Fengyu Cong

Tensor decomposition of EEG signals: a brief review.

Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Second, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higher-order partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously.

Distributed road surface condition monitoring using mobile phones

The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.

Memory-based mismatch response to frequency changes in rats.

Any occasional changes in the acoustic environment are of potential importance for survival. In humans, the preattentive detection of such changes generates the mismatch negativity (MMN) component of event-related brain potentials. MMN is elicited to rare changes (‘deviants’) in a series of otherwise regularly repeating stimuli (‘standards’). Deviant stimuli are detected on the basis of a neural comparison process between the input from the current stimulus and the sensory memory trace of the standard stimuli. It is, however, unclear to what extent animals show a similar comparison process in response to auditory changes. To resolve this issue, epidural potentials were recorded above the primary auditory cortex of urethane-anesthetized rats. In an oddball condition, tone frequency was used to differentiate deviants interspersed randomly among a standard tone. Mismatch responses were observed at 60–100 ms after stimulus onset for frequency increases of 5% and 12.5% but not for similarly descending deviants. The response diminished when the silent inter-stimulus interval was increased from 375 ms to 600 ms for +5% deviants and from 600 ms to 1000 ms for +12.5% deviants. In comparison to the oddball condition the response also diminished in a control condition in which no repetitive standards were presented (equiprobable condition). These findings suggest that the rat mismatch response is similar to the human MMN and indicate that anesthetized rats provide a valuable model for studies of central auditory processing.

MULTI-DOMAIN FEATURE EXTRACTION FOR SMALL EVENT-RELATED POTENTIALS THROUGH NONNEGATIVE MULTI-WAY ARRAY DECOMPOSITION FROM LOW DENSE ARRAY EEG

Non-negative Canonical Polyadic decomposition (NCPD) and non-negative Tucker decomposition (NTD) were compared for extracting the multi-domain feature of visual mismatch negativity (vMMN), a small event-related potential (ERP), for the cognitive research. Since signal-to-noise ratio in vMMN is low, NTD outperformed NCPD. Moreover, we proposed an approach to select the multi-domain feature of an ERP among all extracted features and discussed determination of numbers of extracted components in NCPD and NTD regarding the ERP context.

BENEFITS OF MULTI-DOMAIN FEATURE OF MISMATCH NEGATIVITY EXTRACTED BY NON-NEGATIVE TENSOR FACTORIZATION FROM EEG COLLECTED BY LOW-DENSITY ARRAY

Through exploiting temporal, spectral, time-frequency representations, and spatial properties of mismatch negativity (MMN) simultaneously, this study extracts a multi-domain feature of MMN mainly using non-negative tensor factorization. In our experiment, the peak amplitude of MMN between children with reading disability and children with attention deficit was not significantly different, whereas the new feature of MMN significantly discriminated the two groups of children. This is because the feature was derived from multi-domain information with significant reduction of the heterogeneous effect of datasets.

SINGLE-TRIAL BASED INDEPENDENT COMPONENT ANALYSIS ON MISMATCH NEGATIVITY IN CHILDREN

Independent component analysis (ICA) does not follow the superposition rule. This motivates us to study a negative event-related potential - mismatch negativity (MMN) estimated by the single-trial based ICA (sICA) and averaged trace based ICA (aICA), respectively. To sICA, an optimal digital filter (ODF) was used to remove low-frequency noise. As a result, this study demonstrates that the performance of the sICA+ODF and aICA could be different. Moreover, MMN under sICA+ODF fits better with the theoretical expectation, i.e., larger deviant elicits larger MMN peak amplitude.

Hilbert-Huang versus Morlet wavelet transformation on mismatch negativity of children in uninterrupted sound paradigm

Background Compared to the waveform or spectrum analysis of event-related potentials (ERPs), time-frequency representation (TFR) has the advantage of revealing the ERPs time and frequency domain information simultaneously. As the human brain could be modeled as a complicated nonlinear system, it is interesting from the view of psychological knowledge to study the performance of the nonlinear and linear time-frequency representation methods for ERP research. In this study Hilbert-Huang transformation (HHT) and Morlet wavelet transformation (MWT) were performed on mismatch negativity (MMN) of children. Participants were 102 children aged 8–16 years. MMN was elicited in a passive oddball paradigm with duration deviants. The stimuli consisted of an uninterrupted sound including two alternating 100 ms tones (600 and 800 Hz) with infrequent 50 ms or 30 ms 600 Hz deviant tones. In theory larger deviant should elicit larger MMN. This theoretical expectation is used as a criterion to test two TFR methods in this study. For statistical analysis MMN support to absence ratio (SAR) could be utilized to qualify TFR of MMN. Results Compared to MWT, the TFR of MMN with HHT was much sharper, sparser, and clearer. Statistically, SAR showed significant difference between the MMNs elicited by two deviants with HHT but not with MWT, and the larger deviant elicited MMN with larger SAR. Conclusion Support to absence ratio of Hilbert-Huang Transformation on mismatch negativity meets the theoretical expectations, i.e., the more deviant stimulus elicits larger MMN. However, Morlet wavelet transformation does not reveal that. Thus, HHT seems more appropriate in analyzing event-related potentials in the time-frequency domain. HHT appears to evaluate ERPs more accurately and provide theoretically valid information of the brain responses.

