EEG Waves Research Articles

A MATLAB-BASED GUI FOR REMOTE ELECTROOCULOGRAPHY VISUAL EXAMINATION

In this work, a MATLAB-based graphical user interface is proposed for the visual examination of several eye movements. The proposed solution is algorithm-based, which localizes the area of the eye movement, removes artifacts, and calculates the view trajectory in terms of direction and orb deviation. To compute the algorithm, a five-electrode configuration is needed. The goodness of the proposed MATLAB-based graphical user interface has been validated, at the Clinic of Child Neurology of University Hospital of Ostrava, through the EEG Wave Program, which was considered as “gold standard” test. The proposed solution can help physicians on studying cerebral diseases, or to be used for the development of human-machine interfaces useful for the improvement of the digital era that surrounds us today.

A Smart Detection Method of Sleep Quality Using EEG Signal and Long Short-Term Memory Model

Sleep is the most important physiological process related to human health. The development of society has accelerated the pace of people’s lives and has also increased people’s life pressure. As a result, more and more people suffer from reduced sleep quality, and the resulting diseases are also increasing. In response to this problem, this study proposes a sleep quality detection and management method based on electroencephalogram (EEG). The detection of sleep quality is mainly achieved by staging sleep EEG signals. First, wavelet packet decomposition (WPD) preprocesses the collected original EEG to extract the four rhythm waves of EEG. Second, the relative energy characteristics and nonlinear characteristics of each rhythm wave are extracted. The multisample entropy (MSE) values of different scales are calculated as the main features, and the rest are auxiliary features. Finally, the long short-term memory (LSTM) model is applied to classify the extracted sleep features, and the final result is obtained. Experiments were conducted in the MIT-BIH public database. The experimental results show that the method used in this article has a high accuracy rate for sleep quality detection. For the detected sleep quality data, the data are managed in combination with the mobile terminal software. Management is mainly embodied in two aspects. One is to query and display historical sleep quality data. The second is that when there are periodic abnormalities in the detected sleep quality data, the user will be reminded so that the user can respond in time to ensure physical fitness.

Artificial Neural Network analysis of EEG waves in complex partial seizure patients

Background: Brain dynamics associated with epilepsy remains limited. EEG-based epilepsy diagnosis and seizure detection is still in its infancy. The problem is further amplified for the design and development of automated algorithms, which requires a quantitative parametric representation of the qualitative or visual aspect of the markers. This study proposes an automatic classification system for epilepsy based on neural networks and EEG signals.
 Material and Method: The present study made use of EEG data from 16 controls and 16 temporal lobe epilepsy (TLE) patients in order to comparatively assess neural dynamics in normal healthy young adults and epileptic patients treated with anti-epileptic drugs in the context of resting state during eye close session. Such tangible differences could be appreciated through artificial neural network (ANN) classifiers.
 Results: During eye closed session of EEG in order to diagnose temporal lobe epileptic patient, the extracted features of EEG activity are given to the classifier algorithm for training and test performance. Artificial Neural Network (ANN) classifier was used for the diagnosis task. Fractal dimension (Katz, Higuchi and Permission entropy) were analyzed, in which the best results was observed in trained set of data of Katz (93.18%).
 
 Conclusion: Non-linear analysis plays an important role in prediction of complex partial seizure during interictal period.

A study on CNN image classification of EEG signals represented in 2D and 3D

Objective. The novelty of this study consists of the exploration of multiple new approaches of data pre-processing of brainwave signals, wherein statistical features are extracted and then formatted as visual images based on the order in which dimensionality reduction algorithms select them. This data is then treated as visual input for 2D and 3D convolutional neural networks (CNNs) which then further extract ‘features of features’. Approach. Statistical features derived from three electroencephalography (EEG) datasets are presented in visual space and processed in 2D and 3D space as pixels and voxels respectively. Three datasets are benchmarked, mental attention states and emotional valences from the four TP9, AF7, AF8 and TP10 10–20 electrodes and an eye state data from 64 electrodes. Seven hundred twenty-nine features are selected through three methods of selection in order to form 27 × 27 images and 9 × 9 × 9 cubes from the same datasets. CNNs engineered for the 2D and 3D preprocessing representations learn to convolve useful graphical features from the data. Main results. A 70/30 split method shows that the strongest methods for classification accuracy of feature selection are One Rule for attention state and Relative Entropy for emotional state both in 2D. In the eye state dataset 3D space is best, selected by Symmetrical Uncertainty. Finally, 10-fold cross validation is used to train best topologies. Final best 10-fold results are 97.03% for attention state (2D CNN), 98.4% for Emotional State (3D CNN), and 97.96% for Eye State (3D CNN). Significance. The findings of the framework presented by this work show that CNNs can successfully convolve useful features from a set of pre-computed statistical temporal features from raw EEG waves. The high performance of K-fold validated algorithms argue that the features learnt by the CNNs hold useful knowledge for classification in addition to the pre-computed features.

Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device

In the construction environment with high attention requirements, distraction is the main cause of unsafe behavior and safety performance degradation. However, few studies have focused on distraction's cognitive features and how to monitor it objectively in the construction workplace. To fill the research gap, the present study examined the correlation between distraction and brain activity using an Electroencephalography (EEG) device, intending to provide an approach for objectively monitoring worker distraction. In the simulated hazards identification activity, sustained attention to response task and dual-task paradigms have been employed to induce distraction combined with noise interference. Twenty-seven subjects participated in the experiment to identify whether a hazardous opening exists or not in the workplace in the shown images. The EEG waves were recorded and divided into two groups according to task performance: focused and distracted. Through feature calculation and extraction, it was found that beta and gamma powers in the left temporal and right pre-frontal cortex can distinguish these two statuses, particularly in channels T7 and AF4. The indicators can be considered as an objective evaluation of an individual's sustained attention and attention failures. The developed indicators located in specified brain zones can also be used as a reference for attention training. By providing safety managers with attention status about the workers in high-risk workplaces, distraction detection contributes to control and regulate work error and improper operation, which can extend to apply in other attentive jobs like drivers, pilots, surgeons, and lifeguards.

Simulation of EEG-Based Apparatus Control for Quadriplegic Patients

The use of electroencephalogram (EEG) waves in the field of Brain Computer Interfaces (BCI) has recently attracted a lot of interest, with varied applications ranging from entertainment to medicine emerging, showing that EEG waves are suitable for controlling appliances through a microcontroller. This study presents an offline strategy for the extraction of digital control signals from brain EEG waves using MATLAB/SIMULINK. The principle is applied as follows: (i) the extraction of α- and β-bands from the original brain EEG wave; (ii) detection of the peaks of both bands; and (iii) calculation of the power density of both bands to classify the mental mode and to generate appropriate control signals. Simulation results including signal processing tracking for various EEG waves are demonstrated that verify the effectiveness of the proposed strategy in terms of using brain EEG waves for apparatus control.

Advanced Convolutional Neural Network Classification for Automatic Seizure Epilepsy Detection in EEG Signal

Epilepsy is one of the irregular electro-physiological disorder appeared in human brain, which is characterized by tonic recurrent seizures, Electroencephalogram (EEG) is a sufficient test measure to maintain records with respects to electrical activity of brain and it is widely used in analysis and detection of electro epileptic seizures. Manual inspection of EEG signal extraction will take more time to process and it puts heavy complex on neurologists affects their performance. It is often difficult in identification of brain subtle but emergency changes in EEG wave forms by visual inspection based on research area for bio- engineers implement different types of methodologies for identification of such type of subtle. But all these algorithms/methodologies don’t perform efficient accuracy in classification of normal, ictal class instances. So that in this paper, we propose a novel system based on machine learning, which is single dimensional pyramidal ensemble convolutional neural network (1D-PECNN). Here ensemble means different parts of the signal are assigned to different models for efficient analysis of data. We also propose mathematical augmented approach for learning features. In 1D-PECNN model, system consist high amount of desirable and learnable parameters, in all cases proposed approach 1D-PECNN gives maximum accuracy (Approximately from 92%-99%) when compare to state-of-the methods.

Efficiency of using a neurofeedback device in determining ischaemic early electroencephalography indicators in rabbits with acute brain ischaemia.

