Quantitative Analysis of EEG Neurofeedback using optimized 1-DPSO
Abstract and Figures
Brain-Computer Interface (BCI) systems are the leading technology in the world related to Neurosciences. Human intelligence and imagination have no bounds and this has led to vast advancement in BCI systems related to Neurological and allied sciences. This system measures the activity of the Central Nervous System (CNS) using biosignals and output is called Electroencephalography (EEG). The main purpose of BCI is to acquire EEG brain signals, identify patterns, extract features, and produce resultant actions. This process communicates with a modest electronic system designed for movements of physically challenged or paralyzed people. The purpose of this BCI system is to design a model to check the attention level of body movement. The movements are based on the EEG signals captured from the 19-electrode EEG headset. This allows gaining control over optimized real-time feature selection for EEG signals. The dataset of 30 subjects’ sample EEG signals is recorded for classification and analysis purpose. The EEG signals are classified using Logistic Regression, Decision Tree, and Random Forest algorithms. It is shown that Random Forest is the most efficient classifier with the highest accuracy of 99.47%.
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The accurate categorization of mental health disorders using electroencephalogram (EEG) signals poses significant challenges in the field of biomedical engineering. In this paper,an optimized multivariate Bayesian classification method for EEG signals is proposed. This research paper employs a combination of multilevel wavelet decomposition and k-nearest neighbor classification and incorporates a probabilistic Gaussian model. A multivariate Bayesian model is used to identify the energy distribution across brain waveforms. This model notably leverages non-linear regression techniques with advanced multivariate feature selection methods. Furthermore, our methodology encompasses both normal and aberrant brain activity signals, demonstrating superior performance compared to a state-of-the-art method. Our approach achieves commendable scores in terms of accuracy (0.80 ± 0.05), recall (0.77 ± 0.05), precision (0.93 ± 0.01), and F1 score (0.93 ± 0.16), signifying its effectiveness in mental health disorder categorization.
The cognitive Science is the leading technology which works on the principle of Neuroscience. Human Computer Interface is a challenging approach in neurosciences, which is the leading method to handle the brain activities to control external communications with the electronic devices for physically challenged human beings. The various HCI applications are developed with this advance technology. This helps in various patients which are physically challenged or facing the lock in syndrome, a condition where limbs are not functioning to full extent. Therefore, this paper is the review paper to the various EEG signal classification techniques using different taxonomy with techniques like linear, nonlinear, stable-ubstable, static-discriminant to design various HCI applications.
A brain-computer interface (BCI), provide a method of communication between brain activity and computer systems or external electronic devices. BCI is also referred to brain machine interface (BMI) and other scientists called it mind machine interface (MMI). The main goal of BCI is helping disable patients who are paralyzed due to neuromuscular disorders such as spinal cord injury, brain stem stroke or amyotrophic lateral sclerosis (ALS) to live as normal persons and interact with the external environment. Thanks to the well understanding of different functions and characteristics of brain structure, a new field of research, which is BCI has emerged and evolved in a rapid way. BCI system goes through different stages including preprocessing, feature extraction, signal classification and control. In this review we discussed, a scoping overview for the development of BCI science, recent technologies and tools from software and hardware, challenges concerning efficiency and the ethics of relationship between the human being and machine. Finally we discussed applications and future directions of BCI.
Brain-computer interface (BCI) is one of the technologies growing at an exponential rate with its applications extended to medical and non-medical fields. EEG is widely used in BCI for the detection and analysis of abnormalities of the brain. EEG is characterized by inherently high temporal resolution and precision, low spatial resolution, and specificity plus contains artifacts and redundant or noise information both from the subject and equipment interferences. Thus, feature extraction is a critical issue in translation algorithm development for BCI. Above all, BCI still faces a lot of challenges that result in performance variation across and even within subjects. Thus, this work provides a concise but all-encompassing review of methods that have been adopted in recent times for the development of an EEG classification in BCI.
Background . Nonlinear heart rate variability (HRV) indices have extended the description of autonomic nervous system (ANS) regulation of the heart. One of those indices is approximate entropy, ApEn , which has become a commonly used measure of the irregularity of a time series. To calculate ApEn , a priori definition of parameters like the threshold on similarity and the embedding dimension is required, which has been shown to be critical for interpretation of the results. Thus, searching for a parameter-free ApEn -based index could be advantageous for standardizing the use and interpretation of this widely applied entropy measurement. Methods . A novel entropy index called multidimensional approximate entropy, MApEnmax , is proposed based on summing the contribution of maximum approximate entropies over a wide range of embedding dimensions while selecting the similarity threshold leading to maximum ApEn value in each dimension. Synthetic RR interval time series with varying levels of stochasticity, generated by both MIX( P ) processes and white/pink noise, were used to validate the properties of the proposed index. Aging and congestive heart failure (CHF) were characterized from RR interval time series of available databases. Results . In synthetic time series, MApEnmax values were proportional to the level of randomness; i.e., MApEnmax increased for higher values of P in generated MIX( P ) processes and was larger for white than for pink noise. This result was a consequence of all maximum approximate entropy values being increased for higher levels of randomness in all considered embedding dimensions. This is in contrast to the results obtained for approximate entropies computed with a fixed similarity threshold, which presented inconsistent results for different embedding dimensions. Evaluation of the proposed index on available databases revealed that aging was associated with a notable reduction in MApEnmax values. On the other hand, MApEnmax evaluated during the night period was considerably larger in CHF patients than in healthy subjects. Conclusion. A novel parameter-free multidimensional approximate entropy index, MApEnmax , is proposed and tested over synthetic data to confirm its capacity to represent a range of randomness levels in HRV time series. MApEnmax values are reduced in elderly patients, which may correspond to the reported loss of ANS adaptability in this population segment. Increased MApEnmax values measured in CHF patients versus healthy subjects during the night period point to greater irregularity of heart rate dynamics caused by the disease.
Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG sysyems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. The research on wireless EEG has become an increasingly popular topic. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems.
