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Burn patients often face elevated pain, anxiety, and depression levels. Music therapy adds to integrative care in burn patients, but research including electrophysiological measures is limited. This study reports electrophysiological signals analysis during Music-Assisted Relaxation (MAR) with burn patients in the Intensive Care Unit (ICU). This study is a sub-analysis of an ongoing trial of music therapy with burn patients in the ICU. Electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) were recorded during MAR with nine burn patients. Additionally, background pain levels (VAS) and anxiety and depression levels (HADS) were assessed. EEG oscillation power showed statistically significant changes in the delta (p < 0.05), theta (p = 0.01), beta (p < 0.05), and alpha (p = 0.05) bands during music therapy. Heart rate variability tachograms high-frequencies increased (p = 0.014), and low-frequencies decreased (p = 0.046). Facial EMG mean frequency decreased (p = 0.01). VAS and HADS scores decreased - 0.76 (p = 0.4) and - 3.375 points (p = 0.37) respectively. Our results indicate parasympathetic system activity, attention shifts, reduced muscle tone, and a relaxed state of mind during MAR. This hints at potential mechanisms of music therapy but needs to be confirmed in larger studies. Electrophysiological changes during music therapy highlight its clinical relevance as a complementary treatment for ICU burn patients.Trial registration: Clinicaltrials.gov (NCT04571255). Registered September 24th, 2020. https//classic.clinicaltrials.gov/ct2/show/NCT04571255.

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Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.

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Recent engineering and neuroscience applications have led to the development of brain-computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

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Introduction. In clinical practice, it is difficult to define the prognosis of patients with acute encephalopathy; a syndrome characterized by cognitive dysfunction and altered sensorium. Discharges with triphasic morphology (DTM) are an electroencephalographic pattern that might be useful to establish the risk of death. The aim of this study was to define the prognostic value of DTM regarding mortality in patients with acute encephalopathy. Methods. We conducted an observational retrospective cohort study including patients with acute encephalopathy with and without DTM paired by age and gender in a 1:2 ratio. We calculated the odds ratio (OR) to determine the association between DTM and mortality. In addition, we calculated sensibility, specificity, and predictive values. Results. We included 72 patients, 24 with DTM and 48 without DTM. Mortality was higher in patients with DTM (41.6% vs 14.5%, P = .01). Factors associated with a higher risk of death were DTM (OR = 4.1, 95% confidence interval [CI] 1.3-13, P = .01) and sequential organ failure assessment score (OR = 1.3, 95% CI 1.04-1.67, P = .02). A higher Glasgow coma scale score was associated with a lower risk of death (OR = 0.65, 95% CI 0.51-0.83, P = .001). The sensibility and specificity of DTM were 59% and 75%, respectively. Positive and negative likelihood ratios were 2.36 and 0.55. Discussion. Our results revealed high mortality in patients with acute encephalopathy and DTM. This electroencephalographic pattern was associated with 4 times higher risk of death. However, its usefulness for predicting death was limited.

