EEG Signal Processing and Machine Learning. Saeid Sanei
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discriminant analysisLDALong delta activityLELyapunov exponentLEMLocal EEG modelLLELargest Lyapunov exponentLMSLeast mean squareLORETALow‐resolution electromagnetic tomography algorithmLPLowpassLPMLetters per minuteLPPLate positive potentialLRCNLong‐term recurrent convolutional networkLRTLow‐resolution tomographyLSLeast squaresLSELeast‐squares errorLSTMLong short‐term memory networkLVQLearning vector quantizationLWRLevinson–Wiggins–RobinsonLZCLempel–Ziv complexityM2MMachine‐to‐machineMAMental arithmeticMAMoving averageMAFMultivariate ambiguity functionMAPMaximum a posterioriMCIMild cognitive impairmentMCMCMarkov chain Monte CarloMDIMultidimensional directed informationMDPMoving dipoleMEGMagnetoencephalogramMFDEMultiscale fluctuation‐based dispersion entropymHTTMutant huntingtinMIMutual informationMILMatrix inversion lemmaMLMaximum likelihoodMLEMaximum likelihood estimationMLEMaximum Lyapunov exponentMLPMultilayer perceptronMMNMismatch negativityMMSEMinimum mean squared errorMNIMontreal Neurological Institute and HospitalMNLSMinimum norm least squaresMPMatching pursuitsMRIMagnetic resonance imagingMRPMovement‐related potentialMSEMultiple system atrophyMSEMean squared errorMSEMultiscale entropyMSMultiple sclerosisMTLEMesial temporal lobe epilepsyMUSICMultichannel signal classificationMVARMultivariate autoregressiveNaSodiumNCNormal controlNCDFNormal cumulative distribution functionNCSPNonparametric common spatial patternsNDDNeurodevelopmental disorderNESNonepileptic seizureNIHNational Institute of HealthNIRNear infraredNIRSNear‐infrared spectroscopyNLMSNormalized least mean squareNMFNonnegative matrix factorizationNMCSPNonparametric multiclass common spatial patternsNMRNuclear magnetic resonanceNNNeural networkNNQPNonnegative quadratic programNPNeural processNREMNon‐rapid eye movementNSINonstationary indexOAOcular artefactOBSOptimal basis setOBSOrganic brain syndromeOFCOrbital frontal cortexOMPOrthogonal matching pursuitOPOddball paradigmOSAObstructive sleep apnoeaOSAHSObstructive sleep apnoea hypopnea syndromePARAFACParallel factor analysisPCAPrincipal component analysisPCANetPrincipal component analysis networkPCCPearson product correlation coefficientPCCPosterior cingulate corticesPDParkinson’s diseasePDCPartial directed coherencepdfProbability density functionPeError positivityPerEnPermutation entropyPETPositron emission tomographyPFParticle filterPFCPrefrontal cortexPICPower iteration clusteringPIFPhase interaction functionPLEDPeriodic literalized epileptiform dischargesPLIPhase lag indexPLMDPeriodic limb movement disorderPLSPartial least squaresPMBRPost‐movement beta reboundPMBSPost‐movement beta synchronizationPNRDNonrhythmic delta activityPOSTPositive occipital sharp transientsPPCPhase–phase couplingPPGPhotoplethysmographyPPMPiecewise Prony methodPSDPower spectrum densityPSDMPhase‐space dissimilarity measuresPSGPolysomnographyPSIPhase‐slope indexPSPPost‐synaptic potentialPSPProgressive supranuclear palsyPSWCPeriodic sharp wave complexesPTSDPost‐traumatic stress disorderPWVDPseudo‐Wigner–Ville distributionQEEGQuantitative EEGQGARCHQuadratic GARCHQNNQuantum neural networksQPQuadratic programmingR&KRechtschtschaffen and KalesRAPRecursively applied and projectedRASMRational asymmetryRBDREM sleep behaviour disorderRBFRadial basis functionRBPFRao‐Blackwellised particle filterRBRRelative beta ratioRCERecursive channel eliminationRERegional entropyReLURectified linear unitREMRapid eye movementRFRadio frequencyRFNNRecurrent fuzzy neural networkRKHSReproducing kernel Hilbert spacesRLSRecursive least squaresRMBFRobust minimum variance beamformerRMSRoot mean squareRNNRecurrent neural networkROCReceiver operating characteristicRPReadiness potentialRRRespiratory rateRTReaction timerTMSrepetitive transcranial magnetic stimulationRVResidual varianceSAEStacked autoencoderSampEnSample entropySASSleep apnoea syndromeSCASparse component analysisSCDSickle cell diseaseSCPSlow cortical potentialSCPSSlow cortical potential shiftSCRSkin conductance responseSCVSpectral coherence valueSCWTStroop colour and word testSDAEStacked denoising autoencoderSDTFShort‐time DTFSEMStructural equation modellingSFSSyncFastSlowSGSensory gatingSISynchronization indexSICASpatial ICASLSynchronization likelihoodsLORETAStandardized LORETASLTPShort‐ and long‐term predictionSMISample‐matrix inversionSMlSensorimotor leftSMOTESynthetic minority oversampling techniquesMRIStructural MRISNSalient networkSNNSpike neural networkSNNAPSimulator for Neural Networks and Action PotentialsSNRSignal‐to‐noise ratioSOBISecond‐order blind