- Accurate division, 84
- Activity map, 240–241
- Alzheimer, 77–79, 82, 83, 85, 87–88, 92
- Anemia, 81
- Applications of BCI,
- communication, 14
- detection and diagnosis, 15
- education, 15
- gaming and entertainment, 17
- healthcare, 14
- marketing and advertising, 16
- movement control and locomotion, 14
- prevention, 15
- rehabilitation, 15
- security and authentication, 16
- smart environment, 16
- Artificial intelligence, 121
- Artificial neural networks, 117
- Auditory attention detection (AAD), 91
- Augmented reality (AR), 102
- AutoEncoder (SAAE), 40
- Band power, 240–241
- Bayesian networks, 36
- BCI, 180
- BCI application, 77–82, 84–86, 89–91, 93
- BCI structure, 280
- BCI technology, 3–5
- Better image availability,
- big data rfMRI challenges, 133–134
- large data analysis in neuroimaging, 131–132
- large rfMRI data software packages, 134–136
- Bimodal deep AutoEncoder (BDAE), 40
- BI-SNN technique, 85
- Blood pressure, 81, 83, 92
- Blood sugar, 80, 83, 92
- Brain connectivity,
- anatomical connectivity, 129–130
- functional connectivity, 130
- Brain signals, 180
- Brain surgeries, 173
- Brain–computer interface (BCI), 25–29, 77, 79, 83–84, 232–233
- Brain-inspired neural network, 85
- Brain-machine interface (BMI), 102
- Brain-to-brain (B2B) communication systems, 89
- Cardiovascular, 92
- Cell dysfunction, 84
- Challenges faced during implementation of BCI,
- ethical and socioeconomic challenges, 20–21
- psychophysiological and neurological challenges, 20
-
technical challenges, 18–20
- usability challenges, 17–18
- Classification, 11–12
- architecture of the DL model, 220–221
- control flow overview, 223
- control system, 223
- deep learning (DL) model pipeline, 219–220
- deployment of the DL model, 221–223
- output metrics of the classifier, 221
- Clustering algorithms, 80–86, 88–89
- Clustering and segmentation methods, 82
- Cochlear implants, 91
- Cognition errors, 77
- Cognitive augmentation, 106
- Cognitive science, 78, 83, 86
- Compilation of all systems, 226
- Control modes,
- blink stimulus mapping, 223–224
- imagined motion mapping, 226
- motion mode, 225
- motor arrangement, 225–226
- speech mode, 223
- text interface, 225
- Convolutional neural networks, 79–80, 82, 238–240
- Creutzfeldt-Jakob disease, 40
- Data analysis, 82–83
- Data collection,
- EEG headset, 213–214
- EEG signal collection, 214–215
- overview of the data, 211–213
- Data mining, 82
- Data pre-processing,
- artifact removal, 215–216
- feature extraction, 217–218
- signal processing and dimensionality reduction, 217
- DBN-RBM, 38, 44, 45
- Deep learning algorithms, 80, 84, 86, 90
- Dementia, 26, 78–79, 83–84, 92
- Denoising, 34, 44
- Desynchronization, 28, 42
- Different groups in brain disease, 151
- Diffractive deep neural network, 116
- Disabled, 85, 87, 89–90
- Electrocardiograph, 104
- Electrocorticography, 258
- Electroencephalogram, 233
- Electroencephalography (EEGs), 26–28, 32, 38, 41, 42, 83–85, 104, 180, 257
- Epilepsy, 83–84
- Error-related negativity, 181, 259
- Event related desynchronization (ERD), 28, 42
- Event-related potential (ERP), 33
- Experimental results, 169–172
- Extraction, 48
- Eye movement, 77, 79–80, 84
- Facial discolorations, 81
- Feature extraction, 10–11, 245–247
- Filtration, 48
- Functional magnetic resonance imaging (fMRI), 26, 27, 118
- Fuzzy, 256
- Galvanic skin resistance, 202
- Gas odor, 88
- GPS sensors, 80
- Healthy diet, 83
- Hearing aid, 90
- Hearing loss, 90
- Hearing threshold, 90
- Hearing-impaired people, 90
- Heart rate, 80–82
- Hemispherectomy, 118
-
Hidden Markovian model, 40
- How do BCI’s work?,
- measuring brain activity,
- with surgery, 208–209
- without surgery, 207–208
- mental strategies,
