Brainwave Measurement Research Articles

DETERMINING THE ROLE OF SPATIAL ABILITY IN PERFORMING LUNAR PHASE CHANGE TASK USING BRAIN ACTIVITY ANALYSIS

Many students have difficulty understanding the concept of lunar phase changes (LPCs) due to spatial ability problems such as perspective-taking (PT) and mental rotation (MR). Therefore, this study aimed to compare brain activity during PT and MR tasks while performing the LPC task to determine the involvement of PT and MR. This study measured brain waves using EEG in 20 participants while solving the 3 tasks. First, the power values in the theta band of all cortical areas showed a significant difference between MR and LPC tasks. Second, in the strategy execution section with a statistically significant difference, the occipital lobe and limbic system were mainly active during the PT task, whereas the frontal lobe was mainly active during the LPC task. Third, for strategy execution, during MR and LPC tasks, the frontal lobe, temporal lobe, and limbic system were all activated to significantly different degrees. Therefore, both PT and MR, particularly PT, are required to solve the LPC task. Moreover, for students who have difficulty learning LPC, it is necessary to recognize the need for spatial ability, such as PT ability, and establish an appropriate teaching strategy. Keywords: brain activation, electroencephalography, lunar phase change, mental rotation, perspective-taking

Do Audible Sounds during a Lumbar Spine Thrust Manipulation Have an Impact on Brainwave Activity?

To manage pain and stiffness of the lumbar spine, thrust manipulation is commonly used. High-velocity, small-amplitude thrust manipulation often elicits audible sounds. What causes this audible sound remains unclear, and its clinical significance has not been shown. This study aimed to identify how audible sound affects brainwave activity following a side-lying right rotatory thrust manipulation in a group of healthy individuals. This was a quasi-experimental repeated measures study design in which 44 subjects completed the study protocol. A portable Bluetooth EEG device was used to capture brainwave activity. The environment was controlled during testing to minimize any factors influencing the acquisition of real-time EEG data. After a short accommodation period, initial brainwaves were measured. Following this, each subject underwent a lumbar 4-5 side-lying right rotatory thrust manipulation, immediately followed by a second brainwave measurement. A third measurement took place one minute later, followed by a fourth one at the three-minute mark. 21 subjects did not experience audible sounds, while 23 subjects experienced audible sounds. Both groups had significant changes measured by the 14 electrodes (p < 0.05). The audible group had more significant changes, which lasted only two minutes. The lack of brainwave response differences between the audible and non-audible groups implies no direct, measurable placebo or beneficial effect from the audible sound. This study could not identify a benefit from the audible sound during an HVLA manipulation of the subjects.

Prospective study of epilepsy with generalized tonic-clonic seizures alone: Clinical features, response to treatment, and likelihood of medication withdrawal.

This prospective study aimed to delineate the demographics, natural progression, and treatment response of patients newly diagnosed with epilepsy with generalized tonic-clonic seizures alone (EGTCA). Furthermore, our objective includes assessing the seizure recurrence rate post antiseizure medication (ASM) discontinuation within this cohort, alongside exploring predictive factors for seizure relapse. The study cohort, derived from an ongoing, prospective, multicenter investigation on children and adults with new-onset unprovoked seizures, included consecutive patients enrolled between March 2010 and March 2020, and meeting mandatory ILAE criteria for EGTCA diagnosis. Participants underwent a 3-h sleep-deprived video-EEG recording along with an epilepsy protocol brain magnetic resonance imaging (MRI) with repeat EEG at each follow-up. Cumulative time-dependent probabilities of seizure recurrence were calculated using Kaplan-Meier survival analysis. Logistic regression identified variables associated with seizure recurrence following ASM taper. Eighty-nine patients with a median age of 16 years were included, constituting 31% of those diagnosed with an idiopathic generalized epilepsy. Regarding the circadian distribution of seizures, 59.6% of patients exclusively experienced diurnal seizures, 12.4% exclusively nocturnal, and 28.1% experienced both diurnal and nocturnal seizures. Generalized spike-wave discharges (GSWD) were present in the initial EEG of 88% of patients. A GTC recurred in 14% of patients treated with ASM compared with 73% of untreated patients (p < 0.00001). ASM discontinuation was attempted in 50 patients after a median treatment duration of 3 years, with 44% experiencing a recurrence. Patient-initiated taper and a mixed circadian seizure pattern independently predicted a higher likelihood of recurrence post-ASM discontinuation. Our findings underscore the importance of prompt treatment upon the diagnosis of EGTCA. Notably, lifelong treatment may not be imperative; patients seizure-free for at least 2 years, with the absence of GSWD on EEG, often maintained seizure freedom after ASM withdrawal, especially with physician-initiated tapering. Seizures in individuals diagnosed with "epilepsy with generalized tonic-clonic seizures alone" (EGTCA) typically start during adolescence and often respond well to antiseizure medications. An electroencephalogram, which measure brain waves, will show abnormal discharges in most patients with EGTCA. Lifelong treatment with antiseizure medication is not necessary for everyone with EGTCA; approximately, 40% can successfully stop treatment without facing seizure recurrence. Patients who stop medication on their own have a higher risk of seizures returning compared with those who undergo cessation under a doctor's supervision.

