Kaiquan Shen

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.

Effect of pain perception on the heartbeat evoked potential

OBJECTIVE To investigate the effect of acute tonic pain on the heartbeat-evoked potential (HEP) and to test whether or not pain perception can be reflected by the HEP. METHODS Simultaneous electroencephalogram (EEG) and electrocardiogram (ECG) were recorded from 21 healthy young adults in three conditions: passive no-task control, no-pain control and cold pain. The HEP was obtained by using ECG R-peaks as event triggers. RESULTS Prominent HEP deflection was observed in both control conditions mainly over the frontal and central locations, while it was significantly suppressed in the cold pain condition over the right-frontal, right-central and midline locations. A comparison of the data in the first and last 5 min of cold pain condition showed that lower subjective pain ratings were accompanied by higher HEP magnitudes. A correlation analysis showed that the mean HEP magnitude over the midline locations was significantly negatively correlated with subjective pain ratings. CONCLUSIONS Cold pain induces significant suppression of the HEP across a number of scalp locations, and the suppression is correlated with self-report of pain. SIGNIFICANCE The HEP has the potential to serve as an alternative pain measure.

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.

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.

An Auditory Vigilance Task for Mental Fatigue Detection

An auditory vigilance task (AVT) as a validation criterion for monitoring mental fatigue was proposed in this study. The biological basis of this task design is on the understanding that mental fatigue is a cortical deactivation. This AVT is simple to perform, free of learning curve and independent on acquired skills (aptitude, knowledge). The validity and sensitivity of this task was verified by a scientifically controlled 25-hour fatigue experiment recorded by electroencephalogram (EEG). Results showed that this AVT is highly sensitive to changes during fatigue process. The effectiveness of this AVT was compared to one subjective rating scale (FSS). The 5-level fatigue EEG datasets (labeled by AVT and FSS respectively) were fed into support vector machines (SVM). SVM test accuracy indicated that AVT is more effective than subjects own estimation. The results demonstrate conclusively that this AVT is suitable for fatigue detection study as a reliable validation criterion

Imaging of temperature dependent hemodynamics in the rat sciatic nerve by functional photoacoustic microscopy

BackgroundVascular hemodynamics is central to the regulation of neuro-metabolism and plays important roles in peripheral nerves diseases and their prevention. However, at present there are only a few techniques capable of directly measuring peripheral nerve vascular hemodynamics.MethodHere, we investigate the use of dark-field functional photoacoustic microscopy (fPAM) for intrinsic visualizing of the relative hemodynamics of the rat sciatic nerve in response to localized temperature modulation (i.e., cooling and rewarming).Results and conclusionOur main results show that the relative functional total hemoglobin concentration (HbT) is more significantly correlated with localized temperature changes than the hemoglobin oxygen saturation (SO2) changes in the sciatic nerve. Our study also indicates that the relative HbT changes are better markers of neuronal activation than SO2 during nerve temperature changes. Our results show that fPAM is a promising candidate for in vivo imaging of peripheral nerve hemodynamics without the use of contrast agents. Additionally, this technique may shed light on the neuroprotective effect of hypothermia on peripheral nerves by visualizing their intrinsic hemodynamics.

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.

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.

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.

Structural connectivity analysis reveals topological aberrations in patients with schizophrenia

Topological analysis and the associated parameters allow elucidation of brain networks in health and illness. Evidently useful measures for defining network competency such as small-worldness can potentially improve understanding of brain connectivity and their disruptions underlying neuropsychiatric conditions such as schizophrenia. In the current study, we assessed the structural differences of brain networks in schizophrenia patients as compared with healthy controls. As proof of concept investigation, diffusion tensor imaging recordings from 2 schizophrenia patients and 2, gender and age matched, control subjects were subjected to analysis using several graph network distance metrics. Among them, those that appeared to have the ability to encode and highest sensitivity in shedding light about anatomical changes in neuron deficiency were the shortest path length and clustering coefficient parameters. Schizophrenia patients displayed comparatively lower clustering coefficient, longer path lengths and hence reduced small-worldness. These results suggest aberrant topological architecture in the structural brain networks of patients with schizophrenia, which may impact the psychopathological and cognitive manifestations of this potentially crippling illness.