Ke Yu

Frequency-domain EEG source analysis for acute tonic cold pain perception

OBJECTIVE To investigate electrocortical responses to tonic cold pain by frequency-domain electroencephalogram (EEG) source analysis, and to identify potential electrocortical indices of acute tonic pain. METHODS Scalp EEG data were recorded from 26 healthy subjects under tonic cold pain (CP) and no-pain control (NP) conditions. EEG power spectra and the standardized low-resolution brain electromagnetic tomography (sLORETA) localized EEG cortical sources were compared between the two conditions in five frequency bands: 1-4 Hz, 4-8 Hz, 8-12 Hz, 12-18 Hz and 18-30 Hz. RESULTS In line with the EEG power spectral results, the source power significantly differed between the CP and NP conditions in 8-12 Hz (CPNP) in extensive brain regions. Besides, there were also significantly different 4-8 Hz and 12-18 Hz source activities between the two conditions. Among the significant source activities, the left medial frontal and left superior frontal 4-8 Hz activities, the anterior cingulate 8-12 Hz activity and the posterior cingulate 12-18 Hz activity showed significant negative correlations with subjective pain ratings. CONCLUSIONS The brains perception of tonic cold pain was characterized by cortical source power changes across different frequency bands in multiple brain regions. Oscillatory activities that significantly correlated with subjective pain ratings were found in the prefrontal and cingulate regions. SIGNIFICANCE These findings may offer useful measures for objective pain assessment and provide a basis for pain treatment by modulation of neural oscillations at specific frequencies in specific brain regions.

Common Spatio-Temporal Pattern for Single-Trial Detection of Event-Related Potential in Rapid Serial Visual Presentation Triage

Searching for target images in large volume imagery is a challenging problem and the rapid serial visual presentation (RSVP) triage is potentially a promising solution to the problem. RSVP triage is essentially a cortically-coupled computer vision technique that relies on single-trial detection of event-related potentials (ERP). In RSVP triage, images are shown to a subject in a rapid serial sequence. When a target image is seen by the subject, unique ERP characterized by P300 are elicited. Thus, in RSVP triage, accurate detection of such distinct ERP allows for fast searching of target images in large volume imagery. The accuracy of the distinct ERP detection in RSVP triage depends on the feature extraction method, for which the common spatial pattern analysis (CSP) was used with limited success. This paper presents a novel feature extraction method, termed common spatio-temporal pattern (CSTP), which is critical for robust single-trial detection of ERP. Unlike the conventional CSP, whereby only spatial patterns of ERP are considered, the present proposed method exploits spatial and temporal patterns of ERP separately, providing complementary spatial and temporal features for high accurate single-trial ERP detection. Numerical study using data collected from 20 subjects in RSVP triage experiments demonstrates that the proposed method offers significant performance improvement over the conventional CSP method (corrected p -value <; 0.05, Pearson r=0.64) and other competing methods in the literature. This paper further shows that the main idea of CSTP can be easily applied to other methods.

Cognitive workload modulation through degraded visual stimuli: a single-trial EEG study.

OBJECTIVE Our experiments explored the effect of visual stimuli degradation on cognitive workload. APPROACH We investigated the subjective assessment, event-related potentials (ERPs) as well as electroencephalogram (EEG) as measures of cognitive workload. MAIN RESULTS These experiments confirm that degradation of visual stimuli increases cognitive workload as assessed by subjective NASA task load index and confirmed by the observed P300 amplitude attenuation. Furthermore, the single-trial multi-level classification using features extracted from ERPs and EEG is found to be promising. Specifically, the adopted single-trial oscillatory EEG/ERP detection method achieved an average accuracy of 85% for discriminating 4 workload levels. Additionally, we found from the spatial patterns obtained from EEG signals that the frontal parts carry information that can be used for differentiating workload levels. SIGNIFICANCE Our results show that visual stimuli can modulate cognitive workload, and the modulation can be measured by the single trial EEG/ERP detection method.

The Synergy between Complex Channel-Specific FIR Filter and Spatial Filter for Single-Trial EEG Classification

The common spatial pattern analysis (CSP), a frequently utilized feature extraction method in brain-computer-interface applications, is believed to be time-invariant and sensitive to noises, mainly due to an inherent shortcoming of purely relying on spatial filtering. Therefore, temporal/spectral filtering which can be very effective to counteract the unfavorable influence of noises is usually used as a supplement. This work integrates the CSP spatial filters with complex channel-specific finite impulse response (FIR) filters in a natural and intuitive manner. Each hybrid spatial-FIR filter is of high-order, data-driven and is unique to its corresponding channel. They are derived by introducing multiple time delays and regularization into conventional CSP. The general framework of the method follows that of CSP but performs better, as proven in single-trial classification tasks like event-related potential detection and motor imagery.

The analytic bilinear discrimination of single-trial EEG signals in rapid image triage.

The linear discriminant analysis (LDA) method is a classical and commonly utilized technique for dimensionality reduction and classification in brain-computer interface (BCI) systems. Being a first-order discriminator, LDA is usually preceded by the feature extraction of electroencephalogram (EEG) signals, as multi-density EEG data are of second order. In this study, an analytic bilinear classification method which inherits and extends LDA is proposed. This method considers 2-dimentional EEG signals as the feature input and performs classification using the optimized complex-valued bilinear projections. Without being transformed into frequency domain, the complex-valued bilinear projections essentially spatially and temporally modulate the phases and magnitudes of slow event-related potentials (ERPs) elicited by distinct brain states in the sense that they become more separable. The results show that the proposed method has demonstrated its discriminating capability in the development of a rapid image triage (RIT) system, which is a challenging variant of BCIs due to the fast presentation speed and consequently overlapping of ERPs.

