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![Table 1 . The frequency band of an EEG signal [19]](profile/Sadeem-Kbah/publication/309421571/figure/tbl1/AS:668715976773637@1536445721426/The-frequency-band-of-an-EEG-signal-19_Q320.jpg)
![Figure 2. The human brain cortex and its parts [16]](profile/Sadeem-Kbah/publication/309421571/figure/fig10/AS:668715976773633@1536445721156/The-human-brain-cortex-and-its-parts-16_Q320.jpg)
![Table 2 . The specificatoins of EMOTIV EPOC [25]](profile/Sadeem-Kbah/publication/309421571/figure/tbl2/AS:668715976765448@1536445721486/The-specificatoins-of-EMOTIV-EPOC-25_Q320.jpg)
![Figure 3. This Frequency bands of EEG signal [22]](profile/Sadeem-Kbah/publication/309421571/figure/fig1/AS:421223172460544@1477438838126/This-Frequency-bands-of-EEG-signal-22_Q320.jpg)
Brain Computer Interface means the group of processes in which the generated signals of EEG in the human brain can be interpreted and transferred to control an external device. In this paper, a novel method is adopted to control the rotation of a servo motor via EEG signals extracted from the human brain cortex. These signals have to pass through a...
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... Consumergrade systems, such as those offered by Emotiv and NeuroSky, are designed for personal use and offer lower channel counts and sampling rates. The choice of EEG device depends on the specific BCI application, the user's requirements, and the available budget [39][40][41][42][43]. ...
This paper provides a comprehensive review of the current state of research on Brain Computer Interfaces (BCIs) and their potential applications. The objective of this study was to gather information from various sources, including journal articles, conference papers, and books, to analyze the advancements and limitations of BCIs. A systematic literature review was conducted using databases such as PubMed, IEEE Xplore, and Google Scholar, with specific keywords related to BCIs and their applications. The selected studies were thoroughly analyzed to identify common themes, methodologies, and key findings. The main contributions of this review include an overview of different types of BCIs, their applications in fields such as medicine, entertainment, and education, and the challenges and limitations faced by BCI technology. The findings highlight the potential of BCIs in restoring motor function, improving the quality of life for individuals with various conditions, and enhancing human-technology interaction. Furthermore, the review identifies future research directions, including signal processing improvements, exploring hybrid and multimodal approaches, conducting long-term real-world studies, addressing ethical considerations, and prioritizing user-centred design. This comprehensive analysis of existing literature provides valuable insights for researchers and practitioners in the field of BCIs and sets the stage for future advancements in this rapidly evolving domain.
... EEG signals can also be classified based on the amplitude of the signal. EEG can be recognized based on their shape as illustrated in Table 2 [15][16][17]. ...
... EEG Signal Frequencies[15][16][17] ...
With technology evolving these days, paralyzed patients have found solutions that eased their live a lot. Electrical wheelchairs are commercialized which help patients to move around effortlessly without any assistance. Robotic arms are being commercialized as well and are being connected to the human brain directly for commands. However, extremely paralyzed patients that cannot move any part of their bodies need to feel self-dependent and find something that can ease their lives. This work aims to deliver a wheelchair that can move based on the brain signals of the patient. The brain signals are recorded by an EEG sensor from EMOTIV. There will be a Brain-Computer interface to analyze the brain signals and translate them to commands between the patient’s brain and the computer. The goal of this study is to move the wheelchair freely based on the patient’s intention (i.e. forward, backward, right or left) by building an algorithm that compares a trained set of data from the patient’s brain with the live data that is recorded by the EEG sensor. The outcomes indicated the possibility of BCI-controlled systems being used in complex daily tasks and showed that the suggested BCI could offer satisfactory control accuracy for the wheelchair.
... EEG signals overlapped with other internal and external bio-signals cause artifacts that mimic the EEG seizure signal and thus give false data. Some examples include eye movement, cardiogenic movement, muscle movement, or environmental noise [7]. Table 1 illustrates the frequency bands of EEG signals with normal and abnormal tasks affecting each band. ...
Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels.
... Firstly, data for training only one-hour segments of EEG containing at least one epileptic seizure were compared with data for one-hour sections that did not contain epilepsy. Subclinical seizures of iEEG were used features (26)(27)(28). Other training parts, ...
