Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. Muscular activities strongly obscure EEG signals and complicate subsequent EEG-based data analysis. Conventional methods for removing muscle artifact from EEG are usually based on blind source separation techniques and involve jointly analyzing multichannel EEG recordings. Instead of using the multichannel approaches, this paper proposes to explore single-channel techniques for muscle artifact removal from multichannel EEG. It may seem paradoxical that we denoise each channel individually while ignoring interchannel relationships. We conduct a performance comparison study, through numerical simulations and applications to real EEG recordings contaminated with muscle artifacts. The results demonstrate the advantage of single-channel techniques over multichannel ones, especially for low signal-to-noise ratios. This paper may change the traditional understanding of denoising the EEG signals.

The emergence of wireless sensor networks (WSNs) has motivated a paradigm shift in patient monitoring and disease control. Epilepsy management is one of the areas that could especially benefit from the use of WSN. By using miniaturized wireless electroencephalogram (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time seizure detection outside clinical settings. One major consideration in using such a wireless EEG-based system is the stringent battery energy constraint at the sensor side. Different solutions to reduce the power consumption at this side are therefore highly desired. The conventional approach incurs a high power consumption, as it transmits the entire EEG signals wirelessly to an external data server (where seizure detection is carried out). This paper examines the use of data reduction techniques for reducing the amount of data that has to be transmitted and, thereby, reducing the required power consumption at the sensor side. Two data reduction approaches are examined: compressive sensing-based EEG compression and low-complexity feature extraction. Their performance is evaluated in terms of seizure detection effectiveness and power consumption. Experimental results show that by performing low-complexity feature extraction at the sensor side and transmitting only the features that are pertinent to seizure detection to the server, a considerable overall saving in power is achieved. The battery life of the system is increased by 14 times, while the same seizure detection rate as the conventional approach (95%) is maintained.

Noisy galvanic vestibular stimulation has been associated with numerous cognitive and behavioural effects, such as enhancement of visual memory in healthy individuals, improvement of visual deficits in stroke patients, as well as possibly improvement of motor function in Parkinson's disease; yet, the mechanism of action is unclear. Since Parkinson's and other neuropsychiatric diseases are characterized by maladaptive dynamics of brain rhythms, we investigated whether noisy galvanic vestibular stimulation was associated with measurable changes in EEG oscillatory rhythms within theta (4-7.5 Hz), low alpha (8-10 Hz), high alpha (10.5-12 Hz), beta (13-30 Hz) and gamma (31-50 Hz) bands. We recorded the EEG while simultaneously delivering noisy bilateral, bipolar stimulation at varying intensities of imperceptible currents - at 10, 26, 42, 58, 74 and 90% of sensory threshold - to ten neurologically healthy subjects. Using standard spectral analysis, we investigated the transient aftereffects of noisy stimulation on rhythms. Subsequently, using robust artifact rejection techniques and the Least Absolute Shrinkage Selection Operator regression and cross-validation, we assessed the combinations of channels and power spectral features within each EEG frequency band that were linearly related with stimulus intensity. We show that noisy galvanic vestibular stimulation predominantly leads to a mild suppression of gamma power in lateral regions immediately after stimulation, followed by delayed increase in beta and gamma power in frontal regions approximately 20-25 s after stimulation ceased. Ongoing changes in the power of each oscillatory band throughout frontal, central/parietal, occipital and bilateral electrodes predicted the intensity of galvanic vestibular stimulation in a stimulus-dependent manner, demonstrating linear effects of stimulation on brain rhythms. We propose that modulation of neural oscillations is a potential mechanism for the previously-described cognitive and motor effects of vestibular stimulation, and noisy galvanic vestibular stimulation may provide an additional non-invasive means for neuromodulation of functional brain networks.

