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Software and Hardware for EEG for Capturing and Analysis
Published in Narayan Panigrahi, Saraju P. Mohanty, Brain Computer Interface, 2022
Narayan Panigrahi, Saraju P. Mohanty
An EEG signal is saved in European Data Format (.edf), which cannot be opened directly in MATLAB. The EEGLAB (https://sccn.ucsd.edu/eeglab/index.php) toolbox provides support for reading different formats of EEG files and plots them by analyzing different useful in-built functions. EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. EEGLAB runs under Linux, Unix, Windows, and Mac OS.
Neurophysiology of the Human Scalp EEG
Published in Kaushik Majumdar, A Brief Survey of Quantitative EEG, 2017
The format of EEG data depends exclusively on the acquisition system. Different vendors of the system store data in different formats. Fortunately, software is available to convert the raw EEG data files into a format convenient for processing by electronic computers. This is indeed a great boon to the quantitative EEG community. EEGLAB is one such open source software that is freely available and regularly updated (EEGLAB, 2017). Although EEGLAB is free it works only on a MATLAB® platform, which is not free. However the compiled version of EEGLAB does not require MATLAB although scripting capabilities are more limited.
Modalities for Decoding Human Brain Activity
Published in Huansheng Ning, Liming Chen, Ata Ullah, Xiong Luo, Cyber-Enabled Intelligence, 2019
Decoding of brain activity using EEG and MEG is quite new as compared to fMRI, so extensive research work is required in this field for these two modalities. The advantage of using EEG and MEG is that unlike fMRI, they measure the brain activity directly and both have very high temporal resolution. The main limitation of EEG is its spatial resolution which is poor while MEG has good spatial resolution too on the order of millimeters. EEG is an old technique and has been used for brain studies for a long time [12]; however, in the field of decoding it is not mature and has been used in only a few recent studies [13]. Dingyi Pei et al. observed the hand kinematics using singular value decomposition and performed the neural decoding to identify the neural representation of kinematic synergies. After that these synergies are reconstructed for weighted linear combinations that are further utilized to extract the optimal weights by using linear estimations for optimal cases. In this scheme, EEG can successfully decode the synergy-based movements [14]. MEG is one of the best techniques to study brain activity since it has both good spatial and temporal resolution so it has been used often in recent studies for the application of decoding. Abdelkader et al. present a brain-Geminoid control system where two brain–computer interfaces were involved to perform four bimanual hand movements. It was set up to control a humanoid robot. A non-linear vector machine was utilized to classify the real-time hand movements along with 114 MEG sensors [15]. The current common available tools for the analysis of EEG data are EEGLAB, BESA, Net station, Brainstorm and SPM while MEG data can also be analyzed using the same tools, i.e., EEGLAB, BESA, Brainstorm, and SPM (Figure 11.2).
Simplified Prediction Method for Detecting the Emergency Braking Intention Using EEG and a CNN Trained with a 2D Matrices Tensor Arrangement
Published in International Journal of Human–Computer Interaction, 2023
Hermes J. Mora, Esteban J. Pino
This work was executed following the stages of the diagram shown in Figure 1. We use the interactive EEGLAB Matlab toolbox (Delorme & Makeig, 2004) for processing EEG signals. EEGLAB provides an interactive graphic user (GUI) to process electrophysiological data employing time-frequency and independent component analysis (ICA). The EEG data were band-stop-filtered with a second-order digital filter at 50 Hz to attenuate the power line noise. The EEGLAB toolbox implements filtering functions through Matlab’s (filtfilt) that performs zero-phase digital filtering by processing the input data in both the forward and reverse directions. Additionally, the electrodes’ data were synchronized in a stimulus-response set allowing us to establish a multivariate analysis (MVA).