International Journal of Innovative Research in Science, Engineering and Technology

Eeg News

Citations are important for a journal to get impact factor. Impact factor is a measure reflecting the average number of citations to recent articles published in the journal. The impact of the journal is influenced by impact factor, the journals with high impact factor are considered more important than those with lower ones. Impact factor plays a major role for the particular journal. Journal with higher impact factor is considered to be more important than other ones. Impact factor can be calculated as average number of citation divided by recent cited articles published in 2 years. EEG analysis is exploiting mathematical signal analysis methods and computer technology to extract information from electroencephalography (EEG) signals. The targets of EEG analysis are to help researchers gain a better understanding of the brain; assist physicians in diagnosis and treatment choices; and to boost brain-computer interface (BCI) technology. There are many ways to roughly categorize EEG analysis methods. If a mathematical model is exploited to fit the sampled EEG signals, the method can be categorized as parametric, otherwise, it is a non-parametric method. Traditionally, most EEG analysis methods fall into four categories: time domain, frequency domain, time-frequency domain, and nonlinear methods.[2] There are also later methods including deep neural networks (DNNs). Frequency domain analysis, also known as spectral analysis, is the most conventional yet one of the most powerful and standard methods for EEG analysis. It gives insight to information contained in the frequency domain of EEG waveforms by adopting statistical and Fourier Transform methods. Among all the spectral methods, power spectral analysis is the most commonly used, since the power spectrum reflects the 'frequency content' of the signal or the distribution of signal power over frequency. There are two important methods for time domain EEG analysis: Linear Prediction and Component Analysis. Generally, Linear Prediction gives the estimated value equal to a linear combination of the past output value with the present and past input value. And Component Analysis is an unsupervised method in which the data set is mapped to a feature set.[5] Notably, the parameters in time domain methods are entirely based on time, but they can also be extracted from statistical moments of the power spectrum. As a result, time domain method builds a bridge between physical time interpretation and conventional spectral analysis. EEG recordings during right and left motor imagery allow one to establish a new com-munication channel.[24] Based on real-time EEG analysis with subject-specific spatial patterns, a brain–computer interface (BCI) can be used to develop a simple binary response for the control of a device. Such an EEG-based BCI can help, e.g., patients with amyotrophic lateral sclerosis, with some daily activities. Brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings including MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology.[25] The objective of Brainstorm is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. Brainstorm offers rich and intuitive graphic interface for physicians and researchers, which does not require any programming knowledge. Some other relative open source analysis softwares include FieldTrip, etc.

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