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ETHNOPHARMACOLOGICAL RELEVANCE: For many centuries, Mexican Valerian (Valeriana edulis ssp. procera) has been an important plant in folk medicine. It has been considered useful to control epilepsy; however, electroencephalographic evidence of its anticonvulsant activity is missing in literature. AIM OF THE STUDY: In the present study, in situ electroencephalographic (EEG) analysis was performed along with administration of a crude ethanol extract of V. edulis and its valepotriate fraction on the pentylenetetrazole (PTZ)-induced convulsive behavior in rats. MATERIALS AND METHODS: Experiments were performed using male Wistar rats with nail-shaped electrodes implanted in the frontal and parietal cortices for EEG recording. All animals received a single dose of PTZ (35 mg/kg, i.p.) to test the anticonvulsant activity of V. edulis crude extract and valepotriate fraction (100 mg/kg, i.p.) 15 and/or 30 min after administration. EEG recordings were obtained from the cortices and were evaluated to assess ictal behavior over 60-75 min. Chromatographic analysis of the valepotriate fraction and in silico predictions of pharmacodynamic properties were also explored. The latency, frequency and duration of seizures evaluated using EEG recordings from the frontal and parietal cortices of rats showed significant changes demonstrating the inhibition of paroxystic activity. RESULTS: The spectral analysis confirmed the reduction of excitatory activity induced by V. edulis extract, which was improved in the presence of the valepotriate fraction as compared to that induced by ethosuximide (a reference anticonvulsant drug). The presence of valepotriates such as: isodihydrovaltrate (18.99%), homovaltrate (13.51%), 10-acetoxy-valtrathydrin (4%) and valtrate (1.34%) was identified by chromatographic analysis. Whereas, not only GABAA receptor participation but also the cannabinoid CB2 receptor was found to be likely involved in the anticonvulsant mechanism of action after in silico prediction. CONCLUSIONS: Our data support the anticonvulsant properties attributed to this plant in folk medicine, due to the presence of valepotriates.

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Removal of artifacts induced by muscle activity is crucial for analysis of the electroencephalogram (EEG), and continues to be a challenge in experiments where the subject may speak, change facial expressions, or move. Ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) has been proven to be an efficient method for denoising of EEG contaminated with muscle artifacts. EEMD-CCA, likewise the majority of algorithms, does not incorporate any statistical information of the artifact, namely, electromyogram (EMG) recorded over the muscles actively contaminating the EEG. In this paper, we propose to extend EEMD-CCA in order to include an EMG array as information to aid the removal of artifacts, assessing the performance gain achieved when the number of EMG channels grow. By filtering adaptively (recursive least squares, EMG array as reference) each component resulting from CCA, we aim to ameliorate the distortion of brain signals induced by artifacts and denoising methods. We simulated several noise scenarios based on a linear contamination model, between real and synthetic EEG and EMG signals, and varied the number of EMG channels available to the filter. Our results exhibit a substantial improvement in the performance as the number of EMG electrodes increase from 2 to 16. Further increasing the number of EMG channels up to 128 did not have a significant impact on the performance. We conclude by recommending the use of EMG electrodes to filter components, as it is a computationally inexpensive enhancement that impacts significantly on performance using only a few electrodes.

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Prepulse inhibition (PPI) test has been widely used to evaluate sensorimotor gating. In humans, deficits in this mechanism are measured through the orbicularis muscle response using electromyography (EMG). Although this mechanism can be modulated by several brain structures and is impaired in some pathologies as schizophrenia and bipolar disorder, neural PPI evaluation is rarely performed in humans. Since eye blinks are a consequence of PPI stimulation, they strongly contaminate the electroencephalogram (EEG) signal. This paper describes a method to reduce muscular artifacts and enable neural PPI assessment through EEG in parallel to muscular PPI evaluation using EMG. Both types of signal were simultaneously recorded in 22 healthy subjects. PPI was evaluated by the acoustical startle response with EMG and by the P2-N1 event-related potential (ERP) using EEG in Fz, Cz, and Pz electrodes. In order to remove EEG artifacts, Independent Component Analysis (ICA) was performed using two methods. Firstly, visual inspection discarded components containing artifact characteristics as ocular and tonic muscle artifacts. The second method used visual inspection as gold standard to validate parameters in an automated component selection using the SASICA algorithm. As an outcome, EEG artifacts were effectively removed and equivalent neural PPI evaluation performance was obtained using both methods, with subjects exhibiting consistent neural as well as muscular PPI. This novel method improves PPI test, enabling neural gating mechanisms assessment within the latency of 100-200 ms, which is not evaluated by other sensory gating tests as P50 and mismatch negativity.

