Effects of mirror-box therapy on modulation of sensorimotor EEG oscillatory rhythms: a single-case longitudinal study.

We provide direct electrophysiological evidence that mirror therapy (MT) can change brain activity and aid in the recovery of motor function after stroke. In this longitudinal single-case study, the subject was a 58-yr-old man with right-hand hemiplegia due to ischemic stroke. Over a 9-mo period we treated him with MT twice a week and measured electroencephalograms (EEG) before, during, and after each therapy session. Using advanced signal processing methods, we identified five distinct movement-related oscillatory EEG components: one slow component designated as mu rhythm and four faster components designated as sensorimotor rhythms. Results show that MT produced long-term changes of two oscillatory EEG components including the mu rhythm, which is a well-documented correlate of voluntary movement in the frequency range of 7.5-12 Hz. Specifically, MT was significantly associated with an increase in the power of mu rhythm recorded over both hemispheres and a decrease in the power of one sensorimotor component recorded over the affected hemisphere. To obtain robust, repeatable individual measures of EEG components suitable for longitudinal study, we used irregular-resampling autospectral analysis to separate fractal and oscillatory components in the EEG power spectrum and three-way parallel factor analysis to isolate oscillatory EEG components and track their activations over time. The rhythms were identified over individual days of MT training and were clearly related to the periods of event-related desynchronization and synchronization (rest, observe, and move) during MT. Our results are consistent with a model in which MT promotes recovery of motor function by altering neural activity associated with voluntary movement. NEW & NOTEWORTHY We provide novel evidence that mirror therapy (MT), which helps in the recovery of motor function after a stroke, is also associated with long-lasting changes in brain electrical activity. Using precise measurements of oscillatory EEG components over a 9-mo period in a victim of ischemic stroke, we showed that MT produced long-term increases in the mu rhythm recorded over both hemispheres and a decrease in a sensorimotor EEG component recorded over the affected hemisphere.

On the Individuality of Sleep EEG Spectra.

Research in recent years has supported the hypothesis that many properties of the electroencephalogram (EEG) are specific to an individual. In this study, the intra- and inter-individual variations of sleep EEG signals were investigated. This was carried out by analyzing the stability of the average EEG spectra individually computed for the Rechtschaffen and Kales (RK) sleep stages. Six EEG channels were used to account for the topographical aspect of the analysis. Validity of the results was supported by considering a wide dataset of 174 subjects with normal sleep. Subjects spent two consecutive nights in the sleep laboratory during which EEG recordings were obtained. High similarity between average spectra of two consecutive nights was found considering an individual. More than 89% of the second night recordings were correctly assigned to their counterparts of the first night. The average spectra of sleep EEG computed for each RK sleep stage have shown a high degree of individuality.

Tensor decomposition of human narrowband oscillatory brain activity in frequency, space and time.

Many brain processes in health and disease are associated with modulation of narrowband brain oscillations (NBOs) in the scalp-recorded EEG, which exhibit specific frequency spectra and scalp topography. Isolating and tracking NBOs over time using algorithms is useful in domains such as brain-computer interfaces or when measuring the EEG effects of experimental manipulations. Previously, we successfully applied modified tensor methods for identifying and tracking NBO activity over time or conditions. We introduced frequency and spatial constraints that greatly improved their physiological plausibility. In this paper we rigorously demonstrate the power and precision of tensor methods to separate, isolate and track NBOs using sources simulated with an anatomical forward model. This allows us to control the attributes of NBOs and validate tensor solutions. We find that tensor methods can accurately identify, separate and track NBOs over time, using realistic sources either alone or in combination, and compare favorably to well-known spatio-spectral decomposition methods for NBO estimation.

Profiling continuous sleep representations for better understanding of the dynamic character of normal sleep.