Continuous electroencephalography (EEG) monitoring is a useful method in surgical procedures in which brain circulation is at risk. Providing this function using neurofeedback devices reduced to small dimensions may provide ease of use in the early diagnosis of brain ischaemia. The goal of this study was to demonstrate the efficiency of using a neurofeedback device in determining the early EEG indicators of ischaemia in a rabbit model of acute brain ischaemia. Three randomized groups-carotid ischaemia (CI), global ischaemia (GI) and a sham group-each comprising 8 rabbits, were created. In the CI group, the bilateral main carotid artery was clamped; in the GI group, the bilateral subclavian and main carotid arteries were clamped and brain ischaemia was created for 15 min. Brain reperfusion was then achieved for 30 min. In the sham group, the same surgical preparation was performed but no ischaemia occurred. The brain EEG wave activities of all subjects were recorded during the experiment. At the end of the procedure, all brain tissue was removed and apoptotic indexes were determined by histopathological examination. The statistical significance of the histopathological results and the EEG wave activities among the groups was examined. There was a significant difference between the sham, CI and GI average amplitude ratios, delta (1.02, 0.69, 0.16; P < 0.001) and total wave (0.99, 0.78, 0.49; P < 0.001), respectively. There was no significant difference between the sham and CI groups in delta (sham, CI, 1.01, 0.87; P = 0.1), total wave (sham, CI, 1.22, 0.98; P = 0.2) and amplitude standard deviation rates. However, there was a significant difference in the GI group (P < 0.001). There was a significant difference between all groups in apoptotic index (sham, 17.88; CI, 40.75; GI, 55.88; P < 0.001). Significant EEG wave changes resulting from experimental brain ischaemia were analysed with the use of a neurofeedback device. The results indicated that the change in the delta and the total wave standard deviations may be an additional indicator in the formation of permanent brain damage.

Identifying the Muscle Contraction Activity at Athletes Using Brain Mapping

Background: Understanding the brain function and how it coordinate the motor activity, means to have a map of brain using a lot of modern technologies which can give us information about the role and functions of different brain areas. Objective: The aim of our study is to explore the brain activity using electroencephalography (EEG) and make the evaluation of differences in brain functions depend on specific sport activity. Methods: We make the study on two lots of athletes from judo (12subjects) and volleyball (11subjects), yrs. 22. All of them have a great sport activity experience and the anthropometric characteristics are similar. For recording the brain activity we use Nihon EEG product. The protocol of the research includes recording of brain activity during muscle contraction and relax of hand flexors. The parameters that we follow up are alpha1, alpha2 and theta waves. The information have been analysed using statistic methods and Pearson coefficient. Judo players present a little bit increase values of theta waves and we observe also a correlation between alpha waves for dominant hemisphere. For the second lot the the values of theta waves are highest. Conclusions: Analyse the behaviour of EEG waves could help the trainer and staff for approach the training results in term of build the brain and motor pattern. This is the result of professional approach of training based on neurophysiologic assessment using the brain mapping.

Eugenol induces body immobilization yet evoking an increased neuronal excitability in fish during short-term baths

