In other words, the cleaner the data, the more representative the analysis. Statistical analysis becomes more powerful the less noise your signal has. Examples of artifacts in EEG data Figure 1: Visual patterns of different artifacts.Ĥ Common reference electrode artifact caused by unstable contact between the reference electrode and skin. These movements can happen at the intersection of electrodes, or when moving electrode cables. Whenever a physical part of the measurement setup is moved, it can cause visible artifacts in the data. Any electronic equipment in the vicinity of the sensors.Some examples of such external noise sources are:
Irrelevant underlying brain activity not pertaining to the experimentĪnything that uses electricity will emit an electromagnetic field that may be detected by your measuring equipment.Ocular signal caused by eyeball movement (Electrooculogram, EOG).Artifacts caused by muscle contraction (Electromyogram, EMG).Cardiac signal (Electrocardiogram, ECG or EKG).Physiological factors are known to introduce noise into EEG recordings. There are different sources of noise and artifacts in EEG data. Sources of noise: Where does signal noise come from?įor EEG research, external noise and artifacts are signals that do not come from the brain, but which the sensors detect. That is, reduce noise, and increase the power of their analysis, without relying on enormous sample sizes. Most researchers aim to maximize the Signal-to-Noise Ratio (SNR). Noise is anything a sensor detects, which the researcher did not intend it to detect. But what exactly is noise and how can we remove it? If you are working with bio-data, you have probably come across noise.
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