Effect of Adding Neurofeedback to Neuromuscular Training on Brain Waves in Athletes With Chronic Ankle Instability: A Randomized Clinical Trial

Background and Aims This study evaluated the effects of neuromuscular training compared with neurofeedback and neuromuscular training on brain waves, foot and ankle ability, and pain intensity of table tennis players with chronic ankle instability. Methods This clinical trial study was conducted on 29 table tennis players with chronic ankle instability, which were randomly divided into combined neurofeedback+neuromuscular (Mean±SD age: 21.73±2.60 years, hieght: 173.42±5.57 cm, wieght: 77.10±10.42 kg) and neuromuscular groups (Mean±SD age: 22.14±2.53 years, Mean±SD height: 169.26±6.77 cm, Mean±SD weight:77.95±15.70 kg). ProComp Infiniti System and BioGraph Infiniti version 5 software were used to measure brain waves and perform neurofeedback protocol. The foot and ankle disability questionnaire and the visual analog scale were employed to assess foot and ankle ability and pain intensity, respectively. The rehabilitation protocol was performed for eight weeks. The paired t test and 1-way analysis of covariance were used for the data analyses. Results The paired t test results revealed a significant decrease in the amplitude of theta, alpha, and sensorimotor rhythm (SMR) waves in both groups (P&lt;0.05) and an increase in the amplitude of the beta wave only in the combined group (P&lt;0.05). In addition, ankle ability was significantly increased during daily activities and exercise in both groups (P&lt;0.05). On the other hand, the results of a 1-way analysis of covariance indicated a significant difference between the two groups regarding theta and beta amplitudes and degree of ankle disability during exercise, and the improvement was greater in the combined group (P&lt;0.05). Conclusion According to the results, combined exercises (neuromuscular and neurofeedback) can be used as rehabilitation in table tennis players with chronic ankle instability to correct brain waves, improve leg and ankle ability, and reduce pain.

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection.

Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results' interpretation is performed.

Decoding of imagined speech electroencephalography neural signals using transfer learning method

The use of brain-computer interfaces to produce imagined speech from brain waves has the potential to assist individuals with difficulty producing speech or communicating silently. The decoding of covert speech has been observed to have limited efficacy due to the diverse nature of the associated measured brain waves and the limited number of covert speech databases. As a result, traditional machine learning algorithms for learning and inference are challenging, and one of the real alternatives could be to leverage transfer of learning. The main goals of this research were to create a new deep learning (DL) framework for decoding imagined speech electroencephalography (EEG) signals tasks using transfer learning and to transfer the model learning of the source task of an imagined speech EEG dataset to the model training on the target task of another imagined speech EEG dataset, essentially the cross-task learning transfer of discriminative characteristics of the source task to the target task of imagined speech. The experiment was carried out using two distinct open-access EEG datasets, FEIS and KaraOne, that recorded the imagined speech classes of neural signals from multiple individuals. The target FEIS model and the target KaraOne model for multiclass classification exhibit overall accuracy of 89.01% and 82.35%, respectively, according to the proposed transfer learning. The experiment results indicate that the cross-task deep transfer learning design reliably classifies the imagined speech EEG signals by applying the source task learning to the target task learning. The findings suggest the feasibility of a consistent strategy for classifying multiclass imagined speech with transfer learning, which could thereby open up the possibility of future investigation into cross-task imagined speech classification knowledge usability for generalization of new imagined speech prompts.