Seizures, which last for a while and are a symptom of epilepsy, are bouts of excessive and abnormally synchronized neuronal activity in the patient's brain. For young children, in particular, early diagnosis and treatment are essential to optimize the likelihood of the best possible child- specific result. Electroencephalogram (EEG) signals can be inspected to look for epileptic seizures. However, certain epileptic patients with severe cases show high rates of misdiagnosis or failure to notice the seizures, and they do not demonstrate any improvement in healing as a result of their inability to respond to medical treatment. The purpose of this study was to identify EEG biomarkers that may be used to distinguish between children with epilepsy and otherwise healthy and normal subjects. Savitzky-Golay (SG) filter was used to record and analyze the data from 19 EEG channels. EEG background activity was used to calculate amplitude-aware permutation entropy (AAPE) and enhanced permutation entropy (impe). The hypothesis that the irregularity and complexity in epileptic EEG were decreased in comparison with healthy control participants was tested statistically using the t-test (p < 0,05). As a method of dimensionality reduction, principle component analysis (PCA) was used. The EEG signals of the patients with epileptic seizures were then separated from those of the control individuals using decision tree (DT) and random forest (RF) classifiers. The findings indicate that the EEG of the AAPE and impe was decreased for epileptic patients. A comparison study has been done to see how well the DT and RF classifiers work with the SG filter, AAPE and impe features, and PCA dimensionality reduction technique. When identifying patients with epilepsy and control subjects, PCA with DT and RF produced accuracies of 85% and 80%, respectively, but without the PCA, DT and RF showed accuracies of 75% and 72.5%, respectively. As a result, the EEG may be a trustworthy index for looking at short-term indicators that are sensitive to epileptic identification and classification.
... Once identified, the actuation will be performed by the Arduino Uno micro-controller equipped with a Wi-Fi module and relay cards [9]. Processed signals were passed into the controller to actuate the intended motor action. ...
Brain-Computer Interface (BCI) is a technology that integrates analog brain signals to digital computer-based systems for the purpose of analysis, manipulation, and control. With modern machine learning algorithms, Artificial Neural Network (ANN) was used to develop the intelligence in processing brain signals for electric motor control. In this study, supervised and unsupervised learning methods were explored to train the ANN. Guided and unguided thought-task methods were used in manipulating a 5 and an 8 switching control variations of an electric motor. A brain wave filter was designed, and the different brain bands were explored. Significant signal features were obtained and were varied in terms of qualification. Simulation run in an offline and real-time mode. Results show high control accuracies in using the Gamma band with a supervised learning method. Guided thoughts with 5 switching controls, and 4 features gave better results. Control accuracies varies between off-line and real-time implementations.
... This noise needs to be processed to extract meaningful information from the signal. The frequency range usually sought after when performing EEGgenerated MI is between 8-30 Hz when ERD/ERS is used [37]- [40]. Unwanted signals Higher frequency is typically generated by electromyography (EMG) from the muscles in the subject's body [41]. ...
... After obtaining the noise-free signals from the signal enhancement phase, essential features from the brain signals were extracted. For feature extraction from EEG signals use methods like Adaptive Auto Regressive parameters (AAR), bilinear AAR, multivariate AAR, Fast Fourier Transformations (FFT), PCA, ICA, Genetic Algorithms (GA), Wavelet Transformations (WT), Wavelet Packet Decomposition (WPD) [10] [33][34][35][36][37][38][39]. Among these ICA, PCA, WT, AR, WPD, FFT are mostly used. ...
A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally, discussed is the application of EEG-based BCI.
... In the scientific literature, there is significant variability in frequency ranges used to define traditional EEG frequency bands [21]. For the purposes of comparison with the proposed method, we use the following definitions (Hz) [20]: delta (0, 4), theta [4,7), alpha [7,12), beta [12,30), and gamma [30,100). We analyze a 4-minute segment of a 72-channel 256 Hz EEG signal from a single participant from the study described in [35]. ...