Several studies have suggested eugenol as a suitable anaesthetic for fish. However, it has also been regarded as a toxic and aversive substance to several aquatic organisms, including fish. This study sought to assess the eugenol-induced behavioural alterations and its seizurogenic potential to fish. Moreover, a distinctive methodology for an in vivo evaluation of the brain activity was also presented. Prior to the evaluation of eugenol-induced responses, fish were exposed to pentylenetetrazole (PTZ), to characterize any seizure-like patterns. Antagonizing responses to PTZ were assessed in fish receiving diazepam (BDZ) and subsequently exposed to PTZ. Tambaqui fish juveniles, Colossoma macropomum (15.8 ± 2.8 g) were used as models and assayed as follows: (i) fish exposed to PTZ (15 mM) and (ii) fish receiving a dose of BDZ (10 mg Kg–1) and later exposed to PTZ (15 mM) (BDZ–PTZ group). Thereafter, fish were evaluated throughout (iii) eugenol exposure at 65 μL L–1 (ethanolic solution) and recovery. Control fish and a vehicle control group (ethanol at 585 μL L–1) were also established. PTZ baths elicited body immobilization preceded by hyperactivity in a stereotyped seizure-like behaviour with increased EEG wave amplitude and frequency. PTZ effects in the brain were attenuated by a pre-administration of BDZ. Upon eugenol exposure, tambaqui had an intense neuronal excitability, showing a clonus-like seizure behaviour, also corroborated by the EEG patterns, which were consistent with a seizure-like response. Responses of eugenol-exposed fish resembled those of the PZT-exposed animals, with epileptiform discharges. EMG was in line with the EEG modulation, showing increased tracing oscillations and higher mean amplitudes in PTZ-exposed fish whereas in BDZ–PTZ group muscle contraction was less frequent and powerful. Fish exposed to eugenol showed initially some muscle activity followed by a loss of muscle tonus over time. In summary, our results showed that upon eugenol exposure, although a time-dependent body immobilization was attained, fish presented an intense neuronal excitability comparable to that evoked by PTZ. Eugenol failed to promote depression of the CNS and therefore may be not suitable to be used for general anaesthesia of C. macropomum. As eugenol could be implicated in seizurogenesis and be potentially toxic to the fish brain, protocols suggesting the broad use of eugenol for short-term anaesthesia or euthanasia of fish should be carefully revised, as it raises important concerns in terms of ethics and fish welfare.

Slow Wave Sleep and EEG Delta Spectral Power are Associated with Cognitive Function in Parkinson's Disease.

Cognitive and sleep dysfunction are common non-motor symptoms in Parkinson's disease (PD). Determine the relationship between slow wave sleep (SWS) and cognitive performance in PD. Thirty-two PD participants were evaluated with polysomnography and a comprehensive level II neurocognitive battery, as defined by the Movement Disorders Society Task Force for diagnosis of PD-mild cognitive impairment. Raw scores for each test were transformed into z-scores using normative data. Z-scores were averaged to obtain domain scores, and domain scores were averaged to determine the Composite Cognitive Score (CCS), the primary outcome. Participants were grouped by percent of SWS into High SWS and Low SWS groups and compared on CCS and other outcomes using 2-sided t-tests or Mann-Whitney U. Correlations of cognitive outcomes with sleep architecture and EEG spectral power were performed. Participants in the High SWS group demonstrated better global cognitive function (CCS) (p = 0.01, effect size: r = 0.45). In exploratory analyses, the High SWS group showed better performance in domains of executive function (effect size: Cohen's d = 1.05), language (d = 0.95), and processing speed (d = 1.12). Percentage of SWS was correlated with global cognition and executive function, language, and processing speed. Frontal EEG delta power during N3 was correlated with the CCS and executive function. Cognition was not correlated with subjective sleep quality. Increased SWS and higher delta spectral power are associated with better cognitive performance in PD. This demonstrates the significant relationship between sleep and cognitive function and suggests that interventions to improve sleep might improve cognition in individuals with PD.

A novel study on perception–cognition scenario in music using deterministic and non-deterministic approach

In the last few decades, nonlinear science and chaos theory has provided several robust non-deterministic tools by means of which the complexity of a nonlinear audio waveform can be measured precisely. On the other hand, sound signal analysis in linear deterministic approach has reached a new dimension where a number of well equipped software have been developed which can minutely measure and control the basic parameters of sound like pitch, intensity, tempo etc. The main objective of the present work is to quantitatively study the changes in acoustic signal complexity (measured using chaos based fractal technique) with individual variation in pitch, loudness and timbre of a sound signal. EEG (Electroencephalography) was also performed on 10 participants to see how the neuro-cognitive attributes of a sound change, i.e. when these basic components — pitch, loudness and timbre of the sound vary, one at a time. Single strokes of a piano were recorded where pitch and loudness of the sound signals were varied one at a time keeping the other parameters fixed. Then the sounds of 14 different musical instruments playing the same pitch at same loudness were recorded, which effectively served the purpose of timbre variation. EEG experiment was conducted with these audio signals as stimuli for the participants. The multifractal spectral widths were calculated for all the music signals as well as the corresponding EEG signals using Multifractal Detrended Fluctuation Analysis (MFDFA) and compared with each other. The results point towards the direction of a correlation between the conventional linear parameters and the latest nonlinear features in the acoustic domain, while the changes in the multifractal values of the different EEG waves reveal new information about the cognition of the basic features of sound in human brain. This study is a novel attempt to provide new data in engulfing apparent objective (acoustics) - subjective (EEG) connection, which is highly needed for building any model for perception–cognition connectivity.

