Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The ...performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.
Although promising from numerous applications, current brain-computer interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the ...non-stationarity of electroencephalographic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning.
Objective: Advances in sensor miniaturization and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may ...impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: A total of 22 healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography recordings. The ear-EEG data were analyzed in the both structural complexity and spectral domains. The extracted features were used for automatic sleep stage prediction through supervized machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification. This is supported by a substantial agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is feasible for monitoring overnight sleep outside the sleep laboratory and also mitigates technical difficulties associated with PSG. It, therefore, represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The "standardized" one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.
Abstract
Introduction
Sleepwalking belongs to a family of disorders (Disorders of Arousal - DOA) that are thought to derive from incomplete arousals out of Non Rapid Eye Movement (NREM) sleep. At ...yet, our knowledge about the specific neural dynamics occurring during clinical episodes is limited and relies on one SPECT case study, four stereo-EEG case reports/series and one single high-density electro-encephalography (hdEEG) case report. We herein describe a single case captured by hdEEG.
Report of Case
We collected two consecutive sleep recordings (using a 256-channel hdEEG coupled with standard video-polysomnography) of a non-medicated, otherwise healthy, 13-year-old male, with a history of recurrent daily sleepwalking episodes. We visually identified 17 behavioral events during sleep stage 3 and divided them into two groups: clear clinical episodes (n = 7) and simple movements associated with burst of delta waves (n = 10). Source power topography in the delta range (1-4 Hz) was computed using LORETA. Source images during selected episodes were compared to 30 second-windows of baseline stage 3 sleep. Comparisons were performed using statistical non-parametric mapping with supra-threshold cluster tests. Events were associated with an increase of delta power over the right frontopolar prefrontal cortex (rPFC) / Broadman area 10 (BA10) at their onset. This finding was clearly observable even when considering only clear-cut events, followed by the involvement of the right dorsolateral and medial prefrontal cortex / BA9 and of the left superior temporal gyrus/ BA 22.
Conclusion
We were able to replicate a recently published case report by our group, where we highlighted the putative role of rPFC and PFC and prefronto-temporal circuit in DOA episodes. Intriguingly, we observed a lateralization of this effect, with a prominent right frontal involvement. Novel research has shown a physiological asymmetry in the generation of large slow waves between the two hemispheres. An increased right-left unbalance might prime behavioral episodes in DOA patients.
Abstract
Introduction
Parasomnia episodes (PE) consist of abnormal behaviors during sleep. Using high-density EEG (HDEEG), we sought to quantify topographical differences in spectral power during PE ...in comparison to wake and sleep.
Methods
17 adult subjects with a history of NREM sleep parasomnia underwent 256-electrode HDEEG recordings during recovery sleep after 25h of sleep deprivation. PE occurred either spontaneously or when triggered by a sound. Data preprocessing of PE, sleep and wake data included filtering at 1-25 Hz, careful epoch and channel selection, and adaptive mixture independent component analysis (AMICA). We compared topographies of delta (slow wave activity, or SWA) and theta power, alpha power, and beta/delta ratio (a marker of cortical arousal) between states using paired t-tests. All results were thresholded at p<0.05 corrected for multiple comparison using statistical non parametric mapping (SNPM).
Results
Clean data were obtained in 26 PE arising out of N2/N3 sleep in 11 subjects. During PE, delta and theta power were significantly higher than during wake but lower than during sleep in central regions (at uncorrected p<0.05 for sleep vs. PE delta power). Occipital alpha was lower during PE compared to wake, but higher during PE compared to sleep. Finally, beta/delta ratio values during PE were globally higher than in wake, but globally lower than during sleep.
Conclusion
The present results confirm and extend our previous findings of decreased SWA in central areas during baseline sleep in patients with NREM sleep PE. They suggest that higher cortical arousal in central regions may precipitate motor behaviors during PE. Alpha power and beta-delta ratio during PE were intermediate between sleep and wake, suggesting that PE are transitional states with an admixture of cortical arousal and cortical sleep. Future analyses will use source reconstruction to identify the cortical generators of observed scalp differences.
Support
This work was funded by the Swiss National Science Foundation and the Tiny Blue Dot foundation.
Abstract
Introduction
The impact of EEG referencing on sleep oscillations, such as spindles and slow oscillations, is largely overlooked across studies. While it is recognized that a topographic head ...plot of EEG activity does not reflect the true location of the underlying cortical activity, spatial distributions, as well as spectral properties and morphology of EEG oscillations can change dramatically as a function of referencing scheme. It is therefore vital to understand the impact of referencing when drawing inferences about the nature of EEG sleep oscillations. In this study, we use MRI structural data to construct subject-specific forward models of EEG signals. Using these models, we can simulate cortical activity and observe its true representation on the scalp. In particular, we simulate spindles and slow wave oscillations and examine how referencing affects topography, spectral power, and phase of oscillations.
Methods
High-density EEG (Brain Vision, 64-channel) polysomnography was performed on 9 healthy young subjects. 3T structural MRI scans were acquired and forward models were built in MNE-Python using 3-shell Boundary Element Models (BEM) based on individual anatomical details processed with Freesurfer. Simulations of various sleep spindle and slow oscillation dynamics were projected to the sensor space. Different referencing schemes (common average, Laplacian, linked-mastoid) were then applied to the experimental and simulated data and analyzed for effects on time-frequency characteristics of sleep oscillations.
Results
Analyses of experimental data showed distinct reference-based differences in topographical distribution of spectral power and phase of oscillations. Simulated data revealed many scenarios in which the spatial distribution of activity the EEG sensor space poorly represented the true location of the underlying source activity. Moreover, there were alterations to the spatial spread and envelope form of sleep spindle events under different referencing schemes despite from identical source activities.
Conclusion
This study shows that spindle and slow oscillation activity is highly variable across referencing schemes and that EEG topographical plots on the scalp may poorly represent cortical activity locations. It is thus vital to consider the choice of referencing when quantifying characteristics of sleep EEG oscillations.
Support
This work was supported by R01 NS-096177.
Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. ...The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.
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