High Performance Implementation of Neural Networks Learning using Swarm Optimization Algorithms for EEG Classification based on Brain Wave Data

EEG analysis aims to help scientists better understand the brain, help physicians diagnose and treatment choices of the brain-computer interface. Artificial neural networks are among the most effective learning algorithms to perform computing tasks similar to biological neurons in the human brain. In some problems, the neural network model's performance might significantly degrade and overfit due to some irrelevant features that negatively influence the model performance. Swarm optimization algorithms are robust techniques that can be implemented to find optimal solutions to such problems. In this paper, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms are applied for the feature selection and the training of a Feed-forward Neural Network (FFNN). The performance of the FFNN in terms of test accuracy, precision, recall, and F1_score is investigated. Furthermore, this research has implemented other five machine learning algorithms for the purpose of comparison. Experimental results prove that the neural network model outperforms all other algorithms via GWO.

Diffuse scattering model for human brain wave (Alpha) propagation prediction during meditation inside a temple, through ray tracing, a hypothetical study

Electromagnetic radio waves have been propagating for billions of years through the universe since the beginning of time. Electromagnetic radio wave propagation and the communication revolution it spawned, however are products of the twentieth century. Radio propagation in a particular environment is a complex, multipath phenomenon which involves several different mechanisms. According to a traditional, simplified approach, two major urban propagation mechanisms are identified over-roof-top (ORT) or vertical propagation (VP), where one major radial path undergoes multiple diffractions on building tops, and lateral propagation (LP) where several rays reflect/diffract all vertical building walls/edges according to the geometrical Optics (GO) rules before reaching the receiver.

A Case Study in Attention-Deficit/Hyperactivity Disorder: An Innovative Neurofeedback-Based Approach

In research about attention-deficit/hyperactivity disorder (ADHD) there is growing interest in evaluating cortical activation and using neurofeedback in interventions. This paper presents a case study using monopolar electroencephalogram recording (brain mapping known as MiniQ) for subsequent use in an intervention with neurofeedback for a 10-year-old girl presenting predominantly inattentive ADHD. A total of 75 training sessions were performed, and brain wave activity was assessed before and after the intervention. The results indicated post-treatment benefits in the beta wave (related to a higher level of concentration) and in the theta/beta ratio, but not in the theta wave (related to higher levels of drowsiness and distraction). These instruments may be beneficial in the evaluation and treatment of ADHD.

The Individual Ictal Fingerprint: Combining Movement Measures With Ultra Long-Term Subcutaneous EEG in People With Epilepsy

Background: Epileptic seizures are caused by abnormal brain wave hypersynchronization leading to a range of signs and symptoms. Tools for detecting seizures in everyday life typically focus on cardiac rhythm, electrodermal activity, or movement (EMG, accelerometry); however, these modalities are not very effective for non-motor seizures. Ultra long-term subcutaneous EEG-devices can detect the electrographic changes that do not depend on clinical changes. Nonetheless, this also means that it is not possible to assess whether a seizure is clinical or subclinical based on an EEG signal alone. Therefore, we combine EEG and movement-related modalities in this work. We focus on whether it is possible to define an individual “multimodal ictal fingerprint” which can be exploited in different epilepsy management purposes.Methods: This study used ultra long-term data from an outpatient monitoring trial of persons with temporal lobe epilepsy obtained with a subcutaneous EEG recording system. Subcutaneous EEG, an EMG estimate and chest-mounted accelerometry were extracted from four persons showing more than 10 well-defined electrographic seizures each. Numerous features were computed from all three modalities. Based on these, the Gini impurity measure of a Random Forest classifier was used to select the most discriminative features for the ictal fingerprint. A total of 74 electrographic seizures were analyzed.Results: The optimal individual ictal fingerprints included features extracted from all three tested modalities: an acceleration component; the power of the estimated EMG activity; and the relative power in the delta (0.5–4 Hz), low theta (4–6 Hz), and high theta (6–8 Hz) bands of the subcutaneous EEG. Multimodal ictal fingerprints were established for all persons, clustering seizures within persons, while separating seizures across persons.Conclusion: The existence of multimodal ictal fingerprints illustrates the benefits of combining multiple modalities such as EEG, EMG, and accelerometry in future epilepsy management. Multimodal ictal fingerprints could be used by doctors to get a better understanding of the individual seizure semiology of people with epilepsy. Furthermore, the multimodal ictal fingerprint gives a better understanding of how seizures manifest simultaneously in different modalities. A knowledge that could be used to improve seizure acknowledgment when reviewing EEG without video.

EXPLORE THE IMPACT OF NOISE STRESS INDUCED BRAIN WAVE PATTERN AND BEHAVIOUR ALTERATIONS IN WISTAR ALBINO RATS

Objective: The aim of the study is to investigate the impact of noise-induced stress and electroencephalogram (EEG) with behavioral alteration in male Wistar albino Rats. Methods: Adult albino rats were randomly divided into three groups. Each group contains six animals. Rats exposed to acute and sub-acute noise, stress (100 dB/4 h) were compared with control animals and assessed for learning and memory using an Eight-arm radial maze, Y-maze, T-maze and also monitoring of brain electrical activity showed by the electro encephalography. Results: The reference memory and working memory error increases, in acute and sub-acute noise stress. The amplitude and frequency also increase in frontal and occipital lobar when compared to control animals. Conclusion: Animals were exposed to noise stress showed learning and memory impairment and also changes in EEG wave pattern.

Q-EEG map of parietal and frontal lobes out of brain waves recording during dental hypnosis practice

Introduction: A patient with fear and anxiety is a common case to deal with for a dentist, therefore, dental hypnosis has been widely used to ease this situation. In a hypnotized state, the human brain may easily accept any suggestion. This is projected in the brain waves. Electroencephalograph (EEG) is a brain wave recording device, reflecting several states of consciousness. Beta for conscious, alpha and theta for subconscious, and delta for sleep. Dental hypnosis puts down beta waves to alpha or theta. Quantitative Electroencephalography (Q-EEG) or brain mapping is a comprehensive analysis of (Electroencephalography, EEG) in a colored topographic map, reflecting the brain's electrical activity. The objective of this article was reporting the parietal and frontal lobes activity during dental hypnosis based on the Q-EEG mapping. Methods: The research applied a quantitative research method using observatory study. The sample was taken with an accidental sampling method, with inclusion criteria, patients with dental anxiety and exclusion criteria was patients with special need and high level of dental anxiety. Data of the EEG records was taken in January-March 2018, and processed after in Pramita laboratorium Bandung. Results: Parietal lobe affected more during the inducement than temporal lobe. During dental hypnosis, the hypnotic markers (theta and alpha states) observed from the EEG were found to be more reactive. Conclusion: Dental hypnosis effects can be observed easily using Quantitative Electroencephalography method. Dental hypnosis affects brainwaves and brain mapping which indicate relaxations of brain waves especially on parietal lobes.

Boosting Brain Waves Improves Memory

Have you ever wanted to improve your memory? Or have you struggled to remember what you studied? Memory uses special patterns of activity in the brain. This experiment tested a new way to create brain wave patterns that help with memory. We wanted to see if we could improve memory by using lights and sounds that teach the brain waves to be in sync. People wore special goggles that made flashes of light and headphones that made beeping noises. This trained the brain through a process called entrainment. The entrainment put the brain in sync at a specific brain wave pattern called theta. People whose brains were trained to be in theta had better memory compared to people whose brains did not get trained. We learned that entrainment is a cool new way to make memory better.