Computational Neuroscience - Lecture 3 Kugiumtzis Dimitris Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, Greece e-mail: [email protected] http:nnusers.auth.grndkugiu 22 May 2018 Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 1 Single-channel EEG analysis - Introduction 2 Features of signal morphology 3 Features from linear analysis 4 Features from nonlinear analysis Outline Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 2 Features of signal morphology 3 Features from linear analysis 4 Features from nonlinear analysis Outline 1 Single-channel EEG analysis - Introduction Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 3 Features from linear analysis 4 Features from nonlinear analysis Outline 1 Single-channel EEG analysis - Introduction 2 Features of signal morphology Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 4 Features from nonlinear analysis Outline 1 Single-channel EEG analysis - Introduction 2 Features of signal morphology 3 Features from linear analysis Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Outline 1 Single-channel EEG analysis - Introduction 2 Features of signal morphology 3 Features from linear analysis 4 Features from nonlinear analysis Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Eyeball judgement: Doctor or EEGer may diagnose abnormalities by visual inspection of EEG signals (in ROI). Alternatively: Quantify automatically information from EEG signal (in ROI) ) univariate time series analysis: study the static and dynamic properties of the time series (signal), model the time series. Three main perspectives: 1 Characteristics of signal morphology, e.g. identify bursts. 2 stochastic process (linear analysis), e.g. Xt = φ0 + φ1Xt−1 + ::: + φpXt−p + t 3 nonlinear dynamics and chaos (nonlinear analysis), e.g. d St = f (St−1), St 2 IR , Xt = h(St ) + t Single-channel EEG analysis - Introduction Region of Interest (ROI) of the brain Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Alternatively: Quantify automatically information from EEG signal (in ROI) ) univariate time series analysis: study the static and dynamic properties of the time series (signal), model the time series. Three main perspectives: 1 Characteristics of signal morphology, e.g. identify bursts. 2 stochastic process (linear analysis), e.g. Xt = φ0 + φ1Xt−1 + ::: + φpXt−p + t 3 nonlinear dynamics and chaos (nonlinear analysis), e.g. d St = f (St−1), St 2 IR , Xt = h(St ) + t Single-channel EEG analysis - Introduction Region of Interest (ROI) of the brain Eyeball judgement: Doctor or EEGer may diagnose abnormalities by visual inspection of EEG signals (in ROI). Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 univariate time series analysis: study the static and dynamic properties of the time series (signal), model the time series. Three main perspectives: 1 Characteristics of signal morphology, e.g. identify bursts. 2 stochastic process (linear analysis), e.g. Xt = φ0 + φ1Xt−1 + ::: + φpXt−p + t 3 nonlinear dynamics and chaos (nonlinear analysis), e.g. d St = f (St−1), St 2 IR , Xt = h(St ) + t Single-channel EEG analysis - Introduction Region of Interest (ROI) of the brain Eyeball judgement: Doctor or EEGer may diagnose abnormalities by visual inspection of EEG signals (in ROI). Alternatively: Quantify automatically information from EEG signal (in ROI) ) Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Three main perspectives: 1 Characteristics of signal morphology, e.g. identify bursts. 2 stochastic process (linear analysis), e.g. Xt = φ0 + φ1Xt−1 + ::: + φpXt−p + t 3 nonlinear dynamics and chaos (nonlinear analysis), e.g. d St = f (St−1), St 2 IR , Xt = h(St ) + t Single-channel EEG analysis - Introduction Region of Interest (ROI) of the brain Eyeball judgement: Doctor or EEGer may diagnose abnormalities by visual inspection of EEG signals (in ROI). Alternatively: Quantify automatically information from EEG signal (in ROI) ) univariate time series analysis: study the static and dynamic properties of the time series (signal), model the time series. Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 2 stochastic process (linear analysis), e.g. Xt = φ0 + φ1Xt−1 + ::: + φpXt−p + t 3 nonlinear dynamics and chaos (nonlinear analysis), e.g. d St = f (St−1), St 2 IR , Xt = h(St ) + t Single-channel EEG analysis - Introduction Region of Interest (ROI) of the brain Eyeball judgement: Doctor or EEGer may diagnose abnormalities by visual inspection of EEG signals (in ROI). Alternatively: Quantify automatically information from EEG signal (in ROI) ) univariate time series analysis: study the static and dynamic properties of the time series (signal), model the time series. Three main perspectives: 1 Characteristics of signal morphology, e.g. identify bursts. Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 3 nonlinear dynamics and chaos (nonlinear analysis), e.g. d St = f (St−1), St 2 IR , Xt = h(St ) + t Single-channel EEG analysis - Introduction Region of Interest (ROI) of the brain Eyeball judgement: Doctor or EEGer may diagnose abnormalities by visual inspection of EEG signals (in ROI). Alternatively: Quantify automatically information from EEG signal (in ROI) ) univariate time series analysis: study the static and dynamic properties of the time series (signal), model the time series. Three main perspectives: 1 Characteristics of signal morphology, e.g. identify bursts. 2 stochastic process (linear analysis), e.g. Xt = φ0 + φ1Xt−1 + ::: + φpXt−p + t Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Single-channel EEG analysis - Introduction Region of Interest (ROI) of the brain Eyeball judgement: Doctor or EEGer may diagnose abnormalities by visual inspection of EEG signals (in ROI). Alternatively: Quantify automatically information from EEG signal (in ROI) ) univariate time series analysis: study the static and dynamic properties of the time series (signal), model the time series. Three main perspectives: 1 Characteristics of signal morphology, e.g. identify bursts. 2 stochastic process (linear analysis), e.g. Xt = φ0 + φ1Xt−1 + ::: + φpXt−p + t 3 nonlinear dynamics and chaos (nonlinear analysis), e.g. d St = f (St−1), St 2 IR , Xt = h(St ) + t Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Example: α-wave (8-13 Hz) is reduced in children and in the elderly, and in patients with dementia, schizophrenia, stroke, and epilepsy Clinical applications: predicting epileptic seizures classifying sleep stages measuring depth of anesthesia detection and monitoring of brain injury detecting abnormal brain states others ... Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Clinical applications: predicting epileptic seizures classifying sleep stages measuring depth of anesthesia detection and monitoring of brain injury detecting abnormal brain states others ... Example: α-wave (8-13 Hz) is reduced in children and in the elderly, and in patients with dementia, schizophrenia, stroke, and epilepsy Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Pn A feature can be normalized, fi := fi = j=1 fj , where n number of windows. If fi is measured in several (many) channels, fi;j , for channels j = 1;:::; K: take average at each time window, e.g. 1 PK fi := K j=1 fi;j . Features of signal morphology: Indices (statistics) that capture some characteristic of the shape of the signal. Descriptive statistics: center (mean, median) dispersion (variance, SD, interquartile range) higher moments (skewness, kurtosis) Features of signal morphology Any feature fi is computed on sliding windows i (overlapped or non-overlapped) across the EEG signal (of length nw ). Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 If fi is measured in several (many) channels, fi;j , for channels j = 1;:::; K: take average at each time window, e.g. 1 PK fi := K j=1 fi;j . Features of signal morphology: Indices (statistics) that capture some characteristic of the shape of the signal. Descriptive statistics: center (mean, median) dispersion (variance, SD, interquartile range) higher moments (skewness, kurtosis) Features of signal morphology Any feature fi is computed on sliding windows i (overlapped or non-overlapped) across the EEG signal (of length nw ). Pn A feature can be normalized, fi := fi = j=1 fj , where n number of windows. Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Features of signal morphology: Indices (statistics) that capture some characteristic of the shape of the signal. Descriptive statistics: center (mean, median) dispersion (variance, SD, interquartile range) higher moments (skewness, kurtosis) Features of signal morphology Any feature fi is computed on sliding windows i (overlapped or non-overlapped) across the EEG signal (of length nw ). Pn A feature can be normalized, fi := fi = j=1 fj , where n number of windows. If fi is measured in several (many) channels, fi;j , for channels j = 1;:::; K: take average at each time window, e.g. 1 PK fi := K j=1 fi;j . Kugiumtzis Dimitris Computational Neuroscience - Lecture 3 Descriptive statistics: center (mean, median) dispersion (variance, SD, interquartile range) higher moments (skewness, kurtosis) Features of signal morphology Any feature fi is computed on sliding windows i (overlapped or non-overlapped) across the EEG signal (of length nw ). Pn A feature can be normalized, fi := fi = j=1 fj , where n number of windows. If fi is measured in several (many) channels, fi;j , for channels j =