The Results of Evaluation Online Learning Using Hypnoteaching Method and Self-hypnosis

Hypnoteaching is a method that is attractive, provides comfort, and makes students feel happy when participating in the online learning process. This study aimed to determine the effect of online learning using hypnoteaching and self-hypnosis on the results of student learning evaluation. This type of research is quasi-experimental with a comparison group (post-test only with control group design) for 2 months. The research sample was all students in the Diploma III midwifery program, totaling 87 people. Data analysis was a significant difference in the mean value of learning evaluation results in the treatment group and the control group, it was concluded that the hypnoteaching and self-hypnosis methods were effective in improving student learning evaluation results. It is advisable to conduct research with learning motivation variables and measurement of brain waves.

Augmented Reality Brain–Computer Interface with Spatial Awareness

Brain–computer interfaces (BCIs) measure brain waves to assist communication and operation of equipment. However, for application of BCIs in real life settings, portability and environmental adaptability are crucial. For BCIs using visual stimuli, portability can be achieved by using augmented reality (AR) with head‐mounted displays, but prior placement of markers in the physical space is required for displaying appropriate choices. In this study, we demonstrate an environmentally adaptable AR‐BCI that uses machine learning and depth sensors. The virtual marker that the user is focusing on through the transmissive AR display is estimated from the user's electroencephalograms (EEG), and preliminary accuracy exceeds chance level. These results suggest that the use of machine learning and AR head‐mounted displays can increase the adaptability of BCIs. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Observing Brainwaves when Playing Games Using a Ruby-based Wireless Brainwave Measurement System

Observing Brainwaves when Playing Games Using a Ruby-based Wireless Brainwave Measurement System

The third AI summer: AAAI Robert S. Engelmore Memorial Lecture

The third AI summer: AAAI Robert S. Engelmore Memorial Lecture

A Fundamental Study on Control of CG Character using Brain Wave Discrimination

Virtual Reality (VR) has grown with the development of computer Graphics (CG). In recent years, VR technology is expected to develop in various fields such as medical, welfare, education, training, tourism, business, entertainment, and so on. This paper aims at intuitive operation by focusing on troublesome mounting in VR. When using VR equipment, it is required to set up many cables and sensors. In addition, when using it, there must always have a certain space around the user, because we always use the controller when operating in the world of VR, for reasons of care or for the safety of the user. Therefore, the location must be selected when the VR device is used. Furthermore, we also verified whether the VR space can be operated just by thinking in the head of a device that is economic than the device used in the existing study. As a method to solve this issue, we propose manipulation method of CG character by Electroencephalography (EEG). We use the brain—machine interface to measure brain waves, the fast Fourier transform and the support vector machine as the methods of EEG analysis in this paper. In the future, we will continue our study using different approaches of EEG analysis and new conditions by changing the method of EEG analysis and other conditions.

Attention and Emotional States during Horticultural Activities of Adults in 20s Using Electroencephalography: A Pilot Study

Since indoor, sedentary lifestyles became prevalent in society, humans have lost a sustainable connection to nature. An intervention utilizing outdoor horticultural activities could address such a challenge, but their beneficial effects on the brain and emotions have not been characterized in a quantitative approach. We aimed to investigate brain activity and emotional changes in adults in their 20s during horticultural activity to confirm feasibility of horticultural activity to improve cognitive and emotional states. Sixty university students participated in 11 outdoor horticultural activities at 2-min intervals. We measured brain waves of participants’ prefrontal cortex using a wireless electroencephalography device while performing horticultural activities. Between activities, we evaluated emotional states of participants using questionnaires. Results showed that each horticultural activity showed promotion of brain activity and emotional changes at varying degrees. The participants during physically intensive horticultural activities—digging, raking, and pruning—showed the highest attention level. For emotional states, the participants showed the highest fatigue, tension, and vigor during digging and raking. Plant-based activities—harvesting and transplanting plants—made participants feel natural and relaxed the most. Therefore, this pilot study confirmed the possibility of horticultural activity as a short-term physical intervention to improve attention levels and emotional stability in adults.

An evaluation of digitised neurofeedback - The future of personalised mental health care?

Neurofeedback is a method of brain self-control via measurement of brainwaves and provision of a feedback signal. Previous literature has identified potential for neurofeedback as alternative treatment for neuropsychiatric conditions including stress and anxiety. The SPHERE tool aims to digitise neurofeedback for mobile devices. This study evaluated whether SPHERE was more effective in improving the common symptoms of stress and anxiety than the most popular alternative digital tool available.