- neural motor imagery, 210–211
- SSVEP, 209–210
- Hyper-interaction, 89
- Image processing, 79–84
- Imagined speech, 233–234
- Improper closure, 92
- Informatics infrastructure and analytical analysis, 137
- Information gain, 261
- Internet of health things (IHT), 85
- Internet of medical things (IoMT), 85
- Invasive, 67
- Invasive methods,
- electrocorticography (ECoG), 7–8
- intra-cortical recording, 6–7
- Learning algorithms for analyzing rsfMRI, 151–154
- Long short-term memory (LSTM), 26, 39
- Magnetic resonance imaging (MRI), 91
- Magnetoencephalography (MEG), 31
- Mental decisions, 77–78
- Methodology, 164–169
- Multimedia technology, 87
- Multipurpose applications, 92
- Multiunit BCI, 67
- NCI, 64
- Need of resting-state MRI,
- cerebral energetics, 137
- expanded patient populations, 138
- multi-purpose data sets, 138
- reliability, 138
- signal to noise ratio (SNR), 137–138
- Neural networks (CNN), 25–26, 79–82, 84, 87–88
- Neuroergonomics, 106
- Neurological diseases, 103
- Neuronal rates, 110
- Neurophysiology, 111
- Neurosurgery, 105
- Neurotransmission, 113
- Non-invasive interface, 89
- Non-invasive methods,
- electroencephalogram (EEG), 8–9
- functional magnetic resonance imaging (fMRI), 10
- magnetoencephalography (MEG), 9–10
- near-infrared spectroscopy (NIRS), 10
- Non-verbal communication, 78
- Odor impairments, 88
- Odor-evoked memory, 88
- Olfactory disorders, 88
- Olfactory system, 88–89
- Over-fitting, 37, 46
- Particle swarm optimization, 183
- Pattern detection, 84
- Pelvic floor exercise, 93
- PET (positron emission tomography), 44, 45
- Physical disabilities, 88, 91
- Physical health, 86
- Pre-processing, 179, 244–245
- Principles of functional magnetic resonance imaging (fMRI), 128
- Prostate surgery, 92
- Real-time sensors, 80–81
- Resting state FMRI (rsfMRI) for neuroimaging, 128–129
- Resting-state functional imaging of neonatal brain image, 149–151
-
RGB image processing, 84
- rsfMRI clinical applications,
- amyotrophic lateral sclerosis (ALS) and depression, 143–144
- attention deficit hyperactivity disorder (ADHD), 147
- bipolar, 144–145
- epilepsy/seizures, 147–149
- Fronto-temporal dementia (FTD), 140–141
- mild cognitive impairment (MCI) and Alzheimer’s disease (AD), 139–140
- multiple sclerosis (MS), 141–143
- multiple system atrophy (MSA), 147
- pediatric applications, 149
- schizophrenia, 145–146
- RSVP, 43
- Sedentary, 87
- Seizures, 83–84
- Self-confidence, 86
- Semi-invasive, 65
- Signal acquisition, 5
- Signal processing, 82, 90
- Silent speech, 233–234
- Single unit BCI, 67
- Social isolation and depression, 90
- SoftMax, 46
- Spatial filtering, 34
- Speech communication, 78
- Speech disorders, 83, 85
- Speech impairment, 92
- Spelling-error distance (SEDV), 84
- SSVEP, 43
- Stress urinary incontinence (SUI), 92
- Stroke, 82–83
- Subspace alignment, 40
- SVM, 69, 255
- Taking care of children with seizure disorders, 171
- Technical development, 138–139
- The brain, 3
- The measurement of fully connected and construction of default mode network (DMN), 129
- Three dimensional (3D) convolutional neural networks (CNN), 80
- Time-frequency representations (TFRs), 35
- Transient ischemic attack (TIA), 26
- Types of classifiers,
- k-nearest neighbor classifiers, 12
- linear classifiers, 11
- neural networks classifiers, 11
- non-linear Bayesian classifiers, 12
- Urinary incontinence, 92–93
- User-friendly, 90
- Vagus nerve stimulation (VNS), 172
- Vision disorder people, 77
- Vision processing, 79
- Visual impairment, 78–79
- Wavelet features, 262
- Wavelet independent packet-based component analysis, 182, 260
- What is a BCI?, 206–207
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