Wireless electroencephalogram (EEG) systems have become an increasingly important tool for the diagnosis and management of epilepsy. One major consideration in using such a wireless EEG-based system is the stringent battery energy constraint at the sensor side. Different solutions to reduce the power consumption are therefore highly desired. Conventionally, the entire EEG signals acquired by the sensor nodes are continuously streamed to an external data server (where seizure detection is carried out). Such approach incurs a high power consumption, which substantially limits the battery life of the sensor node. In this study, we examine the use of data reduction techniques, including compressive sensing-based EEG compression and various low-complexity feature extraction techniques, for reducing the amount of data that has to be transmitted and thereby reducing the required power consumption at the sensor side. The performance of such techniques is evaluated in terms of power consumption and seizure detection efficacy. Results show that by extracting and transmitting only the nonlinear autocorrelation features of the EEG signals to the server, the battery life of the system is increased by 14 times relative to the conventional approach of transmitting the entire EEG signals, while the same seizure detection performance is maintained (94.1% sensitivity and 99.9% specificity).

Electroencephalography (EEG) and simultaneously-recorded electromyography (EMG) data are a means to assess integrity of the functional connection between the cortex and the muscle during movement. EEG-EMG coupling is typically assessed with pair-wise squared coherence, resulting in a small, but statistically-significant coherence between a single EEG and a single EMG channel. However, a means to combine results across subjects is not straightforward with this approach because the exact frequency of maximal EEG–EMG coupling may vary between individuals, and it emphasizes the role of an individual locus in the brain in driving the muscle activity, when interactions between head regions may in fact be more influential on ongoing EMG activity. To deal with these issues, we implemented a multiblock Partial Least Squares (mbPLS) procedure, previously proposed in chemical applications, which incorporates a hierarchical structure into the ordinary two-block PLS often used in neuroimaging studies. In the current implementation, each subject's data features are collected in individual data blocks on a sub-level, while simultaneously aggregating the sub-level information to obtain a super-level group “consensus”. We further extended the mbPLS model to include 3-dimensional matrices: time–frequency–EEG channel and a time–frequency–connection utilizing Partial Directed Coherence (PDC). We applied the proposed method to concurrent EEG and EMG data collected from ten normal subjects and nine patients with mild–moderate Parkinson's disease (PD) performing a dynamic motor task—that of sinusoidal squeezing. The results demonstrate that connections between EEG electrodes, rather than activity at individual electrodes, correspond more closely to ongoing EMG activity. In PD subjects, there was enhanced connectivity to and from occipital regions, likely related to the previously-described enhanced use of visual information during motor performance in this group. The proposed mbPLS framework is a promising technique for performing multi-subject, multi-modal data analysis and it allows for robust group inferences even in the face of large inter-subject variability.

Studying brain connectivity has provided new insights to the understanding of brain function. While connectivity measures are conventionally computed from electroencephalogram (EEG) signals directly, the presence of volume conduction represents a serious confound affecting interpretation of results. A common solution is to use a two-stage approach which involves estimating underlying brain sources from scalp EEG recordings and subsequently estimating the connectivity between the inferred sources. Recently, a state-space framework which jointly models the instantaneous mixing effects of volume conduction and the causal relationships between underlying brain sources is proposed. In this paper, we extend the state-space framework and model the source activity by a generalized multivariate autoregressive (mAR) process with possibly non-Gaussian noise. A maximum likelihood estimation approach is developed which allows simultaneous estimation of both the mixing matrix and AR model parameters directly from scalp EEG. The proposed technique was verified with simulated EEG data generated using the single-shell spherical head model and demonstrated improved estimation accuracies compared to conventional two-stage connectivity estimation approaches. Furthermore, the proposed technique was applied to EEG data collected from normal and Parkinson's subjects performing a right-handed force-tracking task with differing amounts of visual feedback. The partial directed coherence (PDC) between sources showed significant differences between groups and conditions. These results suggest that the proposed technique is a powerful method to extract connectivity information from EEG recordings.