Efektivitas Neurofeedback Training Terhadap Emosi dan Interaksi Sosial Pada Anak Autis di Pusat Kajian Psikologi Magister Psikologi Pascasarjana Universitas Medan Area

This This study aims to test the effectiveness of Neurofeedback Training to overcome emotional problems and social interactions of children with autism. This study used a Quasi Experiment One Group Pretest - Posttest Design method. The sampling technique used Purposive Sampling and the research sample was 1 person. The research instrument is a measurement of brain wave activity called Electroencephalography. The data analysis of this research resulted in two types of data, quantitative and qualitative data. Quantitative data is the result of scores and graphs of measuring brain waves of autistic children through Neurofeedback and qualitative data is the result of assessment using DSM V, observation results and interview reports. The hypothesis proposed is that Neurofeedback Training is effective in overcoming emotional problems and social interactions in children with autism. The conclusions in this study are: 1. Neurefeedback training is effective in overcoming emotional problems of children with autism, this is shown by increasing the Alpha value of 18 Hz. 2. Neurefeedback Training is effective in overcoming the social interaction problem of children with autism, this is shown by increasing the Sensorymotor Rhytm value of 5 Hz. 3. Neurefeedback Training is effective in overcoming emotional problems and social interactions in children with autism, this is indicated by an increase in the Alpha value of 18 Hz, the Sensorymotor Rhytm value of 5 Hz and the Beta value of 13 Hz. Based on the conclusion, the proposed hypothesis is accepted. For future researchers who want to research Neurofeedback, they can use subjects in children with special needs with different disorders.

Efektivitas Neurofeedback Training Terhadap Komunikasi dan Stress Pada Anak Autis Di Pusat Kajian Psikologi Magister Psikologi Pascasarjana Universitas Medan Area

This study aims to test the effectiveness of Neurofeedback Training in overcoming communication problems and stress in children with autism. This study used a Quasi Experiment One Group Pretest-Posttesta Design method. The sampling technique was using purposive sampling and the research sample was 1 person. Methods of data collection in this study using a brain wave effectiveness measurement instrument called Electroencephalography. The data analysis in this research resulted in two types of data, namely qualitative data and quantitative data. Quantitative data is the result of scores and graphs of measuring brain waves of autistic children through Neurofeedback, while qualitative data is the result of assessment using DSM V, observation results, reports and interviews. The hypothesis proposed is that Neurofeedback Training is effective in overcoming communication problems and stress in children with autism. and the conclusions in this study are: 1. Neurofeedback training effectively improves communication in children with autism, this is shown by increasing the SMR value from 3Hz to 5Hz. 2. Neurofeedback training is effective in reducing stress in children with autism, this is addressed through the Deff ratio of the balance between the left brain and the right brain which was initially 15% to 3.5%. 3. Neurofeedback training effectively improves communication and reduces stress in children with autism, this is addressed through the Alfha results of 18Hz, 5Hz Sensorymotor Rhytim value and 13Hz Beta value, Deff The balance ratio of the left brain and right brain which was initially 15% to 3.5%. Based on this conclusion, the proposed hypothesis is accepted. And for future researchers who want to research Neurofeedback can use more subjects and on different disorders not only in children with autism.

Physiological and psychological effects of forest healing focused on plant fragrance therapy for maladjusted soldiers

Background and objective This study verified the physiological and psychological effects of plant fragrance therapy to analyze whether the therapy has a forest healing effect on maladjusted soldiers. Methods This study was conducted from March 14 to May 31, 2020 according to the Green Camp curriculum for 2 weeks per recruit class. Thus, plant fragrance therapy was implemented twice each for three recruit classes. Each session of the program was carried out for 4 hours from 1 p.m. to 5 p.m. The venue was the garden within the camp and the forest nearby. Forest healing activities using plant fragrances were led by 1 forest education specialist and 1 assistant. The effects of plant fragrance therapy for maladjusted soldiers were analzed analyzed by quantifying physiological and psychological changes through measuring brain waves and stress levels before and after the therapy. Results As a result of analyzing BRQ and SQ to determine the physiological and psychological healing effect of plant fragrance therapy, both the left and right brain showed higher BRQ and SQ after the therapy with statistical significance. This indicates that plant fragrance therapy on maladjusted soldiers helped positively stabilize their negative psychological and behavioral dispositions, and stabilization of brain waves lowered physical and mental stress and improved self-regulation and immunity. On the other hand, there was no significant difference in ATO. This is the result of analyzing the stress-coping mechanism. Troubleshooting (t = −2.702, df = 61, p = .009), emotional remission (t = −2.173, df = 61, p = .034), pursuit of help (t = −3.286), df = 61, p = .002), and wishful thinking (t = −3.714, df = 61, p &lt; .001) showed statistically significant results. Conclusion Applying plant fragrance therapy to maladjusted soldiers positively stabilized their negative psychological and behavioral dispositions and helped their self-regulation. Thus, plant fragrance therapy has psychological and physical forest healing effect on maladjusted soldiers.

