Role of primary sensorimotor cortex and supplementary motor area in volitional swallowing: a movement-related cortical potential study

We investigated the role of the cerebral cortex, particularly the face/tongue area of the primary sensorimotor (SMI) cortex (face/tongue) and supplementary motor area (SMA), in volitional swallowing by recording movement-related cortical potentials (MRCPs). MRCPs with swallowing and tongue protrusion were recorded from scalp electrodes in eight normal right-handed subjects and from implanted subdural electrodes in six epilepsy patients. The experiment by scalp EEG in normal subjects revealed that premovement Bereitschaftspotentials (BP) activity for swallowing was largest at the vertex and lateralized to either hemisphere in the central area. The experiment by epicortical EEG in patients confirmed that face/tongue SMI and SMA were commonly involved in swallowing and tongue protrusion with overlapping distribution and interindividual variability. BP amplitude showed no difference between swallowing and tongue movements, either at face/tongue SMI or at SMA, whereas postmovement potential (PMP) was significantly larger in tongue protrusion than in swallowing only at face/tongue SMI. BP occurred earlier in swallowing than in tongue protrusion. Comparison between face/tongue SMI and SMA did not show any difference with regard to BP and PMP amplitude or BP onset time in either task. The preparatory role of the cerebral cortex in swallowing was similar to that in tongue movement, except for earlier activation in swallowing. Postmovement processing of swallowing was lesser than that of tongue movement in face/tongue SMI; probably suggesting that the cerebral cortex does not play a significant role in postmovement processing of swallowing. SMA plays a supplementary role to face/tongue SMI both in swallowing and tongue movements.     

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages

ABSTRACT

We compared performance in deriving sleep variables by both Fitbit Charge 2?, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh’ IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.     

Amplitude-frequency modulation of electroencephalogram associated with rhythmic movements

Evoked, motor and final potentials and some other EEG phenomena are suggested as additional components accompanying movements, external stimuli, imagination etc. Obviously, the EEG reactions are not restricted to them. On the basis of the method of synchronic averaging a way for detecting the amplitude-frequency modulation (AFM) related to the repeated movements is proposed. This method permitted to reliably single out the EEG effects which sometimes were detected by visual analysis. As the experiments showed the depth of AFM EEG accompanying the movements was about 3-6% (in this case spontaneous AFM plays the role of noise and equals 50% or more). Relations between changes in AFM for EEG recorded from various points, as well as for EEG rhythms were investigated.     

Improving the Performance of an EEG-Based Motor Imagery Brain Computer Interface Using Task Evoked Changes in Pupil Diameter

For individuals with high degrees of motor disability or locked-in syndrome, it is impractical or impossible to use mechanical switches to interact with electronic devices. Brain computer interfaces (BCIs) can use motor imagery to detect interaction intention from users but lack the accuracy of mechanical switches. Hence, there exists a strong need to improve the accuracy of EEG-based motor imagery BCIs attempting to implement an on/off switch. Here, we investigate how monitoring the pupil diameter of a person as a psycho-physiological parameter in addition to traditional EEG channels can improve the classification accuracy of a switch-like BCI. We have recently noticed in our lab (work not yet published) how motor imagery is associated with increases in pupil diameter when compared to a control rest condition. The pupil diameter parameter is easily accessible through video oculography since most gaze tracking systems report pupil diameter invariant to head position. We performed a user study with 30 participants using a typical EEG based motor imagery BCI. We used common spatial patterns to separate motor imagery, signaling movement intention, from a rest control condition. By monitoring the pupil diameter of the user and using this parameter as an additional feature, we show that the performance of the classifier trying to discriminate motor imagery from a control condition improves over the traditional approach using just EEG derived features. Given the limitations of EEG to construct highly robust and reliable BCIs, we postulate that multi-modal approaches, such as the one presented here that monitor several psycho-physiological parameters, can be a successful strategy in making BCIs more accurate and less vulnerable to constraints such as requirements for long training sessions or high signal to noise ratio of electrode channels.     

Force-dependent changes in movement-related cortical potentials

The purpose of this study was to compare movement-related cortical potentials (MRCPs) associated with different levels of isometric contractions by elbow flexors. Eight healthy, right-handed male subjects participated in this study and performed different levels (10 and 50% of maximal voluntary contraction) of isometric contractions by the right elbow flexors. Electroencephalogram (EEG) signals were recorded from Fz, C3, Cz and C4 of the international 10/20 system. Motor potential (MP) amplitudes (from −200 to approximately −50 ms before force onset) for C3 associated with both force generations was significantly greater (P < 0.01) than those for C4, indicating that contralateral predominance of MRCP was observed in the right arm flexion. In Fz, the potentials of negative slope (NS′) (from −600 to approximately −200 ms) and MPs for 50% MVC were significantly greater than those of 10% MVC. In Cz, the MP associated with 50% MVC revealed a significantly greater (P < 0.05) value than that with 10% MVC. In C3 and C4, the MP associated with 50% MVC tended to be greater than that with 10% MVC, but no statistically significant differences were found. These force-dependent changes in MRCPs imply increased activation of neural circuits involved in motor preparation and initiation. It is therefore suggested that the larger potentials from Fz and Cz for 50% MVC compared with 10% MVC reflect a greater activation of supplementary motor area for the preparation of the larger force generation.     