Recent animal studies have suggested that cortical areas may play a greater role in the modulation of abnormal oscillatory activity in Parkinson's disease (PD) than previously recognized. We investigated task and medication-dependent, EEG-based directional cortical connectivity in the θ (4–7Hz), α (8–12Hz), β (13–30Hz) and low γ (31–50Hz) frequency bands in 10 PD subjects and 10 age-matched controls. All subjects performed a visually guided task previously shown to modulate abnormal oscillatory activity in PD subjects. We examined the connectivity in the simultaneously-recorded EEG between 5 electrode regions of interest (fronto-central, left and right sensorimotor, central and occipital) using a sparse, multivariate, autoregressive-based partial directed coherence method. For comparison, we utilized traditional Fourier analysis to evaluate task-dependent frequency spectra modulation in these same regions. While the spectral analysis revealed some overall differences between PD and control subjects, it demonstrated relatively modest changes between regions. In contrast, the partial directed coherence-based analysis revealed multifaceted, regionally and directionally-dependent alterations of connectivity in PD subjects during both movement preparation and execution. Connectivity was particularly altered posteriorly, suggesting abnormalities in visual and visuo-motor processing in PD. Moreover, connectivity measures in the α, β and low γ frequency ranges correlated with motor Unified Parkinson's Disease Rating Scores in PD subjects withdrawn from medication. Levodopa administration only partially restored connectivity, and in some cases resulted in further exacerbation of abnormalities. Our results support the notion that PD is associated with significant alterations in connectivity between brain regions, and that these changes can be non-invasively detected in the EEG using partial directed coherence methods. Thus, the role of EEG to monitor PD may need to be further expanded.

To explore the cross-information in multi-modal data, several multivariate data fusion techniques have been proposed. Partial least square (PLS) has great potential for neuroimaging studies. However, when performing group analysis with PLS, the presence of inter-subject variability makes the conventional technique of simply pooling data from different subjects problematic. To circumvent this issue, we introduce the idea of multiblock PLS (mbPLS) which incorporates a hierarchical structure into the ordinary two-block PLS. The mbPLS model groups each subject's detail in individual data blocks on the sub-level, while aggregates the sub-level information to obtain a group “consensus” on the super-level. With the hierarchical, two-level design, the mbPLS provides a trade-off between modeling simplicity and preservation of subject specificity. We applied the proposed mbPLS method to concurrent EEG and EMG data collected from normal subjects and patients with Parkinson's disease. The decomposition identifies active brain rhythms and spatial activation pattern involved in the generation of the extracted EEG temporal patterns that show high correlation with EMG signals. The proposed mbPLS framework is a promising technique for performing multi-subject, multi-modal data analysis and it allows for robust group inferences even in the face of large inter-subject variability.

Partial directed coherence (PDC) has recently been proposed for studying brain connectivity in EEG studies. PDC provides a quantitative spectral measure of the causal relations between signals by its central use of a multivariate autoregressive (mAR) model. Yet, in real applications, the successful estimation of PDC depends on the accuracy of mAR parameter estimation, which is often sensitive to the data size and model order. In addition, it is generally believed that connections between EEG nodes (brain regions) may be sparse. To address these concerns, we propose a sparse mAR-based PDC technique where PDC estimates are computed from sparse mAR coefficient matrices derived from penalized regression. The proposed technique is applied to both simulated data and real EEG recordings, and results show enhanced stability and accuracy of the proposed technique compared to the traditional, non-sparse approach. The sparse mAR-based PDC technique is promising for analyzing brain connectivity in EEG analysis.

Recent research efforts in studying brain connectivity has provided new perspectives to understanding of neurophysiology of brain function. Connectivity measures are typically computed from electroencephalogram (EEG) signals, yet the presence of volume conduction makes interpretation of results difficult. One possible alternative is to model the connectivity in the source space. In this study, we proposed a novel source separation technique in which EEG signals are represented as a state-space framework. The framework jointly models the underlying brain sources and the connectivity between them in the form of a generalized autoregressive (AR) process. The proposed technique was applied to real EEG data collected from normal and Parkinson's patients during a motor task. The extracted sources revealed the abnormal beta activity in Parkinson's subjects and showed similar biological networks as previous studies.

To investigate the effects of stroke and hand dominance on muscle association patterns during reaching movements, we applied the hidden Markov model, multivariate autoregressive (HMM-mAR) framework to real sEMG recordings from healthy and stroke subjects performing reaching tasks. Statistical analysis is performed to construct subject- and group-level muscle connectivity networks. Associating structural features are extracted for subsequent classification of reaching movements. The HMM-mAR framework is shown to be able to consistently segments each reaching movement into the initial phase and the full-movement phase. The inferred muscle networks illustrate that healthy and stroke subjects use distinguishably different muscle synergies during the initial phase. The classification results further confirm that structural features extracted from the initial phase are useful in classifying subjects with differing stroke condition and handedness.

