Biomaterial Database

Multiresolution analysis of event-related potentials by wavelet decomposition. 1995

V J Samar, and K P Swartz, and M R Raghuveer

Communication Research Department, National Technical Institute for the Deaf at Rochester Institute of Technology, NY 14623-0887, USA.

Wavelet analysis is presented as a new tool for analyzing event-related potentials (ERPs). The wavelet transform expands ERPs into a time-scale representation, which allows the analyst to zoom in on the small scale, fine structure details of an ERP or zoom out to examine the large scale, global waveshape. The time-scale representation is closely related to the more familiar time-frequency representation used in spectrograms of time-varying signals. However, time-scale representations have special properties that make them attractive for many ERP applications. In particular, time-scale representations permit theoretically unlimited time resolution for the detection of short-lived peaks and permit a flexible choice of wavelet basis functions for analyzing different types of ERPs. Generally, time-scale representations offer a formal basis for designing new, specialized filters for various ERP applications. Among recently explored applications of wavelet analysis to ERPs are (a) the precise identification of the time of occurrence of overlapping peaks in the auditory brainstem evoked response; (b) the extraction of single-trial ERPs from background EEG noise; (c) the decomposition of averaged ERP waveforms into orthogonal detail functions that isolate the waveform's experimental behavior in distinct, orthogonal frequency bands; and (d) the use of wavelet transform coefficients to concisely extract important information from ERPs that predicts human signal detection performance. In this tutorial we present an intuitive introduction to wavelets and the wavelet transform, concentrating on the multiresolution approach to wavelet analysis of ERP data. We then illustrate this approach with real data. Finally, we offer some speculations on future applications of wavelet analysis to ERP data.