identificationSOBIUMSecond‐order blind identification of underdetermined mixturesSPETSingle photon emission tomographySPMStatistical parametric mappingSPQSchizotypal personality questionnaireSREDASubclinical rhythmic EEG discharges of adultsSRNNSleep EEG recognition neural networkSSASingular spectrum analysisSSLOFOShrinking standard LORETA‐FOCUSSSSPESubacute sclerosing panencephalitisSSVEPSteady‐state visual evoked potentialSSVERSteady‐state visual evoked responseSSWTSynchro‐squeezing wavelet transformSTFSpace–time–frequencySTFDSpatial time–frequency distributionSTFTShort time–frequency transformSTLShort‐term largest Lyapunov exponentSTSSuperior temporal sulcusSVSupport vectorsSVDSingular‐value decompositionSVMSupport vector machinesSVRSupport vector regressionSWASlow‐wave activitySWDAStep‐wise discriminant analysisSWSlow waveSWPSlow‐wave powerSWSSlow‐wave sleepTBITraumatic brain injuryTDNNTime delay neural networkTDOATime difference of arrivalTDP‐43Transactive response DNA‐binding protein 43 kDaTENSTranscutaneous electrical nerve stimulationTFTime–frequencyTGARCHThreshold GARCH modelTICATemporal ICATISTMS induction simulatorTLETemporal lobe epilepsyTMSTranscranial magnetic stimulationTNTrue negativeTNMTraditional nonlinear methodTOATime of arrivalTotHbTotal haemoglobinTPTrue positiveTRRepeat timeTSSATensor‐based singular spectrum analysisTTDThought translation deviceUBIUnderdetermined blind identificationUOMUnderdetermined orthogonal modelUSPUndetected source numberUSRUnderdetermined source recoveryVAEVariational autoencoderVEOGVertical electro‐oculographVEPVisual evoked potentialVLSIVery large‐scale integratedvMPFCVentral medial prefrontal corticesVPPVertex positive peakVRVirtual realityWAWald tests on amplitudesWCWord chainWCOWeakly coupled oscillatorWDCWeighted degree centralityWLWald test on locationsWMNWeighted minimum normWNWavelet networkWPEWavelet packet energywPLIWeighted phase lag indexwSMIWeighted symbolic mutual informationWTWavelet transformWUWeighted undersamplingWVWigner–Ville
1 Introduction to Electroencephalography
1.1 Introduction
The brain is the most amazing and complicated part of the human body and is responsible for controlling all other organs. The neural activity of the human brain starts between the seventeenth and twenty‐third week of prenatal development. It is believed that from this early stage and throughout life electrical signals generated by the brain represent not only the brain function but also the status of the whole body. This assumption provides the motivation to study and understand the range of brain activities including normal brain rhythms, brain responses to stimuli, brain motor generators, and finally brain connectivity. One or more of these activities change in cases of brain disorder, disease, or abnormality. The brain status and often the entire body condition can then be recognized by applying advanced digital signal processing and machine learning methods to the electroencephalography (EEG) signals measured from the brain, and thereby underpin the later chapters of this book.
Although nowhere in this book do the authors attempt to comment on the physiological aspects of brain activities, there are several issues related to the nature of the original sources, their actual patterns, and the characteristics of the medium, that have to be addressed. The medium defines the path from the neurons, so‐called signal sources, to the electrodes, which are the sensors where some form of mixtures of the sources (for the case of scalp electrodes) or individual sources (e.g. for subdural electrodes) are measured.
Understanding of neuronal functions and neurophysiological properties of the brain together with the mechanisms underlying the generation of signals and their recordings is however, vital for those who deal with these signals for detection, diagnosis, and treatment of brain disorders and the related diseases.
Examining brain activity or abnormality, however, is not limited to the use of EEG. For the abnormalities which affect the brain structure, different radiographical methods can be used. Also, for detecting the brain functional abnormalities, which is the main agenda for activity detection and functional monitoring, magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) may be used. However, MEG is expensive and not readily accessible, and fMRI has very low temporal resolution and is not widely available. Therefore, EEG remains the main functional brain scanning modality as it is cheap, portable, and widely available.
We begin by providing a brief history of EEG measurements and looking at the journey from the time the brain function was initially recognized to