Empirical Mode Decomposition-Based Approach for Intertrial Analysis of Olfactory Event-Related Potential Features Chi-Hsun Wu, Po-Lei Lee, Chih-Hung Shu, Chia-Yen Yang, Men-Tzung Lo, Chun-Yen Chang & Jen-Chuen Hsieh Chemosensory Perception ISSN 1936-5802 Volume 5 Combined 3-4 Chem. Percept. (2012) 5:280-291 DOI 10.1007/s12078-012-9134-8
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Author's personal copy Chem. Percept. (2012) 5:280–291 DOI 10.1007/s12078-012-9134-8
Empirical Mode Decomposition-Based Approach for Intertrial Analysis of Olfactory Event-Related Potential Features Chi-Hsun Wu & Po-Lei Lee & Chih-Hung Shu & Chia-Yen Yang & Men-Tzung Lo & Chun-Yen Chang & Jen-Chuen Hsieh
Received: 27 March 2012 / Accepted: 27 August 2012 / Published online: 12 September 2012 # Springer Science+Business Media, LLC 2012
Abstract This study presents an empirical mode decomposition (EMD)-based method to study the intertrial variability of olfactory event-related potential (OERP) features. The olfactory stimulus in this study was a mixture of 60 % humidity air and 40 % phenyl ethanol alcohol generated by a computercontrolled olfactometer with a constant flow rate of 8 L/min. A 32-channel whole-head EEG system was utilized to investigate the olfactory responses in 12 healthy subjects. Each EEG epoch was segmented based on the olfactory stimulus
onset and subsequently decomposed into a set of intrinsic mode functions (IMFs) by using EMD. Only IMFs that met both frequency and spatial dual criteria were chosen as OERPrelated IMFs for reconstructing the noise-suppressed singletrial activity, and those significant trials with N1/P2 amplitudes lower/greater than the mean minus/plus two times the standard deviations of baseline amplitudes were denoted as single-trial OERP for intertrial variability analysis. The present approach enables the capability to study intertrial OERP
C.-H. Wu : P.-L. Lee (*) Department of Electrical Engineering, National Central University, No.300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan, Republic of China e-mail:
[email protected]
C.-H. Shu Department of Otolaryngology, Taipei Veterans General Hospital, No.201, Sec. 2, Shipai Rd., Beitou District, Taipei City 11217( Taiwan, Republic of China
C.-H. Wu e-mail:
[email protected] C.-H. Wu : P.-L. Lee : C.-H. Shu : J.-C. Hsieh Laboratory of Integrated Brain Research, Department of Medical Research and Education, Taipei Veterans General Hospital, No.201, Sec. 2, Shipai Rd., Beitou District, Taipei City 11217( Taiwan, Republic of China C.-H. Shu e-mail:
[email protected] J.-C. Hsieh e-mail:
[email protected] P.-L. Lee : J.-C. Hsieh Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan P.-L. Lee : M.-T. Lo Center for Dynamical Biomarkers and Translational Medicine, National Central University, No.300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan, Republic of China M.-T. Lo e-mail:
[email protected]
C.-Y. Yang Department of Biomedical Engineering, Ming-Chuan University, No. 5 De Ming Rd., Gui Shan District, Taoyuan County 333( Taiwan, Republic of China e-mail:
[email protected]
C.-Y. Chang Science Education Center and Graduate Institute of Science Education, National Taiwan Normal University, No. 88, Sec. 4, Ting-Chou Road, Taipei City 11677, Taiwan e-mail:
[email protected]
C.-Y. Chang Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan
J.-C. Hsieh School of Medicine, National Yang-Ming University, Taipei, Taiwan
Author's personal copy Chem. Percept. (2012) 5:280–291
features, such as the latencies and amplitudes of N1 and P2 peaks, on trial-by-trial basis, which may be helpful to shed light on future olfactory dysfunction studies. Keywords Electroencephalography . Empirical mode decomposition . Olfactory event-related potential Abbreviations ANN Artificial neural network A-N1 N1 peak amplitude A-N1P2 Peak-to-peak N1P2 amplitude A-P2 P2 peak amplitude COI Channel of interest DRS Dementia rating scale ECD Equivalent current dipole ECG Electrocardiogram EMD Empirical mode decomposition EMG Electromyography EOG Electrooculogram ERP Event-related potential FFT Fast Fourier transform GMM Gaussian mixture model IMF Intrinsic mode function ISI Interstimulus interval L-N1 N1 latency L-P2 P2 latency MS Multiple sclerosis OERP Olfactory event-related potential PEA Phenyl ethyl alcohol PD Parkinson’s disease SSP Signal space projection
Introduction Olfactory event-related potential (OERP) received little attention prior to the 1980s due to the lack of an accurate method to produce a selective and controlled odorant for inducing human olfactory responses. Only after Kobal solved the major methodological concern of controlling odorant stimulation at the millisecond level did the measurement of OERP become an objective tool for examining olfactory function (Kobal and Hummel 1988). Compared to other psychophysical examination tools for olfactory dysfunction assessment, OERPs are directly correlated to neuronal activities, are independent of the subject’s bias, and have high temporal resolution for sequentially investigating olfactory information. Researchers have used OERPs to explore neurophysiological mechanisms in normal brains and to probe pathophysiology in the diseased. The OERP technique has been used as a diagnostic index (Lorig 2000) for investigating congenital anosmia (Cui and Evans 1997), normal aging (Murphy et al. 2000), Parkinson’s disease (Barz et al. 1997), multiple sclerosis (Doty et al.