UI	MeSH Term	Description	Entries
D008991	Monitoring, Physiologic	The continuous measurement of physiological processes, blood pressure, heart rate, renal output, reflexes, respiration, etc., in a patient or experimental animal; includes pharmacologic monitoring, the measurement of administered drugs or their metabolites in the blood, tissues, or urine.	Patient Monitoring,Monitoring, Physiological,Physiologic Monitoring,Monitoring, Patient,Physiological Monitoring
D001921	Brain	The part of CENTRAL NERVOUS SYSTEM that is contained within the skull (CRANIUM). Arising from the NEURAL TUBE, the embryonic brain is comprised of three major parts including PROSENCEPHALON (the forebrain); MESENCEPHALON (the midbrain); and RHOMBENCEPHALON (the hindbrain). The developed brain consists of CEREBRUM; CEREBELLUM; and other structures in the BRAIN STEM.	Encephalon
D001931	Brain Mapping	Imaging techniques used to colocalize sites of brain functions or physiological activity with brain structures.	Brain Electrical Activity Mapping,Functional Cerebral Localization,Topographic Brain Mapping,Brain Mapping, Topographic,Functional Cerebral Localizations,Mapping, Brain,Mapping, Topographic Brain
D004569	Electroencephalography	Recording of electric currents developed in the brain by means of electrodes applied to the scalp, to the surface of the brain, or placed within the substance of the brain.	EEG,Electroencephalogram,Electroencephalograms
D005071	Evoked Potentials	Electrical responses recorded from nerve, muscle, SENSORY RECEPTOR, or area of the CENTRAL NERVOUS SYSTEM following stimulation. They range from less than a microvolt to several microvolts. The evoked potential can be auditory (EVOKED POTENTIALS, AUDITORY), somatosensory (EVOKED POTENTIALS, SOMATOSENSORY), visual (EVOKED POTENTIALS, VISUAL), or motor (EVOKED POTENTIALS, MOTOR), or other modalities that have been reported.	Event Related Potential,Event-Related Potentials,Evoked Potential,N100 Evoked Potential,P50 Evoked Potential,N1 Wave,N100 Evoked Potentials,N2 Wave,N200 Evoked Potentials,N3 Wave,N300 Evoked Potentials,N4 Wave,N400 Evoked Potentials,P2 Wave,P200 Evoked Potentials,P50 Evoked Potentials,P50 Wave,P600 Evoked Potentials,Potentials, Event-Related,Event Related Potentials,Event-Related Potential,Evoked Potential, N100,Evoked Potential, N200,Evoked Potential, N300,Evoked Potential, N400,Evoked Potential, P200,Evoked Potential, P50,Evoked Potential, P600,Evoked Potentials, N100,Evoked Potentials, N200,Evoked Potentials, N300,Evoked Potentials, N400,Evoked Potentials, P200,Evoked Potentials, P50,Evoked Potentials, P600,N1 Waves,N2 Waves,N200 Evoked Potential,N3 Waves,N300 Evoked Potential,N4 Waves,N400 Evoked Potential,P2 Waves,P200 Evoked Potential,P50 Waves,P600 Evoked Potential,Potential, Event Related,Potential, Event-Related,Potential, Evoked,Potentials, Event Related,Potentials, Evoked,Potentials, N400 Evoked,Related Potential, Event,Related Potentials, Event,Wave, N1,Wave, N2,Wave, N3,Wave, N4,Wave, P2,Wave, P50,Waves, N1,Waves, N2,Waves, N3,Waves, N4,Waves, P2,Waves, P50
D005583	Fourier Analysis	Analysis based on the mathematical function first formulated by Jean-Baptiste-Joseph Fourier in 1807. The function, known as the Fourier transform, describes the sinusoidal pattern of any fluctuating pattern in the physical world in terms of its amplitude and its phase. It has broad applications in biomedicine, e.g., analysis of the x-ray crystallography data pivotal in identifying the double helical nature of DNA and in analysis of other molecules, including viruses, and the modified back-projection algorithm universally used in computerized tomography imaging, etc. (From Segen, The Dictionary of Modern Medicine, 1992)	Fourier Series,Fourier Transform,Analysis, Cyclic,Analysis, Fourier,Cyclic Analysis,Analyses, Cyclic,Cyclic Analyses,Series, Fourier,Transform, Fourier
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012815	Signal Processing, Computer-Assisted	Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.	Digital Signal Processing,Signal Interpretation, Computer-Assisted,Signal Processing, Digital,Computer-Assisted Signal Interpretation,Computer-Assisted Signal Interpretations,Computer-Assisted Signal Processing,Interpretation, Computer-Assisted Signal,Interpretations, Computer-Assisted Signal,Signal Interpretation, Computer Assisted,Signal Interpretations, Computer-Assisted,Signal Processing, Computer Assisted
D016057	Evoked Potentials, Auditory, Brain Stem	Electrical waves in the CEREBRAL CORTEX generated by BRAIN STEM structures in response to auditory click stimuli. These are found to be abnormal in many patients with CEREBELLOPONTINE ANGLE lesions, MULTIPLE SCLEROSIS, or other DEMYELINATING DISEASES.	Acoustic Evoked Brain Stem Potentials,Auditory Brain Stem Evoked Responses,Brain Stem Auditory Evoked Potentials,Evoked Responses, Auditory, Brain Stem,Acoustic Evoked Brain Stem Potential,Acoustic Evoked Brainstem Potential,Acoustic Evoked Brainstem Potentials,Auditory Brain Stem Evoked Response,Auditory Brain Stem Response,Auditory Brain Stem Responses,Auditory Brainstem Evoked Response,Auditory Brainstem Evoked Responses,Auditory Brainstem Responses,Brain Stem Auditory Evoked Potential,Brainstem Auditory Evoked Potential,Brainstem Auditory Evoked Potentials,Evoked Potential, Auditory, Brainstem,Evoked Potentials, Auditory, Brainstem,Evoked Response, Auditory, Brain Stem,Evoked Response, Auditory, Brainstem,Evoked Responses, Auditory, Brainstem,Auditory Brainstem Response,Brainstem Response, Auditory,Brainstem Responses, Auditory,Response, Auditory Brainstem,Responses, Auditory Brainstem
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron