Electroencephalogram based brain concentration and its human computer interface application

BackgroundHighly structured electroencephalography (EEG) oscillations can occur in adults during etomidate-induced general anesthesia, but the link between these two phenomena is poorly understood. Therefore, in the present study, we investigated the electroencephalogram dynamics of etomidate-induced loss of consciousness (LOC) in order to understand the neurological mechanism of etomidate-induced LOC.MethodsThis study is a prospective observational study. Etomidate-induced anesthesia was performed on eligible patients undergoing elective surgery. We analyzed EEG data from 20 patients who received etomidate for the induction of general anesthesia. We used power spectra and coherence methods to process and analyze the EEG data. Our study was based on 4-channel EEG recordings.ResultsCompared with the baseline (awake period), etomidate induced an increase in power in delta, theta, alpha and beta waves during LOC. Compared with the awake period, the delta-wave (1–4 Hz), alpha-wave(8–13 Hz), and theta-wave(4–8 Hz) coherence increased significantly during LOC, while the slow-wave (< 1 Hz) coherence decreased. However, the delta wave (1.0–4.0 Hz) during etomidate-induced LOC was more coherent than during the awake period (1.86–3.17 Hz, two-group test for coherence, p < 0.001).ConclusionsThe neural circuit mechanism of etomidate-induced LOC is closely related to the induction of oscillation in delta, theta, alpha and beta waves and the enhancement of delta-wave coherence.Trial registrationChiCTR1800017110

Background. The method of anterior decompression (discectomy) for monosegmental stenosis of the cervical spinal canal is subjectively assessed as highly effective by both neurosurgeons and the vast majority of patients. However, the objective criteria for the early effectiveness of this intervention have not been sufficiently studied to date. Aim of the study to evaluate the dynamics of electroencephalography parameters in the preoperative and early postoperative period in patients with monosegmental stenosis of the cervical spine. Materials and methods. Electroencephalography studies were conducted on 22 patients of the neurosurgery department, before and after surgical treatment under code A16.23.085.001. The spectral composition of the bioelectrical activity of the brain (alpha, beta, delta and theta rhythms), as well as the coherence of oscillations in selected leads, were studied. Results. In the preoperative period, a disorganized type of electroencephalogramm was recorded in the examined group according to the classification of E.A. Zhirmunskaya; there was a decrease in the index and amplitude of the alpha rhythm in the parietal-occipital leads; predominance of high-amplitude beta activity in the frontal-central leads; presence of diffuse delta activity of the average index and theta activity of the average index, localized in leads P3, P4, C3, C4. On the 5th day after the operation, an organized type of electroencephalography was recorded on the encephalogram; a well-modulated alpha rhythm was observed, with a frequency in the range from 9 to 10 Hz, and an amplitude of 70 to 80 microvolts. Low-amplitude beta rhythm activity was recorded in the frontocentral leads. Low-amplitude delta and theta rhythms were recorded in the parieto-occipital leads and had a low index. Overall, the obtained data indicated positive dynamics in the restoration of cortical rhythms. Conclusion. The positive dynamics of cerebral cortex biorhythms in the early postoperative period allows the use of the electroencephalography method to objectify the results of surgical treatment, and in the future for the purposes of neurorehabilitation.

In this study cyclic changes of human sleep structure were examined. For whole-night polysomnograms of 35 healthy volunteers of both sexes, manual hypnograms were created and divided into NREM-REM cycles. EEG signals from C3-A2 derivation were analysed by computer using a Fast Fourier Transform (FFT). For consecutive NREM-REM cycles of individual sleep stages, EEG power density contents for delta, theta, alpha, sigma and beta waves were analysed. For consecutive sleep cycles, a clear decrease in NREM sleep duration, especially slow wave sleep duration, was obtained. In addition, a decrease in power density of delta waves was observed. For consecutive sleep cycles, increases in REM sleep duration and in power density of theta and alpha waves were obtained. In consecutive sleep cycles, high amplitude delta slow waves are replaced by higher frequency and lower amplitude waves. Thus stages of NREM sleep are replaced by stages of REM.

Relevance of electroencephalogram assessment in amyloid and tau pathology in rat

Objectives: Continuous connected breathing (CCB) is a high-frequency breathing technique without breath retention. This study examines changes in electroencephalogram (EEG) band power and heart rate variability (HRV) before, during and after breathwork intervention of 3 rounds of 40 CCB versus normal breathing in healthy volunteers. Materials and Methods: Thirty-three healthy volunteers, aged between 19 and 23 years, participated in the study (n = 15; female = 7; mean age = 21.5 years) group, while the control group consisted of 18 individuals (n = 18; male = 11; mean age = 19.6 years). After acquiring baseline measures, the study group engaged in 40 CCB practice online for 10 days, while the control group continued with their usual activities. EEG and HRV signals were recorded for both groups after the 10 days. Post-intervention, the study group practised 40 CCB breathwork, while the control group breathed normally during signal acquisition. EEG was recorded for 1-min durations at five different time events (1st, 4th, 5th, 7th and 10th min) according to the protocol. HRV was continuously recorded for the entire 10 min, and 1-min readings corresponding to the EEG recording time intervals were analysed. EEG signals were acquired using an Enobio-8 cap with dry electrodes in pre-frontal (FP1 and FP2), temporal (T7 and T8), central (C3 and C4) and occipital (O1 and O2) regions. HRV data were acquired from Polar H7/H10 chest strap sensors through Bluetooth using Elite HRV app on android phone and data were later processed with Kubios software. EEG signals were processed using MATLAB software to extract EEG power for delta (0– 3.5Hz), theta (4–7 Hz), alpha (8–13 Hz), beta (14–29 Hz) and gamma frequencies (30–50 Hz). The EEG power and HRV signals were later arranged as before-after and during time events and statistically analysed for within and between-group changes using t-tests for significance of means. Results: There was an overall quantitative change in EEG band power of −2 units within the study group and −0.03 within the control group. A significant decrease was found in overall delta (−2.27), theta (−1.7), alpha (−1.8) and beta (−2.27) brain waves in pre-frontal (FP1, FP8), temporal (T7, T8) and occipital (O1, O2) regions after breathwork compared to before within the study group. A significant increase was found in beta (+2.8) band power in the control group with a higher increase in the left compared to the right hemisphere. Bilateral average change ranged from −1.6 to −2.6 in the study and −0.1 to +4.9 in the control group. HRV parameters were reduced after intervention in both groups but without significance. The parameters improved after the intervention compared to during within the study group, though not significant. Conclusion: 10 days of practice of 40 CCB breathworks (3 rounds) for 5 min/day may reduce EEG power in delta, theta, alpha and beta waves. It may help to maintain autonomic balance with an increase of HRV in the recovery phase after an initial decrease during breathwork. Further studies are recommended to test the consistency of outcomes.

This chapter encompasses the patterns of cerebral and extracerebral origin as well as patterns of uncertain significance to illustrate the range of normal electroencephalogram (<abbrev>EEG</abbrev>) encountered in clinical practice. It reviews the parameters of recording and analyses the state of the patient and background activity. The chapter explores the most salient features of the epoch. The alpha rhythm is the starting point in the orderly approach to analyze <abbrev>EEG</abbrev>. In the normal <abbrev>EEG</abbrev>, a posterior dominant rhythm is represented bilaterally over the posterior head regions and lies within the 8-Hz to 13-Hz bandwidth. Beta rhythms are frequencies that are more than 13 Hz. Theta rhythms are composed of 4- to 7-Hz frequencies of varying amplitude and morphologies. Delta rhythms are frequencies that consist of <4-Hz activity. With new waveforms still being identified, <abbrev>EEG</abbrev> continues to remain at the forefront of neurological evaluations.

Comparative exploration on EEG signal filtering using window control methods

Alpha and theta oscillations during the cognitive reappraisal of aversive pictures: A spatio-temporal qEEG investigation

Electroencephalographic study of parasomnia: Sleep-talking, enuresis and bruxism

Inflammation-associated peripheral blood cells and serum lipid levels are associated with Parkinson's disease.

To assess spatial working memory disorders in patients with mild depressive disorders and determine their neurophysiological correlates. Thirty patients (right-handed) with ICD-10 diagnosis Mood Disorders (F31.3, F32.0, F33.0, F34.1), aged 37±8 years, were examined before treatment. A control group included 30 mentally and somatically healthy individuals (32±7 years old). The study of spatial working memory was carried out using the Corsi Block-Tapping test. EEG was recorded and the values of the spectral power of theta, alpha and beta rhythms were analyzed. A decrease in the level of working memory that was correlated with higher values of theta rhythm power in the frontal and occipital regions and alpha rhythm in the frontal cortex was observed in affective disorders with mild depressive symptoms.

Parkinson's disease, ischemic stroke, dementia, and cranial computed tomography: Preliminary study

The goal of this study was to determine whether the cortical responses elicited by whole‐body balance perturbations were similar to established cortical markers of action monitoring. Postural changes imposed by balance perturbations elicit a robust negative potential (N1) and a brisk increase of theta activity in the electroencephalogram recorded over midfrontal scalp areas. Because action monitoring is a cognitive function proposed to detect errors and initiate corrective adjustments, we hypothesized that the possible cortical markers of action monitoring during balance control (N1 potential and theta rhythm) scale with perturbation intensity and the eventual execution of reactive stepping responses (as opposed to feet‐in‐place responses). We recorded high‐density electroencephalogram from eleven young individuals, who participated in an experimental balance assessment. The participants were asked to recover balance following anteroposterior translations of the support surface at various intensities, while attempting to maintain both feet in place. We estimated source‐resolved cortical activity using independent component analysis. Combining time‐frequency decomposition and group‐level general linear modeling of single‐trial responses, we found a significant relation of the interaction between perturbation intensity and stepping responses with multiple cortical features from the midfrontal cortex, including the N1 potential, and theta, alpha, and beta rhythms. Our findings suggest that the cortical responses to balance perturbations index the magnitude of a deviation from a stable postural state to predict the need for reactive stepping responses. We propose that the cortical control of balance may involve cognitive control mechanisms (i.e., action monitoring) that facilitate postural adjustments to maintain postural stability.

