2019
DOI: 10.15547/ictte.2019.02.095
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Non-Invasive Bci Method: Eeg - Electroencephalography

Abstract: Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this st… Show more

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Cited by 2 publications
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“…It is seen at rest, meditation, and before falling asleep, waves as given in Figure 3(b). Their ranges overlap along the frequency spectrum by 0.5 Hz or more 17,20 .…”
Section: Figure 3(b)mentioning
confidence: 99%
See 1 more Smart Citation
“…It is seen at rest, meditation, and before falling asleep, waves as given in Figure 3(b). Their ranges overlap along the frequency spectrum by 0.5 Hz or more 17,20 .…”
Section: Figure 3(b)mentioning
confidence: 99%
“…Electrodes are attached to an EEG cap which is multi-channels (16 channels) to catch the signals of gamma, beta, alpha, theta, and delta from the various areas of the brain and different frequencies of these signals are merged with the amplifier to draw each measured potential on the screen. In Figure 3(b), the wavelength ranges of these different frequencies are given, and digital recording is made for physiological characteristics 17 .…”
Section: Introductionmentioning
confidence: 99%
“…Human EEG potentials often occur in numerous clearly pre-defined bands, such as 0.5-4 Hz (delta), 4-8 Hz (theta), 8-13 Hz (alpha), 13-30 Hz (beta), and >30 Hz (gamma), and are aperiodic, unpredictable oscillations with intermittent bursts of oscillations [6]. These oscillations' sources are billions of In Figure 2 [7], examples of these EEG cycles are displayed. Because they produce results that are simple to comprehend, contemporary machine learning methods for biomedical engineering, such as CNNs, are immediately applicable to one-dimensional biosignals.…”
mentioning
confidence: 99%
“…Figure2. Examples of EEG rhythms -from top to bottom: delta, theta, alpha, beta and gramma rhythms[7] …”
mentioning
confidence: 99%