Index

  • 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
  • Ketogenic diet, 172
  • 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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