Notice that the design of these filters attenuates low and high frequencies. On the one hand, there are amplifiers designed with internal filters: 1) a low pass filter to agree with the Nyquist theorem and 2) a high pass filter to eliminate EEG offsets and DC components to avoid the saturation of internal electronics (those amplifiers without high pass filters are called DC coupled). The bandwidth is the effective frequency band that the EEG system can measure according to the sample rate (see above) and the internal filters of the amplifier. In real-time neuroscience (neurotechnology and biomedical applications) or mobile applications, 256 Hz is usually the standard as the data need to be transferred and processed in near real-time.
For more challenging scenarios, 512 Hz is typical, but this can be increased up to 1024 Hz, which is considered a very high frequency for EEG data. For neuroimaging research, the minimal acceptable sample rate is 256 Hz. Generally, sample rate selection works as follows. Higher sampling frequencies will give higher resolution in the EEG bandwidth (0 to 80 Hz) but no more information. Although 160 Hz is the minimum sample rate that captures the 0 (DC) to 80 Hz range (2 samples per 1 signal period), standard amplifiers usually acquire at least at a frequency of 256 Hz. The sampling rate has to be at least twice the maximum frequency of the signal being measured (due to the Nyquist sampling theorem, see Jones, 2014). Note that there are also brain potentials and applications that make use of lower frequency ranges (DC – 0.5 Hz), denoted as slow cortical potentials ( Garipelli et. The EEG signals carry information within a bandwidth between 0.5 Hz and 80 Hz, and this bandwidth is referred to as the EEG bands in the power spectrum: delta (0.5 – 4 Hz), theta (4 – 8 Hz), alpha (8 – 12 Hz), beta (16 – 24 Hz) and gamma (up to 80 Hz), see Weiergräber et al., 2016.
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