The amount and quality of sleep substantially influences health, daily behaviour and overall quality of life. The main goal of this study was to investigate to what extent sleep structure, as derived from the polysomnographic (PSG) recordings of nocturnal human sleep, can provide information about sleep quality in terms of correlating with a set of variables representing the daytime subjective, neurophysiological and cognitive states of a healthy population without serious sleep problems. We focused on a continuous sleep representation derived from the probabilistic sleep model (PSM), which describes the microstructure of sleep by a set of sleep probabilistic curves representing a finite number of sleep microstates. This contrasts with approaches where sleep is characterised by a set of one-dimensional sleep measures derived from the standard discrete sleep staging. Considering this continuous sleep representation, we aimed to identify typical sleep profiles that represent the dynamic aspect of sleep during the night and that are associated with a set of studied daily life quality measures. Cluster analysis of sleep probabilistic curves has proven to be a helpful tool when identifying specific sleep temporal profiles, but it faces problems when curves are complex and time misalignment is present. To overcome these problems, we proposed and validated a novel 2-step iterative clustering and time alignment method. We compared the quality of alignment and cluster homogeneity produced by the method with existing approaches in which (i) the time alignment of curves precedes the clustering step, and (ii) time alignment and clustering are performed simultaneously. The obtained homogeneous clusters of REM, Wake and Slow Wave Sleep resembled the clustering structure of subjects with significantly different subjective scores of sleep quality and mood, as well as more objective cognitive test scores. Moreover, the sleep profiles associated with individual clusters help to better understand the existing associations between the overnight dynamics of specific sleep states and daily measures.

Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials.

We have developed and tested two electroencephalogram (EEG)-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KPLS classifier to map power spectra of 62-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject's average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: 1) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal electrooculograms (EOG) signals, 2) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from 12 electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle artifact is controlled via adaptive normalization of the SSVEP. Training of the classifier requires about 3 min. We have tested our system in real-time operation in three human subjects. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 s for movement initiation and turning. We have also developed a realistic demonstration of our system for control of a moving map display (http://ti.arc.nasa.gov/).

Multimodal neuroelectric interface development.

We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.

In search of objective components for sleep quality indexing in normal sleep.

The main goal of this study was to investigate to what extent polysomnographic (PSG) recordings of nocturnal human sleep can provide information about sleep quality in terms of correlation with a set of daytime measures. These measures were designed with the aim of comprising selected quality of night sleep and consist of subjective sleep quality ratings, neuropsychological tests and physiological parameters. First, a factor analysis model was applied to the large number of daytime measures of sleep quality in order to detect their latent structure. Secondly, in addition to the gold standard sleep staging method to arrive at variables about sleep architecture from PSG, we applied a recently developed continuous sleep representation by considering the probabilistic sleep model (PSM) describing the microstructure of sleep. Significant correlations between sleep architecture and daytime variables of sleep quality were found. Both the factor analysis and the PSM helped maximize the information about this relationship.

Extracting more information from EEG recordings for a better description of sleep.

We are introducing and validating an EEG data-based model of the sleep process with an arbitrary number of different sleep stages and a high time resolution allowing modeling of sleep microstructure. In contrast to the standard practice of sleep staging, defined by scoring rules, we describe sleep via posterior probabilities of a finite number of states, not necessarily reflecting the traditional sleep stages. To test the proposed probabilistic sleep model (PSM) for validity, we correlate statistics derived from the state posteriors with the results of psychometric tests, physiological variables and questionnaires collected before and after sleep. Considering short, in this study 3s long, data window the PSM allows describing the sleep process on finer time scale in comparison to the traditional sleep staging based on 20 or 30s long data segments visual inspection. By combining sleep states and using two measures derived from the posterior curves we show that the average absolute correlations between the measures and subjective and objective sleep quality measures are considerably higher when compared with the analogous measures derived from hypnograms based on sleep staging. In most cases these differences are significant. The results obtained with the PSM support its wider use in sleep process modeling research and these results also suggest that EEG signals contain more information about sleep than what sleep profiles based on discrete stages can reveal. Therefore the standardized scoring of sleep may not be sufficient to reveal important sleep changes related to subjective and objective sleep quality indexes. The proposed PSM represents a promising alternative.