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1999), dementia (Morgan and Murphy 2002), brain tumors (Daniels et al. 2001), head trauma (Geisler et al. 1999), and epilepsy (Hummel et al. 1995). The aforementioned OERP studies required an average of olfactory responses over many trials, which may mask the intertrial variability and smear the OERP amplitudes. Since EEG responses can vary from trial to trial depending on the subject’s performance and state and since they carry important information on cognitive and physiological states such as expectation, attention, and arousal (Jung et al. 2001; Lee et al. 2003, 2009), the average of many trials may obscure the intertrial OERP variability during olfactory experiments. Previous researches show that OERP responses are affected by the intensity of odorized air (Rombaux et al. 2006), flow rate of odorant stimulus (Rothe 2003), interstimulus interval (ISI) (Hummel and Kobal 1999; Wang et al. 2002; Wetter and Murphy 2003), subject vigilance (Geisler and Murphy 2000; Nordin et al. 2005), age of participant (Evans et al. 1993; Hummel et al. 1998, 2003; Covington et al. 1999), gender (Evans et al. 1993; Morgan et al. 1997, 1999; Lundstrom et al. 2005), hormonal cycle (Pause et al. 1996), pregnancy (Olofsson et al. 2005), attention loss (Masago et al. 2001), olfaction fatigue (Caccappolo et al. 2000), and training effect (Livermore and Hummel 2004). Accordingly, an effective method for studying OERP on single-trial basis can greatly reduce experimental time and enable the trial by trial examination of brain olfactory functions. Trial-wise EEG analysis has been developed for timelocked and phase-locked, evoked brain activities. Previous methods include blind source separation algorithms (Belouchrani et al. 1997; Jung et al. 2001; Tang et al. 2002; Barbati et al. 2006), differentially variable component analysis (Knuth et al. 2006), Kalman filter methods (Georgiadis et al. 2005), and wavelet decomposition and time-frequency-based methods (Quian Quiroga and Garcia 2003; Wang et al. 2007). However, signal extraction in the aforementioned trial-wise methods requires prespecified basis functions or predefined statistical models. This might cause difficulties in adaptively determining the requisite prior information due to the complex temporal and spectral changes in stochastic signals. For example, the ICA-based method premises no more than one normal Gaussian source existing in the extracted components under the assumption of either a super-Gaussian or subGaussian probability distribution (Bingham and Hyvarinen 2000). Other methods, such as wavelet or short-time Fourier transform, decompose signals using a predefined basis, which may be too stringent for stochastic signal interpretations (Huang et al. 1998a). This study presents an empirical mode decomposition (EMD)-based approach to study intertrial variability of OERP signals. Huang et al. firstly proposed the EMD process as an efficient method for analyzing nonlinear and nonstationary data (Huang et al. 1998a; Flandrin et al.
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2004; Wu et al. 2007). The EMD is a useful data-driven tool for extracting meaningful stochastically modulated signals in many applications, such as measuring blood pressure (Huang et al. 1998b), heart-rate variability in electrocardiogram (ECG) (Balocchi et al. 2004), and pulmonary hypertension (Huang et al. 1998b). With the merit of EMD, trialby-trial examination of OERP quality is allowed which makes it possible to exclude trials with poor-performance OERPs.