Cortical generators of normal electroencephalographic waveforms: A literature review

Introduction: EEG is a useful, noninvasive, safe technique to study the electrical activity of brain in various physiological and pathological conditions like alertness, sleep, seizure activity. Normal brain rhythms, the electroencephalogram (EEG) represents the electrical activity of brain as recorded through the metal electrodes applied over scalp. An insight to the possible source of EEG, various rhythms and its activation during certain maneuvers can give a better understanding to its applications. Materials and Methods: Number of electronic database, as well as library resources were searched for related references included in published literature till 2017, using following key words ‘cortical’, ‘electroenchephalogram’,’ EEG’,’ EEG Rhythm’,’ EEG wave’, ‘alpha wave’,’ beta wave’,’ theta wave’,’ delta wave’. Those which were related to our subject were included in the review. Results: Of the total number of studies we have finally included 35 studies from search results. These are mentioned in the references. Conclusion: Beta, Alpha, Theta & Delta are produced from various regions of the brain, including cortical & thalamic networks, hippocampus, reticular networks and so on. The rhythm of EEG can be correlated with the level of activation of the brain, with beta wave being produced during alert state and delta wave being produced during deep sleep. Interventions like hyperventilation and photic stimulation can be used to increase the diagnostic yield of EEG. It has been seen that sex-related differences do exist in the degree of lateralization of hemispheric function. Keywords: Electroencephalogram, Brain waves, Cortex, Thalamus, EEG.

Impact of preksha meditation on alpha waves in EEG

Aims: The present study aims at demonstrating changes in alpha wave frequency on electroencephalograph (EEG) at two and four months of preksha meditation training in young novice students. Materials and Methods: Participants included 52 girls between the 17 to 21 years of age from a local college selected using a convenience sampling technique. They were randomly divided into experimental (n = 29) and control (n = 26) groups. Study duration was 4 months during which experimental group was administered a supervised PM training for 4 months. During this period control group continued their normal activities without mediation practice. EEG was analyzed for alpha waves and compared at base line, at 2 months and at 4 months. Results: The mean value of alpha wave frequency (%) in control group at beginning was 49.65 ±21.598 which increased to 52.58±18.005 at 2 months. In experimental group, in beginning it was 49.52 ±18.169 which increased to 54.31±22.490 at 2 months. However, this increment was not statistically significant in both groups. A statistically significant increment in alpha waves percentage to 66.59±9.493 was observed in experimental group after four months of PM which was significant at the level of p Conclusions: A significant increase in alpha waves was observed at 4 months of PM practice in young novice students. This increase was observed at 4 months and not at 2 months suggesting that longer practice of meditation is needed to bring out significant improvement. Keywords: Preksha meditation, EEG, Alpha waves.

Eyes Closing and Drowsiness in Human Subjects Decrease Baseline Galvanic Skin Response and Active Palmar Sweating: Relationship Between Galvanic Skin and Palmar Perspiration Responses.

