain–Computer Interfaces (BCIs) have the potential to revolutionize humans’ quality of life by processing these brain signals for controlling external devices. Being an emerging and innovative field, BCIs offer numerous applications in various fields of life, including robotics, education, prosthetics, security and communication technologies. Processing neuro-physiological signals, a major component of BCIs, involves further procedures of (1) noise removal, (2) feature extraction and (3) classification. Pre-processed signals are subject to various noises, including power line noises, physiological noises, motion artifacts and interference noises. These noises can affect the efficiency of the entire BCI procedure. For this reason, noise removal algorithms are utilized for noise removal or reduction. Next, the process of feature extraction begins, in which algorithms are used to acquire relevant task-based features. This phase acquires data based on spectral, spatial and temporal domains. The last step for signal processing is classification, whereby the acquired and processed features are converted into viable commands, which ultimately control external devices. This reprint focuses particularly on these three signal-processing techniques. |
| format | Online |
| id | doab-20.500.12854ir-137780 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1377802024-05-14T14:26:07Z Signal Processing for Brain–Computer Interfaces Naseer, Noman Niazi, Imran Khan Santosa, Hendrik functional near-infrared spectroscopy (fNIRS) finger-tapping classification motor cortex machine learning artificial neural network (ANN) upper-limb prosthesis transhumeral amputee error-related potentials brain-computer interface cerebral palsy amputation stroke neurorehabilitation artificial neural network functional near-infrared spectroscopy convolutional neural network long short-term memory BCI fNIRS SRC channel selection unmanned aerial vehicle spectroscopy brain–computer interface application mathematical modelling semiconductor laser schizophrenia obsessive compulsive disorder migraine Stroop test EEG functional NIRS multimodal neuroimaging concurrent recording integrated analysis brain–computer interface (BCI) electroencephalography (EEG) emotion classification convolutional neural network (ConvNet) smart home phone control event-related potentials P300 active BCI mental state modulation features n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics With the astounding ability to capture a wealth of brain signals, Brain–Computer Interfaces (BCIs) have the potential to revolutionize humans’ quality of life by processing these brain signals for controlling external devices. Being an emerging and innovative field, BCIs offer numerous applications in various fields of life, including robotics, education, prosthetics, security and communication technologies. Processing neuro-physiological signals, a major component of BCIs, involves further procedures of (1) noise removal, (2) feature extraction and (3) classification. Pre-processed signals are subject to various noises, including power line noises, physiological noises, motion artifacts and interference noises. These noises can affect the efficiency of the entire BCI procedure. For this reason, noise removal algorithms are utilized for noise removal or reduction. Next, the process of feature extraction begins, in which algorithms are used to acquire relevant task-based features. This phase acquires data based on spectral, spatial and temporal domains. The last step for signal processing is classification, whereby the acquired and processed features are converted into viable commands, which ultimately control external devices. This reprint focuses particularly on these three signal-processing techniques. 2024-05-14T14:26:02Z 2024-05-14T14:26:02Z 2024 book ONIX_20240514_9783725805204_376 9783725805204 9783725805198 https://directory.doabooks.org/handle/20.500.12854/137780 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9013 https://mdpi.com/books/pdfview/book/9013 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0519-8 10.3390/books978-3-7258-0519-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725805204 9783725805198 202 open access |
| spellingShingle | functional near-infrared spectroscopy (fNIRS) finger-tapping classification motor cortex machine learning artificial neural network (ANN) upper-limb prosthesis transhumeral amputee error-related potentials brain-computer interface cerebral palsy amputation stroke neurorehabilitation artificial neural network functional near-infrared spectroscopy convolutional neural network long short-term memory BCI fNIRS SRC channel selection unmanned aerial vehicle spectroscopy brain–computer interface application mathematical modelling semiconductor laser schizophrenia obsessive compulsive disorder migraine Stroop test EEG functional NIRS multimodal neuroimaging concurrent recording integrated analysis brain–computer interface (BCI) electroencephalography (EEG) emotion classification convolutional neural network (ConvNet) smart home phone control event-related potentials P300 active BCI mental state modulation features n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics Signal Processing for Brain–Computer Interfaces |
| title | Signal Processing for Brain–Computer Interfaces |
| title_full | Signal Processing for Brain–Computer Interfaces |
| title_fullStr | Signal Processing for Brain–Computer Interfaces |
| title_full_unstemmed | Signal Processing for Brain–Computer Interfaces |
| title_short | Signal Processing for Brain–Computer Interfaces |
| title_sort | signal processing for brain computer interfaces |
| topic | functional near-infrared spectroscopy (fNIRS) finger-tapping classification motor cortex machine learning artificial neural network (ANN) upper-limb prosthesis transhumeral amputee error-related potentials brain-computer interface cerebral palsy amputation stroke neurorehabilitation artificial neural network functional near-infrared spectroscopy convolutional neural network long short-term memory BCI fNIRS SRC channel selection unmanned aerial vehicle spectroscopy brain–computer interface application mathematical modelling semiconductor laser schizophrenia obsessive compulsive disorder migraine Stroop test EEG functional NIRS multimodal neuroimaging concurrent recording integrated analysis brain–computer interface (BCI) electroencephalography (EEG) emotion classification convolutional neural network (ConvNet) smart home phone control event-related potentials P300 active BCI mental state modulation features n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics |
| topic_facet | functional near-infrared spectroscopy (fNIRS) finger-tapping classification motor cortex machine learning artificial neural network (ANN) upper-limb prosthesis transhumeral amputee error-related potentials brain-computer interface cerebral palsy amputation stroke neurorehabilitation artificial neural network functional near-infrared spectroscopy convolutional neural network long short-term memory BCI fNIRS SRC channel selection unmanned aerial vehicle spectroscopy brain–computer interface application mathematical modelling semiconductor laser schizophrenia obsessive compulsive disorder migraine Stroop test EEG functional NIRS multimodal neuroimaging concurrent recording integrated analysis brain–computer interface (BCI) electroencephalography (EEG) emotion classification convolutional neural network (ConvNet) smart home phone control event-related potentials P300 active BCI mental state modulation features n/a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics |
| url | ONIX_20240514_9783725805204_376 |