The frequency-domain properties of nonstationary functional time series often contain valuable information. These properties are characterized through its time-varying power spectrum. Practitioners seeking low-dimensional summary measures of the power spectrum often partition frequencies into bands and create collapsed measures of power within bands. However, standard frequency bands have largely been developed through manual inspection of time series data and may not adequately summarize power spectra. In this article, we propose a framework for adaptive frequency band estimation of nonstationary functional time series that optimally summarizes the time-varying dynamics of the series. We develop a scan statistic and search algorithm to detect changes in the frequency domain. We establish theoretical properties of this framework and develop a computationally-efficient implementation. The validity of our method is also justified through numerous simulation studies and an application to analyzing electroencephalogram data in participants alternating between eyes open and eyes closed conditions.
... EEG is by far the most widely used method for mensuration brain function. [19]- [23] EEG monitors voltage produced via electrical current in brain neurons. Electrodes on the scalp test EEG signals amplitude. ...
... • Beta pulse (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) is the dominant frequency when eyes are open and highly receptive. The beta model represents almost all of our everyday tasks (eat, move, speak). ...
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Abstract The futuristic age requires progress in handwork or even sub-machine dependency and Brain-Computer Interface (BCI) provides the necessary BCI procession. As the article suggests, it is a pathway between the signals created by a human brain thinking and the computer, which can translate the signal transmitted into action. BCI-processed brain activity is typically measured using EEG. Throughout this article, further intend to provide an available and up-to-date review of EEG-based BCI, concentrating on its technical aspects. In specific, we present several essential neuroscience backgrounds that describe well how to build an EEG-based BCI, including evaluating which signal processing, software, and hardware techniques to use. Individuals discuss Brain-Computer Interface programs, demonstrate some existing device shortcomings, and propose some eld's viewpoints.
... This work is licensed under a Creative Commons Attribution 4.0 International License Servo motor control is another area that is benefitting from EEG research. Being able to control servo motors with brain wave analysis can potentially open the door for many biomedical and generic motor control breakthroughs [11]. A team of researchers from the University of Baghdad were able to accurately control a servo motor to turn to a 90-degree position when specific EEG signal criteria was met, and then to turn back when the criteria was no longer met [11]. ...
... Being able to control servo motors with brain wave analysis can potentially open the door for many biomedical and generic motor control breakthroughs [11]. A team of researchers from the University of Baghdad were able to accurately control a servo motor to turn to a 90-degree position when specific EEG signal criteria was met, and then to turn back when the criteria was no longer met [11]. If this research can be built upon and multiple positions can be reached accurately, it may benefit consumers [11]. ...
... A team of researchers from the University of Baghdad were able to accurately control a servo motor to turn to a 90-degree position when specific EEG signal criteria was met, and then to turn back when the criteria was no longer met [11]. If this research can be built upon and multiple positions can be reached accurately, it may benefit consumers [11]. ...
... The phenomena of event-related synchronization (ERS) and event-related desynchronization (ERD) of elbow motoric movement occurs in these frequency bands, and the EEG power is known decreaing in the alpha and beta band from relax state to movement state while performing motor imagery or voluntary contraction, based on previous research by [12][13][14]. Support Vector Machine (SVM) is used for the signal classification. The classification uses the features extracted from EEG Delta Alpha Ratio Power and EMG rms as the input and the output is the movement classification, relax, flexion, and extension. ...
Brain-computer interface (BCI) or also its advancement, hybrid brain-computer interface (hBCI), is a technology that is vastly developed. This technology has been used in many fields. BCI is a system that directly changes a human’s mind into data that can be extracted to information that can be meaningful to people. The development of this technology has applications as a rehabilitation aid for someone suffering from an inability to move his limbs, such as the arms. Through this research, it is hoped to be able to design an orthosis control system as a rehabilitation device by using a classification method with EEG and EMG signals, so that subjects who use this tool can carry out rehabilitation in upper arm movements especially in the elbow joint. The system utilized Raspberry Pi 3 B+ as the computer and ADS1299EEG-FE as analog front end for EEG and EMG. EEG frequency band power and EMG Vrms feature are extracted using Wavelet Transform and the model used for movement classification is Support Vector Machine. The results of the movement classification using both signals, using delta alpha ratio and root mean square features, obtained training accuracy for three movements namely relax, flexion, and extension of 90.3% and for testing accuracy of 85.2%. The combination of EEG and EMG signals are considered a promising approach for developing rehabilitation device of right arm limb.