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System

The present study aimed to use the proposed system to measure and analyze brain waves of users to allow intelligent upper limb rehabilitation and to optimize the system using a genetic algorithm. The study used EPOC Neuroheadset for Emotiv with EEG electrodes attached as a non-invasive method for measuring brain waves. The brain waves were measured according to the EEG 10-20 standard electrode layout, which allows measurement of signals from each spot where electrodes are attached based on EEG characteristics. The measured data were added in a database. In the intelligent neuro-fuzzy model, wave transform was used for extracting brain wave characteristics according to user intentions and to eliminate noise from the signals in an effort to increase reliability. Moreover, to construct the option rules of the neuro-fuzzy system, FCM technique and optimal cluster evaluation method were used. Furthermore, the asymmetric Gaussian membership function was used to improve performance, whereas SD and WF divided into left and right sides were used to express the chromosomes. Optimal EEG electrode locations were found, and comparative analysis was performed on the differences based on membership function, number of clusters, and number of learning generations, learning algorithm, and wavelet settings. The performance evaluation results showed that the optimal EEG electrode locations were F7, F8, FC5, and FC6, whereas the accuracy of learning and test data of user-intention recognition was found to be 94.2% and 92.3%, respectively, which suggests that the proposed system can be used to recognize user intention for specific behavior. The system proposed in the present study can allow continued rehabilitation exercise in everyday living according to user intentions, which is expected to help improve the user's willingness to participate in rehabilitation and his or her quality of life.

Effect of Brain and Pulse Waves on Safety Consciousness and Safety Commitment of Workers at Construction Sites

Even though individual mental and health status largely affects the safety in industrial sites, most studies for preventing industrial accidents are mainly focused on external factors such as regulations, education, etc. In this study, the effect of individual factors on safety (i.e., safety consciousness and safety commitment) was analyzed by collecting brainwave and pulse data at construction sites where industrial accidents have occurred with the highest percentage. The effects of brain stress, concentration, brain activity, and left and right brain imbalance on safety accidents were evaluated through brain wave measurements. In addition, the effects of cumulative fatigue, physical vitality, autonomic nerve health, and autonomic balance were identified through pulse wave measurements. Data were acquired for 180 construction workers at various construction sites, and the workers were classified into three grades according to factors that affected safety accidents at construction sites. Then, the safety consciousness and safety commitment levels of workers corresponding to each grade of the influence factors were evaluated by conducting a questionnaire on safety consciousness and safety commitment. As a result, the characteristics of brain and pulse waves required to improve safety consciousness and safety commitment ability of workers at construction sites were explored.

Brain-printing biometrics underlying mechanism as an early diagnostic technique for Alzheimer's disease neurodegenerative type.

Early diagnosis of neurodegenerative diseases, especially Alzheimer's disease (AD), is essential for implementing the appropriate treatment protocols and controlling disease progression. Early AD diagnosis helps patients achieve the best therapeutic outcomes, lessening irreversible neurodegenerative damage and severe cognitive decline. The measurement of brain waves and structural modifications, including gray/white matter and brain volume, have recently been considered a promising approach for brain biometrics because of the inherent specificity, degree of confidentiality, and reproducibility. Brain printing biometrics (BPB) is thus becoming more commonly considered as tool for early AD detection. This review proposes using BPB as a tool for the detection of AD prior to the appearance of persistent hallmark depositions, including Aβ and tau protein aggregations in different brain regions. It also describes BPB authentication, a method of implementation, as well as potential outcomes.