Scale-Free Music of the Brain

Background

There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG.

Methodology/Principal Findings

In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG) waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner''s law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM) and slow-wave sleep (SWS). The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN), 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(κ = 0.800, P<0.001). We also applied the method to the EEG data from eyes closed, eyes open and epileptic EEG, and the results showed these mental states can be identified by listeners.

Conclusions/Significance

The sonification rules may identify the mental states of the brain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy.     

Pan politicus. Review of Chimpanzee Politics: Power and Sex Among rhe Apes,by Frans de Waal. New York,Harper & Row, 1982, 223 pp., $16.50

This report describes periodic oscillations in electroencephalographic (EEG) and behavioral activity with a cycle length of 15–30 seconds in chair-restrained squirrel monkeys (Saimiri sciureus). These oscillations consisted of alternating episodes of vigilance, characterized by visual scanning and motor movement, and inattentiveness, characterized by behavioral quiescence with little eye or limb movement. During vigilance the EEG exhibited low-amplitude, high-frequency (> 16 Hz) activity. During quiescent periods, a high-amplitude synchronized EEG was present with activity in the 8–16-Hz band predominating. The presence or frequency of this EEG and behavioral periodicity was not modified by time of day, as no difference was found between morning and afternoon recording sessions. Although the factors or mechanisms responsible for this rhythm are unclear, it should be noted by those investigators studying the behavior or neurophysiology of Saimiri sciureus in the laboratory setting.     

Electro/magnetoencephalography beamforming with spatial restrictions based on sparsity

We present a source localization method for electroencephalographic (EEG) and magnetoencephalographic (MEG) data which is based on an estimate of the sparsity obtained through the eigencanceler (EIG), which is a spatial filter whose weights are constrained to lie in the noise subspace. The EIG provides rejection of directional interferences while minimizing noise contributions and maintaining specified beam pattern constraints. In our case, the EIG is used to estimate the sparsity of the signal as a function of the position, then we use this information to spatially restrict the neural sources to locations out of the sparsity maxima. As proof of the concept, we incorporate this restriction in the “classical” linearly constrained minimum variance (LCMV) source localization approach in order to enhance its performance. We present numerical examples to evaluate the proposed method using realistically simulated EEG/MEG data for different signal-to-noise (SNR) conditions and various levels of correlation between sources, as well as real EEG/MEG measurements of median nerve stimulation. Our results show that the proposed method has the potential of reducing the bias on the search of neural sources in the classical approach, as well as making it more effective in localizing correlated sources.     

Detection of Seizure Event and Its Onset/Offset Using Orthonormal Triadic Wavelet Based Features

Background

Epileptic seizures are unpredictable in nature and its quick detection is important for immediate treatment of patients. In last few decades researchers have proposed different algorithms for onset and offset detection of seizure using Electroencephalogram (EEG) signals.

Energy analysis reveals the negative effect of delays in passive movement mirror therapy

Wavelet transform energy analyses of the mean and standard error of the electromyogram (EMG) and electroencephalogram (EEG) of eight subjects were investigated in passive movement mirror therapies with no delay (in-phase) and with delay (out-of-phase) situations in two frequency bands of 7.81–15.62 and 15.62–31.25?Hz. It was found that the energy levels of EEG at electrode C4 in the in-phase situation were lower than those in out-of-phase situations, while the energy levels of flexor and extensor forearm muscle groups were larger. With two exceptions, this pattern could be seen in all other subjects. The difference between the in-phase (D0) and out-of-phase situations (D025 and D05) for the frequency range of 15.62–31.25?Hz was found to be significant at a significance level of 0.05 (paired t-test analysis). The respective elevation and decline of EEG and EGM with regard to the increase of the delay may indicate the necessity for synchronization of passive movement and mirror therapy.     

A new method to detect event-related potentials based on Pearson’s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.  Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise.  The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP’s waveform, these waveforms being time- and phase-locked.  In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson’s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase—in consonance with the stimuli—in EEG signal correlation over all channels.  This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs.  These hidden components seem to be caused by variations (between each successive stimulus) of the ERP’s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology.  The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language.     