In his famous monograph “An Essay in Shaking Palsy,” James Parkinson provided astute descriptions of impaired sleep in his case series of patients with Parkinson's disease (PD) two centuries ago. It is only three decades ago that sleep dysfunction started to attract attention of medical and scientific communities involved in the clinical care and research of PD. Tremendous advancements in our understanding of impaired sleep and alertness associated with PD have developed since then. Sleep problems are one of the major, challenging issues in patients with PD, affecting a significant number of patients. The etiology of sleep problems is multifactorial, including disease-related nocturnal symptoms, medication adverse effects, and primary sleep disorders including restless legs syndrome, rapid eye movement sleep behavior disorder, and sleep apnea syndrome. These causes of impaired sleep-wake cycles in the PD population often coexist or even overlap, rendering the management of sleep problems in PD patients difficult. Also, excessive daytime sleepiness is observed in a significant number of patients. In PD patients, therefore, appropriate assessment of disease-related nocturnal disturbances and primary sleep disorders is imperative. This special issue addresses unmet need for understanding PD-related sleep problems. PD sleep scale 2 (PDSS-2) is a recently developed tool for screening and managing sleep disturbance in PD patients, consisting of 15 items which are clinically relevant to nocturnal problems including nonmotor and motor problems in PD [1]. The original PDSS-2 (German and English) has been translated into several languages, including Japanese [2] and Italian [3]. The PDSS-2 has been used to observe treatment response. In a double-blind, placebo-controlled trial, including 287 PD patients, mean PDSS-2 total score decreased by −5.9 points with rotigotine and by −1.9 points with placebo [4]. In this special issue, K. Horvath et al. estimated the threshold representing minimal clinically important difference of the PDSS-2 total score: the study results showed −3.44 points for detecting improvement or the threshold of 2.07 points for observing worsening. This finding is important when planning studies using the PDSS-2 as outcome measures. Full-night polysomnography (PSG) is a gold standard for diagnosing sleep apnea syndrome; however, applying PSG is often difficult in patients with PD, who have severe parkinsonism or psychiatric comorbidity. P. Gros et al. evaluated the usefulness of unattended portable monitoring (PM) for diagnosis of obstructive sleep apnea (OSA) in patients with PD. Although discrepancy between portable monitoring and PSG was greater in PD patients with more motor dysfunction, the authors confirmed the usefulness of portable monitoring in diagnosing moderate to severe OSA in PD patients. These results provide the rationale for the use of portable sleep monitoring in PD and are very relevant for circumstances where a complete PSG in a sleep laboratory is not available and/or feasible. M. Kaminska et al. performed a review on the relationship between OSA and PD. Although the clinical significance of OSA in PD has been controversial [5, 6], the authors suggest the possibility that treatment of OSA could delay cognitive decline or motor dysfunction in patients with PD. This area of research is of high significance as it is important to assess the prevalence of OSA and the impact of its treatment in the PD population. D. Martinez-Ramirez et al. correlated PSG findings and sleep disorders with clinical characteristics in PD patients and found that sleep disorders and sleep architecture were poorly predictable by clinical characteristic of PD patients. This comprehensive study demonstrates the complexity of sleep dysfunction associated with PD and its complex associations with metrics of PD. In a fine and thoughtful study K. Suzuki et al. provided an interesting insight into the complex relationship between PD and restless legs syndrome (RLS) and leg motor restlessness (LMR). A significant relationship between RLS and PD is suggested by observing a favorable response to dopaminergic treatment in both disorders. However, RLS prevalence in PD patients varies according to different studies. Recently, LMR, characterized by an urge to move the legs that does not fulfill the diagnostic criteria for RLS, has been reported more frequently in patients untreated with PD than healthy controls. This review article may provide a new insight into the relationship between RLS, LMR, and PD. Finally, the complex and still only partially understood interaction of impulse control disorders in PD and sleep is discussed in a review from the clinic of one of the guest editors. In summary, sleep problems are common but underreported by PD patients and underdiagnosed by health professionals. Further understanding of mechanisms that underlie impaired sleep and alertness in PD will allow for development of much needed treatment approaches for this nonmotor manifestation of PD. Koichi Hirata Birgit Hogl Eng King Tan Aleksandar Videnovic

This study will discuss the characteristics of the frontal area brain electrical signals in cigarette addicts before and after smoking based on QEEG analysis. Several neuroimaging techniques are used to understand the relationships between brain functionality. Quantitative Electroencephalography (QEEG) is a non-invasive technique that can be used to provide an overview of brain functionality through several physical quantities being assessed. Recording of brain signals using Emotiv Epoc 14 electrodes and only focuses on 8 frontal area electrodes (AF3, F7, F3, FC5, FC6, F4, F8, AF4) and 2 reference channels. The number of test subjects in the study was 5 people, male with an age range between 20-25 years. Brain recording was done when the eyes were open, eyes closed, and the task was to do math problems. Data analysis methods include pre-processing of EEG data to remove noise and artifacts, calculation of spectral power using Welch's periodogram, and analysis of brain functional connectivity by calculating the amount of intra-hemisphere and inter-hemisphere coherence. The results of the analysis of the spectral power showed that before smoking an increase in the spectral power occurred at the delta and alpha wave frequencies, and a decrease occurred in the theta wave frequencies. Meanwhile, after smoking an increase occurred in the frequency of delta, alpha and beta waves and a decrease occurred in the frequency of theta waves. The coherence analysis results showed a significant difference in the coherence of the right intra-hemisphere of theta wave frequency and the inter-hemisphere coherence of beta wave frequencies before and after smoking.