Energy-efficient data reduction techniques for wireless seizure detection systems.

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 modulates the amplitude of EEG synchrony patterns.

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.

A multiblock PLS model of cortico-cortical and corticomuscular interactions in Parkinson's disease.

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.

Altered directional connectivity in Parkinson's disease during performance of a visually guided task.

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), ß (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, ß 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.

EEG source extraction by autoregressive source separation reveals abnormal synchronization in Parkinson's disease.

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.

Study of stroke condition and hand dominance using a hidden Markov, multivariate autoregressive (HMM-mAR) network framework.

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.

Hidden Markov multivariate autoregressive (HMM-mAR) modeling framework for surface electromyography (sEMG) data.

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.