Behavioral management of epileptic seizures following EEG biofeedback training of the sensorimotor rhythm

Eight severely epileptic patients, four males and four females, ranging in age from 10 to 29 years, were trained to increase 12–14 Hz EEG activity from the regions overlying the Rolandic area. This activity, the sensorimotor rhythm(SMR), has been hypothesized to be related to motor inhibitory processes(Sterman, 1974). The patients represented a crosssection of several different types of epilepsy, including grand mal, myoclonic, akinetic, focal, and psychomotor types. Three of them had varying degrees of mental retardation. SMR was detected by a combination of an analog filtering system and digital processing. Feedback, both auditory and/or visual, was provided whenever one-half second of 12–14-Hz activity was detected in the EEG. Patients were provided with additional feedback keyed by the output of a 4–7-Hz filter which indicated the presence of epileptiform spike activity, slow waves, or movement. Feedback for SMR was inhibited whenever slow-wave activity spikes or movement was also present. During the treatment period most of the patients showed varying degrees of improvement. Two of the patients who had been severely epileptic, having multiple seizures per week, have been seizure free for periods of up to 1 month. Other patients have developed the ability to block many of their seizures. Seizure intensity and duration have also decreased. Furthermore, the successful patients demonstrated an increase in the amount of SMR and an increase in amplitude of SMR during the training period. Spectral analyses for the EEGs were performed periodically. The effectiveness of SMR conditioning for the control of epileptic seizures is evaluated in terms of patient characteristics and type of seizures.     

EEG-based estimation of mental fatigue by using KPCA–HMM and complexity parameters

Two complexity parameters of EEG, i.e. approximate entropy (ApEn) and Kolmogorov complexity (Kc) are utilized to characterize the complexity and irregularity of EEG data under the different mental fatigue states. Then the kernel principal component analysis (KPCA) and Hidden Markov Model (HMM) are combined to differentiate two mental fatigue states. The KPCA algorithm is employed to extract nonlinear features from the complexity parameters of EEG and improve the generalization performance of HMM. The investigation suggests that ApEn and Kc can effectively describe the dynamic complexity of EEG, which is strongly correlated with mental fatigue. Both complexity parameters are significantly decreased (P < 0.005) as the mental fatigue level increases. These complexity parameters may be used as the indices of the mental fatigue level. Moreover, the joint KPCA–HMM method can effectively reduce the dimensionality of the feature vectors, accelerate the classification speed and achieve higher classification accuracy (84%) of mental fatigue. Hence KPCA–HMM could be a promising model for the estimation of mental fatigue.     

Pixel by pixel: real-time observation and quantification of passive flotation speeds of three common equine endoparasite egg types

The efficacy of anthelmintic treatments against populations of endoparasites infecting livestock throughout the world is decreasing. To mitigate this, the use of fecal egg counts is recommended to determine both the necessity, and to ensure the appropriate choice, of anthelmintic treatment. Traditionally, and in order to facilitate easier identification and/or enumeration, samples are analysed after separating eggs from other fecal particulates by exposing them to a solution with a density higher than that of the eggs, but lower than the remaining fecal contents. While many parasite egg flotation protocols exist, little is known about the characteristics of these eggs with respect to their movement through a flotation solution. In this study, we have demonstrated a novel method for the observation and quantification of microscopic (65–100 µm) objects as they experience unassisted flotation. This also represents, to our knowledge for the first time, that the flotation of parasite eggs has been observed and their movement characteristics quantified as they float through solution. Particle tracking and video analysis software were utilised to automatically detect and track the movement of individual eggs as they floated. Three 30 s videos and one 2 min video of each egg type were analysed. If the first 30 s of video were discounted, the differences in mean flotation speed among all videos was statistically significant between egg types (P = 0.0004). Strongyle type eggs (n = 201) moved the fastest with a mean 51.08 µm/s (95% confidence interval: 47.54–54.62). This was followed by Parascaris spp. (n = 131) and Anoplocephala perfoliata eggs (n = 322), with mean speeds of 44.43 µm/s (95% confidence interval: 39.47–49.4) and 31.11 µm/s (95% confidence interval: 29.6–32.61), respectively. This method for evaluating the mean speed of passive flotation may represent a first step towards further optimizing fecal egg flotation and be of interest to parasitologists and veterinary practitioners.     

Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer’s intention to stimulate a rat’s brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer’s intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.     

Automated EEG preprocessing during anaesthesia: new aspects using artificial neural networks.

