Neural NoiseElectroencephalography (EEG) is the measurement of electrical activity generated by the brain using electrodes placed on the scalp. Since Hans Berger’s first human EEG recording in 1924, the technique has become one of the most widely used non-invasive neuroimaging methods in both clinical and research settings. EEG offers millisecond temporal resolution, making it particularly suited to tracking rapid neural dynamics such as oscillations, event-related potentials, and transient cognitive states.
In the context of bci-and-neural-decoding, EEG is the dominant non-invasive recording modality. motor-imagery, P300 evoked potentials, and steady-state visually evoked potentials (SSVEPs) are the three main EEG signal paradigms used for BCI control. While EEG suffers from lower spatial resolution and signal-to-noise ratio compared to invasive recordings, its accessibility, portability, and safety profile make it the practical choice for a wide range of applications from neurofeedback to clinical monitoring.
EEG signal processing involves artifact rejection, spectral analysis, and feature extraction techniques that continue to evolve with advances in machine learning. The development of dry electrodes, high-density arrays, and wireless recording systems is expanding the use of EEG beyond the laboratory into consumer and field applications.