In this letter, we propose an eigenspectra-based feature extraction technique for classification of multivariate surface electromyographic (sEMG) recordings. The proposed method exploits the maximum eigenvalue vectors of the time-varying covariance patterns between sEMG channels. Together with a support vector machine (SVM) classifier, the proposed feature extraction technique is shown to be more reliable and robust, and it enhances classification between stroke and normal subjects, compared to the conventional univariate analysis methods that examine each muscle individually. In addition, analysis results show that the spatial whitening operation enhances the discriminability of eigenspectral features. This simple, easily-implemented, biologically-inspired approach is able to succinctly capture the subtle differences in muscle recruitment patterns between healthy and disease states. It appears to be a promising means to monitor motor performance in disease subjects.

As the primary method of measuring muscle activation, the surface electromyography (sEMG) is of great importance in the study of motor deficits seen in patients with brain injuries and neuromuscular disorders. While clinicians have long intuitively understood that deficits in motor control are related to inappropriate recruitment of muscle synergies across several muscles, sEMG recordings are still typically examined in a univariate fashion. However, most traditional univariate techniques are unable to quantitatively capture the complex interactions between muscles during natural movements. To address this issue, multivariate signal processing techniques are employed in this thesis to study muscle co-activation patterns in patient populations. A method for classification of multivariate sEMG recordings between stroke and healthy subjects is proposed. The proposed classification scheme utilizes the eigenspectra of timevarying covariance patterns between sEMG channels as feature vectors and the support vector machines (SVM) as classifiers. Despite the minimal differences in the RMS profiles of individual muscles, the proposed scheme is able to effectively differentiate between healthy and stroke subjects. Moreover, the classification rate is shown to be monotonically related to the severity of motor impairment. This simple, biologically-inspired approach is able to quantitatively capture the subtle differences in muscle recruitment patterns between two populations and appears to be a promising means to measure motor performance. The other approach to modeling multivariate sEMG utilizes the HMM-mAR framework, which combines hidden Markov models (HMMs) and multivariate autoregressive (mAR) models. Different forms of sEMG data are analyzed, including raw sEMG, amplitude sEMG and carrier sEMG. The classification between healthy and stroke subjects is performed

Surface electromyographic (sEMG) analysis is complicated by the fact that the data are inherently non- stationary. To deal with this and to determine muscle activity patterns during reaching movements, we proposed modeling sEMG with a hidden Markov model-multivariate autoregressive (HMM-mAR) framework. The classification between healthy and stroke subjects was performed using structural features extracted from HMM-mAR models. Both the raw and carrier data produced excellent classification performance. The proposed method represents a fundamental departure from most existing methods where only the amplitude is analyzed or the mAR coefficients are directly used for classification. In contrast, our analysis shows that structural features of the multivariate sEMG carrier data or the residuals after model fitting can enhance the classification of reaching movements.

A method for classification of sEMG recordings based on the time-varying covariance patterns between sEMG muscle channels is proposed. The proposed eigenspectral feature vector appears to enhance classification of sEMG patterns with an SVM classifier. The method is shown to be more reliable, robust and enhances classification between stroke and normal subjects, compared to standard analysis methods that examine each muscle individually. This simple, easily-implemented, biologically-inspired approach appears to be a promising means to monitor motor performance in healthy and disease subjects

The security policies of the UBC wireless network are concerned with origin integrity, i.e. authentication, and availability. To enforce origin integrity, users of the UBC wireless network are required to login via one of the three types of authentication mechanisms, known as Quick-Connect, VPN- PPTP and VPN-IPsec. If Quick-Connect is used, we analyzed that an adversary is able to use the authorized user's login to access the network without his knowledge. If VPN-PPTP is used, we analyzed that an adversary may be able to recover the authorized user's account name and password, and hence will be able to access the network. We analyzed that the group password used in the VPN-IPsec may be recovered, which may then lead to a man-in-the-middle and further attacks. To enforce availability, monitoring systems are setup to log users' consumption of bandwidth. However, the correctness of the log is dependent on the correctness of the authentication mechanisms, which are vulnerable. Hence the log may be incorrect and may lead to actions taken against innocent users for over-consumption of bandwidth. We conclude that the security policies may be breached due to the vulnerabilities of the authentication mechanisms, and follow up with discussions to rectify them.