The computer-aided detection of artefacts became an essential task with increasing automation of quantitative electroencephalogram (EEG) analysis during anaesthesiological applications. The different algorithms published so far required individual manual adjustment or have been based on limited decision criteria. In this study, we developed an artificial neural networks-(ANN-)aided method for automated detection of artefacts and EEG suppression periods. 72 hr EEG recorded before, during and after anaesthesia with propofol have been evaluated. Selected parameterized patterns of 0.25 s length were used to train the ANN (22 input, 8 hidden and 4 output neurons) with error back propagation. The detection performance of the ANN-aided method was tested with processing epochs between 1 to10 s. Related to examiner EEG evaluation, the average detection performance of the method was 72% sensitivity and 80% specificity for artefacts and 90% sensitivity and 92% specificity for EEG suppression. The improvement in signal-to-noise ratio with automated artefact processing was 1.39 times for the spectral edge frequency 95 (SEF95) and 1.89 times for the approximate entropy (ApEn). We conclude that ANN-aided preprocessing provide an useful tool for automated EEG evaluation in anaesthesiological applications.     

Plasma choline-containing phospholipids: potential biomarkers for colorectal cancer progression

Colorectal cancer (CRC) is believed to progress through the adenoma–carcinoma sequence. The adenoma–carcinoma transition is an important window for early detection and intervention of CRC. In the present study, plasma samples from patients with CRC (n = 120), patients with adenomatous polyps (AP) (n = 120), and healthy controls (n = 120) were collected. Plasma phospholipid levels were analyzed with liquid chromatography–tandem mass spectrometry. It was found that the plasma levels of major lysophosphatidylcholine (LPC) species were gradationally decreased from healthy controls, AP to CRC subjects. A formula including total saturated LPCs, 18:2 LPC and sphingosylphosphorylcholine (SPC) yielded a sensitivity and specificity of 88.3 and 80 % for separating CRC from healthy controls. An optimized model with total saturated LPCs, 20:4 LPC and sphingomyelins (SM) as markers yielded a sensitivity and specificity of 89 and 80 % for separating AP from the healthy controls. Moreover, with SM, SPC and saturated LPCs as markers, a model was made to separate CRC from AP with the sensitivity and specificity of 90 and 92.5 %, respectively. These data indicate that the plasma choline-containing phospholipid levels represent potential biomarkers to distinguish between healthy controls, AP and CRC cases, implying their clinical usage in CRC and/or AP-CRC progression detection.     

Effect of adaptive motion-artifact reduction on QRS detection

Motion artifact resulting from electrode and patient movement is a significant source of noise in ECG, EEG, EMG, and impedance pneumography recording. Noise resulting from motion is particularly troublesome in ambulatory ECG recordings, such as those made during Holter monitoring or stress tests, because the bandwidth of the motion artifact overlaps with the ECG signal bandwidth. The authors investigated the effect of an adaptive motion-artifact removal algorithm on the performance of a standard QRS detector. They made four ECG recordings on each of the three subjects while manually generating artifact. Adaptive noise removal was applied to the ECG signal using a skin-stretch signal as the noise reference. Adaptive noise removal reduced the number of false QRS detections in the records from 380 to 104, for an average reduction in false detections of 72.6%. False-detection reductions for individual records ranged from 12% to 93%.     

Investigation of EEG abnormalities in the early stage of Parkinson’s disease

The objective of the present study was to investigate brain activity abnormalities in the early stage of Parkinson’s disease (PD). To achieve this goal, eyes-closed resting state electroencephalography (EEG) signals were recorded from 15 early-stage PD patients and 15 age-matched healthy controls. The AR Burg method and the wavelet packet entropy (WPE) method were used to characterize EEG signals in different frequency bands between the groups, respectively. In the case of the AR Burg method, an increase of relative powers in the δ- and θ-band, and a decrease of relative powers in the α- and β-band were observed for patients compared with controls. For the WPE method, EEG signals from patients showed significant higher entropy over the global frequency domain. Furthermore, WPE in the γ-band of patients was higher than that of controls, while WPE in the δ-, θ-, α- and β-band were all lower. All of these changes in EEG dynamics may represent early signs of cortical dysfunction, which have potential use as biomarkers of PD in the early stage. Our findings may be further used for early intervention and early diagnosis of PD.     

A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses

This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system. Such a system employs completely integrated textile electrodes to monitor ECG and is also equipped with a triaxial accelerometer for movement monitoring. In the literature, the most used technique to remove movement artifacts, when noise bandwidth overlaps the primary source bandwidth, is the adaptive filter. In this study we propose a new algorithm, hereinafter called Stationary Wavelet Movement Artifact Reduction (SWMAR), where the Stationary Wavelet Transform (SWT) decomposition algorithm is employed to identify and remove movement artifacts from ECG signals in horses. A comparative analysis with the Normalized Least Mean Square Adaptive Filter technique (NLMSAF) is performed as well. Results achieved on seven hours of recordings showed a reduction greater than 40% of MA percentage (between before- and after- the application of the proposed algorithm). Moreover, the comparative analysis with the NLMSAF, applied to the same ECG recordings, showed a greater reduction of MA percentage in favour of SWMAR with a statistical significant difference (p–value < 0.0.5).