We previously constructed a perspiration ratemeter for the measurement of palmar sweating in human subjects. Although galvanic skin response (GSR) has been used to evaluate emotional responses in human subjects, little is known about the relationships between the phasic and baseline components in GSR and active palmar sweating. From the aforementioned, we aimed to investigate the relationships in human subjects with handgrip exercise and eyes closing or opening. Fifteen healthy volunteers (mean age: 26.9 ± 8.7 years) participated in the present experiments. We investigated the effects of maximal handgrip exercise, eyes closing or opening, and self-awareness of drowsy on the GSR, active palmar sweating, R-R interval in electrocardiograph (ECG), and percentage of α wave in EEG. The faster phasic component in GSR completely agreed with the starting point of active palmar sweating. Handgrip exercise induced significantly faster spike in GSR, active palmar sweating, and decrease in R-R interval in ECG. Eyes closing produced significant decreases in baseline GSR and active palmar sweating in all human subjects. The percentage of α wave in electroencephalograph (EEG) also increased. In contrast, eyes opening increased significantly the baseline GSR and active palmar sweating. In the equivalent electrical model of human skin, the eyes closing–mediated time-dependent decrease in the baseline GSR completely agreed with the hypothesis that the palmar skin voltage only in the model decreased time dependently to 0.4 of the control during 6 min. The self-awareness of drowsy in mid-night working with computer produced similar decreases in baseline GSR and active palmar sweating to the responses with eyes closing in all human subjects. In conclusion, the faster spike in GSR completely agreed with the starting point of active palmar sweating. Eyes closing and opening or self-awareness of drowsy significantly produced changes in baseline GSR and active palmar sweating, which may become useful tools for evaluating clearness or drowsiness in human subjects.

Changes in the Electrical Activity of the Brain in the Alpha and Theta Bands during Prayer and Meditation.

Focused attention meditation (FAM) is a category of meditation based on an EEG pattern, which helps the wandering mind to focus on a particular object. It seems that prayer may, in certain respects, be similar to FAM. It is believed that emotional experience correlates mainly with theta, but also with selective alpha, with internalized attention correlating mainly with the synchronous activity of theta and alpha. The vast majority of studies indicate a possible impact of transcendence in meditation on the alpha wave in EEG. No such reports are available for prayer. Seventeen women and nineteen men aged 27–64 years with at least five years of intensive meditation/prayer experience were recruited to participate in the study. We identified the two largest groups which remained in the meditation trend originating from the Buddhist system (14 people) (Buddhist meditators) and in the Christian-based faith (15 people) (Christian meditators). EEG signal was recorded with open eyes, closed eyes, during meditation/prayer, and relaxation. After the EEG recording, an examination was conducted using the Scale of Spiritual Transcendence. Buddhist meditators exhibited a statistically significantly higher theta amplitude at Cz during meditation compared to relaxation. Meanwhile, spiritual openness favored a higher theta amplitude at Pz during relaxation. Our study did not reveal statistically significant differences in frontal areas with regard to alpha and theta, which was often indicated in previous studies. It seems necessary to analyze more closely the midline activity in terms of dispersed neural activity integration.

Effects of Virtual Reality and Non-Virtual Reality Exercises on the Exercise Capacity and Concentration of Users in a Ski Exergame: Comparative Study.

BackgroundRecently, ski exergames have been gaining popularity due to the growing interest in health improvement. Conventional studies evaluating the effects of ski exergames only considered exercise capacity and overlooked concentration. Ski exergames consist of a motion platform for exercise and virtual reality (VR) content in the game. The VR content enhances the exercise capacity and concentration of the user by providing a challenging goal.ObjectiveThe aim of this study is to evaluate the effects of VR and non-VR exercises on the exercise capacity and concentration of users in a ski exergame.MethodsTo examine the effects of the VR content in ski exergames, we performed 2 experiments, non-VR exercise and VR exercise, where participants exercised on the motion platform. If a user performs an exercise without using any VR content, it is a non-VR exercise. Contrastingly, in the case of VR exercise, a user exercises according to the VR content (a downhill scenario). In addition to the range of motion (ROM) of the ankle and rated perceived exertion (RPE) to assess exercise capacity, we used electroencephalography (EEG) to assess users’ concentration.ResultsWe evaluated the effects of the VR content by comparing the results obtained from VR and non-VR exercises. The ROM of the ankle with VR exercise was wider than that with non-VR exercise. Specifically, ROM of the ankle was 115.71° (SD 17.71°) and 78.50° (SD 20.43°) in VR exercise and non-VR exercise, respectively. The RPE difference between the 2 exercises was not statistically significant. The result of the sensorimotor rhythm waves (which are concentration-related EEG signals) was more favorable for VR exercise than non-VR exercise. The ratios of sensorimotor rhythm wave in EEG were 3.08% and 2.70% in the VR exercise and non-VR exercise, respectively.ConclusionsAccording to the results of this experiment, higher exercise capability and concentration were achieved with the VR exercise compared with non-VR exercise. The observations confirm that VR content can enhance both exercise capability and concentration of the user. Thus, the ski exergames can be used effectively by those who, in general, do not like exercise but enjoy games.

Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with a shared control scheme. In general, subjects wearing a single-channel EEG electrode located at F8 (10–20 international standards) were required to perform mental tasks by mentally visualising the rotation of a star and mind relaxation at a specific time and by robot orientation. The visualisation of a rotating star suggests that the mobile robot is currently oriented towards a target, thus enabling target selection. We showed that proposed classifier obtained the best accuracy of 92.09% in classifying the subject’s performing mental rotation task or mental relaxation when compared with conventional classification methods such as support vector machine—75.69%, Kth-nearest neighbour—65.50% and linear discriminant analysis—65.20%. Furthermore, different from conventional methods, the use of 1D-CNN enables end-to-end learning, that is the automatic decoding of EEG signals without requiring feature selection or extraction. To validate that the proposed classifier performs better than conventional methods, the extracted kernel weights of proposed 1D-CNN filters were visualised as a temporal plot, and spectral analysis was performed on the extracted weights. The obtained results confirmed that the proposed 1D-CNN was able to generate filters that resemble the EEG wave patterns of different frequencies and spectral analysis confirmed that the filters exploited information from multiple frequency bands (such as alpha band and beta band) that are often associated with a heightened mental state when performing mental tasks.

Frontal electroencephalogram activity during emergence from general anaesthesia in children with and without emergence delirium

BackgroundEmergence delirium (ED) in children after general anaesthesia causes significant distress in patients, their family members, and clinicians; however, electroencephalogram (EEG) markers predicting ED have not been fully investigated. MethodsThis prospective, single-centre observational study enrolled children aged 2–10 yr old under sevoflurane anaesthesia. ED was assessed according to Diagnostic and Statistical Manual of Mental Disorders (DSM) IV or 5 criteria. The relative power of low-frequency (delta and theta) and high-frequency (alpha and beta) EEG waves during the emergence period was compared between the children with and without ED. The linear relationships between the relative power and peak Paediatric Assessment of Emergence Delirium (PAED) score were investigated. ResultsAmong the 60 patients, 22 developed ED (ED group), whereas the other 38 did not (non-ED group). The relative power of the delta wave was higher (mean [standard deviation], 0.579 [0.083] vs 0.453 [0.090], respectively, P<0.001) in the ED group, whereas that of the alpha and beta waves was lower in the ED group, than in the non-ED group (0.155 [0.063] vs 0.218 [0.088], P=0.005 and 0.114 [0.069] vs 0.186 [0.070], P<0.001, respectively). The areas under the receiver operating characteristic curves of the relative power of the delta wave, low-to-high frequency power ratio, and delta-to-alpha ratio were 0.837 (95% confidence interval, 0.737–0.938), 0.835 (0.735–0.934), and 0.768 (0.649–0.887), respectively. The relative power of the delta wave and the two ratios had a positive linear relationship with the peak PAED scores. ConclusionsPaediatric patients developing ED have increased low-frequency (delta) frontal EEG activity with reduced high-frequency (alpha and beta) activity during emergence from general anaesthesia. Clinical trial registrationNCT03797274.

FREQUENCY AND POWER ANALYSIS OF EEG THETA ACTIVITY DURING DIFFERENT MATHEMATICAL AND VERBAL TASK IN MALES

Abstract: The brain forms EEG waves due to mental processes. The aim of this study is to investigate the effectiveness of the EEG theta wave formed in the brain during different mathematical and verbal tasks. Twenty healthy medical students without any health problems were included in the study. The five questions were answered by the subjects via power point on the computer screen. Bipolar electrode positioning was performed. In this study, domain measurements, FFT and power spectral density were analyzed. As a result of the study, no statistically significant difference was found between EEG theta waves obtained as a result of both numerical and verbal correct and incorrect answers (p&gt;0.05). An increase in mean amplitude was found in both the mathematical and verbal processes to respond incorrectly. As a result, no significant change was observed in the domain measurements, frequency and power analysis of EEG theta waves during numerical and verbal correct and incorrect responses.