Type of study
The approach adopted in this research is experimental in nature, focused on the simulation and analysis of EEG signals through the use of cutting-edge technology and open access data. This type of study is essential in the initial phases of development of biomedical technologies, where validation of concepts and optimization of parameters is required before proceeding to clinical trials.
Specifically, our experimental simulations are designed to emulate the behavior of EEG signals as they would be captured by a developing neural capture device. These simulations allow us to test a variety of scenarios and variables in a controlled and risk-free environment, facilitating the understanding of the dynamics and the identification of potential technical challenges in signal processing.
The experimental nature of this study is also reflected in the iterative design methodology, allowing successive refinements based on the simulation results. This methodological approach ensures that each iteration is based on solid evidence and contributes to the body of knowledge for practical application and implementation in real-life settings.
Description of Materials
For the analysis of simulated EEG signals, the BESA simulator has been used.
It is an advanced tool for the analysis of neurophysiological data, providing a robust environment for the simulation of brain signals. In addition, use has been made of PhysioNet, a comprehensive platform that offers a vast database of physiological signals and health-related records, thus allowing access to a wide spectrum of data for algorithm testing and validation (Inoue et al., 2019).
Experimental Procedures
Given the preliminary nature of this project, the experimental procedures have been based exclusively on simulations. Using the simulation software in Proteus.
Experimental environments have been recreated to obtain EEG signals, which allows the manipulation and study of the signals in a controlled and replicable environment. This approach has allowed experimentation with various configurations and parameters without the need for test subjects, ensuring a safe and ethical research and development stage.
Study design
This study is experimental, with the objective of developing an effective and reliable EEG signal capture device. Although a physical prototype has not yet been built, simulations have provided preliminary data that will inform future design. An iterative approach has been prioritized, with the results of each simulation informing improvements and adjustments to the device design.
Analysis of data
For the analysis of the simulated data, a combination of tools has been used. The Proteus software has been used for the simulation and visualization of the EEG signals, while the BESA simulator has allowed a more detailed evaluation of the signals in terms of their quality and applicability. The data collected has been analyzed using Microsoft Excel, taking advantage of its calculation and visualization capabilities to interpret the simulations and to prepare the data for more in-depth presentations and discussions (Michael N. Mitchell, 2020).
Materials and instruments
In this study, simulated data collection of EEG signals was performed using a set of high-precision materials and instruments designed for neurophysiological research. The main components used were:
Dry EEG Comb Electrodes
For direct and non-invasive capture of EEG signals, dry EEG comb electrodes integrated into an Ultracortex Mark IV headset were used. This device is part of the OpenBCI headband kit, which allows a flexible and adaptable user interface for different head sizes and shapes, ensuring optimal contact without the need for gels or conductive solutions.
Procedure
This study was structured in meticulously designed phases to guarantee precision in the acquisition and analysis of EEG signals and their consequent reproduction through digital simulations.
Phase 1: Bibliographic Review and Preliminary Data Acquisition It began with an exhaustive bibliographic review to identify optimal practices in the acquisition of EEG signals. A data collection session was performed using a prototype circuit connected to electrodes placed in the frontal lobe of the scalp. The visualization and monitoring of the signal was carried out using Arduino software, supported by a code specifically developed for the capture of EEG signals.
Phase 2:Data Manipulation and Filtering Once the data was collected, digital filtering techniques were applied. Multiple data samples were extracted from FisioBank, selecting specific sampling periods. The data downloaded in CSV format were processed to separate and vectorize the signals of interest (Shen et al., 2023)..
Phase 3:Simulation and Signal Analysis Signal analysis was simulated using Proteus 8 Professional (version 8.16) and BESA Simulator (version 7.1.2.1), extensively exploring the functionalities to refine the investigation. These simulators served to evaluate the replicability of the signal and confirm the fidelity of the simulated data with respect to the real data.
Phase 4: Communication and Data Transfer Protocol An Arduino board was used as a communication bridge between the electrodes and the processing system. The board facilitated the transmission of signals to the computer for later analysis.
Phase 5: Data Processing and Preparation for Visualization With the FisioBank data, a cleaning and organization process was carried out. The data was segmented and organized into columns, allowing vectorization and storage in text format for advanced analysis (Stytsenko et al., 2022).
Phase 6: Experimental Setup and Simulations in Proteus In Proteus, a simulation environment was set up using virtual components to inject the vectorized signals and observe their behavior. A 'frequency interactive' component was used to accurately simulate the reference signal and additional signals.
Elements used in assembly
TL084 Operational Amplifier
Within the physical assembly of the circuits used in the capture of electroencephalographic signals, the TL084CN operational amplifier plays a crucial role. This device is an integral component in bioelectrical signal processing, due to its high input impedance and low bias current, which is essential to minimize any interference and maintain the integrity of weak signals from the brain.
According to Mexbit. (2023). TL084It is a low-noise quad operational amplifier with JFET at the inputs, giving it desirable characteristics for biomedical applications. Its internal structure contains four independent amplifiers, each capable of amplifying a specific signal coming from the EEG electrodes. Thanks to its wide frequency band and ability to drive multiple inputs simultaneously, the TL084 is exceptionally suitable for applications requiring the amplification of multiple EEG signals.
In the context of studying electroencephalographic signals, each channel of the TL084 can be assigned to a specific electrode, allowing multiple data to be processed in parallel. This provides a more holistic view of brain electrical activity, facilitating differential analysis between different areas of the brain. The pin configuration of the TL084CN allows for easy and efficient integration within data acquisition circuits, where the input pins receive the raw signals from the scalp and, after amplification, the signals are sent through the output pins. exit to the next level of processing or registration.
This operational amplifier is essential for the signal conditioning stage in EEG research, where signal precision and clarity are paramount. In addition to amplifying signals, the TL084CN helps filter noise and improve signal-to-noise ratio, which is critical when working with EEG signals, which are notoriously subtle and susceptible to environmental noise and electrical interference.
In summary, the TL084CN is a vital component in the signal uptake and processing chain in neuroscience studies and plays a significant role in improving the quality of EEG signals for advanced research in this field.
Assembly carried out to take the signal obtained from the brain, based on the simulation of the Proteus software