Linking Brain Responses to Naturalistic Music Through Analysis of Ongoing EEG and Stimulus Features

This study proposes a novel approach for the analysis of brain responses in the modality of ongoing EEG elicited by the naturalistic and continuous music stimulus. The 512-second long EEG data (recorded with 64 electrodes) are first decomposed into 64 components by independent component analysis (ICA) for each participant. Then, the spatial maps showing dipolar brain activity are selected in terms of the residual dipole variance through a single dipole model in brain imaging, and clustered into a pre-defined number (estimated by the minimum description length) of clusters. Subsequently, the temporal courses of the EEG theta and alpha oscillations of each component for each cluster are produced and correlated with the temporal courses of tonal and rhythmic features of the music. Using this approach, we found that the extracted temporal courses of the theta and alpha oscillations along central and occipital area of scalp in two of the selected clusters significantly correlated with the musical features representing progressions in the rhythmic content of the stimulus. We suggest that this demonstrates that with the proposed approach, we have managed to discover what kinds of brain responses were elicited when a participant was listening continuously to the long piece of naturalistic music.

Event-related potentials to unattended changes in facial expressions: detection of regularity violations or encoding of emotions?

Visual mismatch negativity (vMMN), a component in event-related potentials (ERPs), can be elicited when rarely presented “deviant” facial expressions violate regularity formed by repeated “standard” faces. vMMN is observed as differential ERPs elicited between the deviant and standard faces. It is not clear, however, whether differential ERPs to rare emotional faces interspersed with repeated neutral ones reflect true vMMN (i.e., detection of regularity violation) or merely encoding of the emotional content in the faces. Furthermore, a face-sensitive N170 response, which reflects structural encoding of facial features, can be modulated by emotional expressions. Owing to its similar latency and scalp topography with vMMN, these two components are difficult to separate. We recorded ERPs to neutral, fearful, and happy faces in two different stimulus presentation conditions in adult humans. For the oddball condition group, frequently presented neutral expressions (p = 0.8) were rarely replaced by happy or fearful expressions (p = 0.1), whereas for the equiprobable condition group, fearful, happy, and neutral expressions were presented with equal probability (p = 0.33). Independent component analysis (ICA) revealed two prominent components in both stimulus conditions in the relevant latency range and scalp location. A component peaking at 130 ms post stimulus showed a difference in scalp topography between the oddball (bilateral) and the equiprobable (right-dominant) conditions. The other component, peaking at 170 ms post stimulus, showed no difference between the conditions. The bilateral component at the 130-ms latency in the oddball condition conforms to vMMN. Moreover, it was distinct from N170 which was modulated by the emotional expression only. The present results suggest that future studies on vMMN to facial expressions should take into account possible confounding effects caused by the differential processing of the emotional expressions as such.

Answering six questions in extracting children's mismatch negativity through combining wavelet decomposition and independent component analysis.

This study combines wavelet decomposition and independent component analysis (ICA) to extract mismatch negativity (MMN) from electroencephalography (EEG) recordings. As MMN is a small event-related potential (ERP), a systematic ICA based approach is designed, exploiting MMN’s temporal, frequency and spatial information. Moreover, this study answers which type of EEG recordings is more appropriate for ICA to extract MMN, what kind of the preprocessing is beneficial for ICA decomposition, which algorithm of ICA can be chosen to decompose EEG recordings under the selected type, how to determine the desired independent component extracted by ICA, how to improve the accuracy of the back projection of the selected independent component in the electrode field, and what can be finally obtained with the application of ICA. Results showed that the proposed method extracted MMN with better properties than those estimated by difference wave only using temporal information or ICA only using spatial information. The better properties mean that the deviant with larger magnitude of deviance to repeated stimuli in the oddball paradigm can elicit MMN with larger peak amplitude and shorter latency. As other ERPs also have the similar information exploited here, the proposed method can be used to study other ERPs.