A spatio-temporal filtering approach to denoising of single-trial ERP in rapid image triage.

Conventional search for images containing points of interest (POI) in large-volume imagery is costly and sometimes even infeasible. The rapid image triage (RIT) system which is a human cognition guided computer vision technique is potentially a promising solution to the problem. In the RIT procedure, images are sequentially presented to a subject at a high speed. At the instant of observing a POI image, unique POI event-related potentials (ERP) characterized by P300 will be elicited and measured on the scalp. With accurate single-trial detection of such unique ERP, RIT can differentiate POI images from non-POI images. However, like other brain-computer interface systems relying on single-trial detection, RIT suffers from the low signal-to-noise ratio (SNR) of the single-trial ERP. This paper presents a spatio-temporal filtering approach tailored for the denoising of single-trial ERP for RIT. The proposed approach is essentially a non-uniformly delayed spatial Gaussian filter that attempts to suppress the non-event related background electroencephalogram (EEG) and other noises without significantly attenuating the useful ERP signals. The efficacy of the proposed approach is illustrated by both simulation tests and real RIT experiments. In particular, the real RIT experiments on 20 subjects show a statistically significant and meaningful average decrease of 9.8% in RIT classification error rate, compared to that without the proposed approach.

ERP signal estimation from single trial EEG.

Non-invasive EEG recordings are subject to effects such as surface conduction, resulting in very low signal to noise ratio (SNR). The conventional approach of using signal averaging to improve the SNR cannot be used for single trial EEG estimation. As such, this paper proposes a beamforming based technique that can be used to improve the signal quality from a signal trial EEG measurement. Results on experimental data show that the proposed technique can successfully isolate the signal of interest from background processes.

Multiple time-lag canonical correlation analysis for removing muscular artifacts in EEG

In this work, a new approach for joint blind source separation (BSS) of datasets at multiple time lags using canonical correlation analysis (CCA) is developed for removing muscular artifacts from electroencephalogram (EEG) recordings. The proposed approach jointly extracts sources from each dataset in a decreasing order of between-set source correlations. Muscular artifact sources that typically have lowest between-set correlations can then be removed. It is shown theoretically that the proposed use of CCA on multiple datasets at multiple time lags achieves better BSS under a more relaxed condition and hence offers better performance in removing muscular artifacts than the conventional CCA. This is further demonstrated by experiments on real EEG data.

An Electroencephalography Network and Connectivity Analysis for Deception in Instructed Lying Tasks

Deception is an impactful social event that has been the focus of an abundance of researches over recent decades. In this paper, an electroencephalography (EEG) study is presented regarding the cognitive processes of an instructed liar/truth-teller during the time window of stimulus (question) delivery period (SDP) prior to their deceptive/truthful responses towards questions related to authentic (WE: with prior experience) and fictional experience (NE: no prior experience). To investigate deception in non-experienced events, the subjects were given stimuli in a mock interview scenario that induced them to fabricate lies. To analyze the data, frequency domain network and connectivity analysis was performed in the source space in order to provide a more systematic level understanding of deception during SDP. This study reveals several groups of neuronal generators underlying both the instructed lying (IL) and the instructed truth-telling (IT) conditions for both tasks during the SDP. Despite the similarities existed in these group components, significant differences were found in the intra- and inter-group connectivity between the IL and IT conditions in either task. Additionally, the response time was found to be positively correlated with the clustering coefficient of the inferior frontal gyrus (44R) in the WE-IL condition and positively correlated with the clustering coefficient of the precuneus (7L) and the angular gyrus (39R) in the WE-IT condition. However, the response time was found to be marginally negatively correlated with the clustering coefficient of the secondary auditory cortex (42L) in the NE-IL condition and negatively correlated with the clustering coefficient of the somatosensory association cortex (5L, R) in the NE-IT condition. Therefore, these results provide complementary and intuitive evidence for the differences between the IL and IT conditions in SDP for two types of deception tasks, thus elucidating the electrophysiological mechanisms underlying SDP of deception from regional, inter-regional, network, and inter-network scale analyses.

A combination of spatial and spectral filters for mental condition discrimination

It is widely accepted that the common spatial pattern (CSP) analysis method, albeit being very popular in brain-computer interface (BCI) applications as a feature extraction method for binary classification, is vulnerable to artifact. It could underperform when it is exposed to an input whose frequency band is too broad that many interfering frequency components are contained. These drawbacks are closely related to the nature of CSP filters which are based on completely spatial weighting. That is, CSP has no control on the temporal space of brain signals. This work is one attempt to extend CSP by eliminating the undesirable temporal components through spectral filtering. The proposed method in this work retains the simplicity of CSP but derives a number of complex spatial and spectral integrated filters by applying multiple time lags and a regularization term. These filters are data-driven and channel-specific. Their ability to narrow the frequency band of signals so as to enhance feature extraction is demonstrated using a public available dataset, where 4.7% higher mean classification accuracy is achieved.