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The main objective of this study was to enhance the performance of sleep stage classification using single-channel electroencephalograms (EEGs), which are highly desirable for many emerging technologies, such as telemedicine and home care. The proposed method consists of decomposing EEGs by a discrete wavelet transform and computing the kurtosis, skewness and variance of its coefficients at selected levels. A random forest predictor is trained to classify each epoch into one of the Rechtschaffen and Kales' stages. By performing a comprehensive set of tests on 106,376 epochs available from the Physionet public database, it is demonstrated that the use of these three statistical moments has enhanced performance when compared to their application in the time domain. Furthermore, the chosen set of features has the advantage of exhibiting a stable classification performance for all scoring systems, i.e., from 2- to 6-state sleep stages. The stability of the feature set is confirmed with ReliefF tests which show a performance reduction when any individual feature is removed, suggesting that this group of feature cannot be further reduced. The accuracies and kappa coefficients yield higher than 90 % and 0.8, respectively, for all of the 2- to 6-state sleep stage classification cases.

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Neuromodulations are an important component of extracellular electrical potentials (EEP), such as the Electroencephalogram (EEG), Electrocorticogram (ECoG) and Local Field Potentials (LFP). This spatially temporal organized multi-frequency transient (phasic) activity reflects the multiscale spatiotemporal synchronization of neuronal populations in response to external stimuli or internal physiological processes. We propose a novel generative statistical model of a single EEP channel, where the collected signal is regarded as the noisy addition of reoccurring, multi-frequency phasic events over time. One of the main advantages of the proposed framework is the exceptional temporal resolution in the time location of the EEP phasic events, e.g., up to the sampling period utilized in the data collection. Therefore, this allows for the first time a description of neuromodulation in EEPs as a Marked Point Process (MPP), represented by their amplitude, center frequency, duration, and time of occurrence. The generative model for the multi-frequency phasic events exploits sparseness and involves a shift-invariant implementation of the clustering technique known as k-means. The cost function incorporates a robust estimation component based on correntropy to mitigate the outliers caused by the inherent noise in the EEP. Lastly, the background EEP activity is explicitly modeled as the non-sparse component of the collected signal to further improve the delineation of the multi-frequency phasic events in time. The framework is validated using two publicly available datasets: the DREAMS sleep spindles database and one of the Brain-Computer Interface (BCI) competition datasets. The results achieve benchmark performance and provide novel quantitative descriptions based on power, event rates and timing in order to assess behavioral correlates beyond the classical power spectrum-based analysis. This opens the possibility for a unifying point process framework of multiscale brain activity where simultaneous recordings of EEP and the underlying single neuron spike activity can be integrated and regarded as marked and simple point processes, respectively.

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Deficits in visual short-term memory (VSTM) binding have been proposed as an early and specific marker for Alzheimer's disease (AD). However, no studies have explored the neural correlates of this domain in clinical categories involving prodromal stages with different risk levels of conversion to AD. We assessed underlying electrophysiological modulations in patients with mild cognitive impairment (MCI), patients in the MCI stages of familial AD carrying the mutation E280A of the presenilin-1 gene (MCI-FAD), and healthy controls. Moreover, we compared the behavioral performance and neural correlates of both patient groups. Participants completed a change-detection VSTM task assessing recognition of changes between shapes or shape-color bindings, presented in two consecutive arrays (i.e., study and test) while event related potentials (ERPs) were recorded. Changes always occurred in the test array and consisted of new features replacing studied features (shape-only) or features swapping across items (shape-color binding). Both MCI and MCI-FAD patients performed worse than controls in the shape-color binding condition. Early electrophysiological activity (100-250 ms) was significantly reduced in both clinical groups, particularly over fronto-central and parieto-occipital regions. However, shape-color binding performance and their reduced neural correlates were similar between MCI and MCI-FAD. Our results support the validity of the VSTM binding test and their neural correlates in the early detection of AD and highlight the importance of studies comparing samples at different risk for AD conversion. The combined analysis of behavioral and ERP data gleaned with the VSTM binding task can offer a valuable memory biomarker for AD.

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