Materials and Methods EEG Recordings A 32-channel whole-head EEG system (Quick Amplifier, Brain Product Co., Munich, Germany) was used to measure EEG signals. Interelectrode impedance was monitored at below 10 kΩ during EEG recordings to ensure EEG signal quality. One electrooculogram (EOG) channel was monitored with an electrode pair placed at the outer canthi below/ above the left eye and above/below the right eye in oblique direction, providing eye-blinking information, and an EOG threshold at 150 μV was used to define artifact-suppressed EEG epochs. EEG activities and EOG signals were both digitized at 2 kHz without applying any digital filter, subjected to the following EMD-based approach and conventional event-related potential (ERP) processes. Odorant Preparation Olfactory stimuli were given by an olfactometer (OM6b, Burghart, Wedel, Germany) (Kobal and Hummel 1988) which is capable of generating rectangular-shaped chemosensory stimuli with rise time less than 20 ms. Every olfactory stimulus had a 300-ms stimulus duration. One odorant as olfactory stimuli and odorless air were used for the experiment. Humidified air was controlled at 36.5 °C, and 80 % humidity was used for the airstream during the nonstimulation period and for odorant dilution during the olfactory stimulation period. The odorant was 40 % concentration of phenyl ethanol alcohol (PEA) (40 % volume fraction of pure PEA mixed with the 60 % volume fraction of humidified air), while the odorless air was pure humidified air. The airstream and olfactory stimuli were delivered to each subject’s right nostril by using a Teflon tubing (1.6 mm inner diameter) and computer-controlled at 8 L/min to avoid inducing mechanosensory responses. Subjects and Experimental Paradigm Twelve healthy subjects (eight males and four females; aged between 22 and 42 years old, mean age 26.2±5.5 years old) were recruited in this study. All subjects had no history of
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olfaction disorder. Each subject was asked to sit comfortably in a dimly lit, well-ventilated EEG room. A pair of earplugs was provided to each subject to block out ambient noise. An LCD screen located at 50 cm in front of the subject was used to present experimental instructions. Each trial was 40 s long, including an experimental period (0–30 s) and a relaxation period (30–40 s). A red marker appeared on the lower side of the LCD screen at the beginning of each trial to maintain subject’s vigilance. The olfactory stimulus onset was randomly administered, with equal presence probability between 10 and 20 s anchored to the onset timing of the red marker, to avoid subject’s expectation effect. A green marker appeared on the LCD screen at 30 s to instruct the subject to relax (Fig. 1). During the whole experimental period, subjects were asked to keep their eyes focused on a fixation cross in the center of the LCD to reduce eye motion artifacts. Before EEG recording, five odorless-air trials were given for each subject to familiarize with the experimental paradigm. In the olfactory experiment, each subject completed 100 trials (50 odorant trials and 50 odorless-air trials) in a random order. To prevent subjects from olfactory fatigue, subjects were forced to take a 5-min break after every 20 trials. All subjects gave an informed consent after a full explanation of the experimental protocol. This study was approved by the Ethics Committee of the Institutional Review Board at Taipei Veterans General Hospital, Taiwan. Determination of Subject-Specific OERP-Laden Frequency Band and Creation of Spatial Template for Frequency and Spatial Dual Criteria To optimize the procedure of EMD-based approach for OERP-related single-trial activity extraction, the frequency
Subject’s action
eye fixation velopharyngeal breathing
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Visual cue Olfactory stimulus onset (stimulus duration = 0.3 s)
Event duration
Time 10 s
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Fig. 1 Experimental paradigm for the EMD-based OERP study. Each trial was 40 s long and included an experimental period (0–30 s) and a relaxation period (30–40 s). A red marker was presented as an instruction for subject’s vigilance. The olfactory stimulus onset was randomly administered, with equal presence probability between 10 and 20 s anchored to the onset timing of red marker, to avoid subject’s expectation effect. A green marker appeared at 30 s to instruct the subject to relax
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~ St ¼ ½AP2 ð1ÞAP2 ð2Þ AP2 ðM Þ;
ð1Þ
where AP2(j) is the amplitude of the jth EEG channel at tP2 and M is the total number of EEG channels. The signal flowchart for the generation of subject-specific OERP-laden frequency band and spatial template was shown in Fig. 3. Extraction of Single-Trial Activity Using EMD-Based Approach and Frequency and Spatial Dual Criteria Empirical mode decomposition is a time-series data analysis method. The EMD method is based on the assumption that any data set can be constructed by a series of IMFs. These bases are analytic, self-constructed, well-defined, data-driven function whose amplitudes and frequencies can vary with time (Huang et al. 1998a). In signal processing, the construction of IMF
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and spatial dual criteria were designed for each subject to select OERP-related intrinsic mode functions (IMFs) in the EMD-based approach (see below). For each subject, the continuous EEG data were first filtered within 0.1–100 Hz and then segmented into epochs, from −300 to 1,000 ms, anchored to the olfactory stimulus onset. The epochs that passed the EOG examination (EOG
