Origin: Electrical activity from the heart.Effect on frequency domain: Effect in high frequencies overlapping artifacts in beta and gamma EEG bands.The amplitude correlates with the strength of the muscle contraction. Effect on time domain: We can observe a high frequency signal that overlaps the EEG signal.Types of effects: Clenching the jaw, neck and shoulder muscles tension, swallowing, chewing, talking, sucking, sniffing, grimacing, frowning or hiccupping.
We can observe these high frequency artifacts with the naked eye. Why it affects EEG: That electrical activity produced by the muscles can interfere with the actual EEG activity.This activity can be measured and the resulting signal is called electromyography (EMG). Origin: Muscles produce electrical activity when they are contracted.Effect on frequency domain: Effect in low frequencies that can be confused with delta and theta bands.In general the artifact amplitude of the artifact is almost proportional to the angle of gaze. Lateral movements of the eye affect also the frontal areas but are more significant the closer to the temples. Effect on time-domain: Blinking produces a quick change with high amplitude on the EEG signals in the electrodes of the frontal area, more pronounced in those closer to the eyes.Types of effects: Blinking, lateral movement, eye movements.Why it affects EEG: This distortion is known as the EOG (Electrooculogram) signal and has an amplitude usually one order of magnitude larger than the EEG signal, reaching values around 100-200 microvolts.Origin: The eye can be electrically modeled as a magnetic dipole and it distorts the electric field in the region when it moves.Independent components were extracted using logistic infomax ICA algorithm ( Bell & Sejnowski, 1995). The EEG device used to create most of the figures showing the artifacts was collected with the Bitbrain EEG versatile 16ch system, band pass filtered between 0.5 and 30 Hz. AC electrical and electromagnetic interferences.The most usual are ( Sörnmo & Laguna, 2005 Urigüen & Garcia-Zapirain, 2015 Clark, 1998 Ismal et al., 2016): Physiological artifacts EEG artifacts can be classified depending on their origin, which can be physiological or external to the human body (non-physiological). The ability to recognize artifacts is the first step in removing them. Physiologic artifacts originate from the patient and non-physiologic artifacts originate from the environment of the patient.” ( EEG Artifacts, Springer) Types of EEG artifacts Awareness of logical topographic field of distribution for true EEG abnormality is important in distinguishing artifact from brain waves. Some artifact may mimic true epileptiform abnormalities or seizures. “Artifacts are signals recorded by EEG but not generated by brain. Thus, an artifact occurs when there is the noise registered by the system that contamines the neural EEG data ( Urigüen & Garcia-Zapirain, 2015). We denote an “artifact” as any component of the EEG signal that is not directly produced by human brain activity (under some circumstances neural processes generated by the brain can themselves be artifacts, but we skip them from the text as they are restricted to very specific research contexts). These electroencephalographic signals (EEG) are transmitted through tissue, bone, and hair before it is recorded, and by then its amplitude is very attenuated ( Sörnmo & Laguna, 2005 Nunez & Srinivasan, 2006). A single electric signal from neuron to neuron is not recordable but when millions of neurons synchronize, the electric field generated can be measured from the scalp. Biochemistry exchanges between cells produce small electrical activity when the neurons communicate among them. We review in this post different EEG artifacts and the main tools and techniques to remove them. The EEG signal amplitude is in the microvolts range and it is easily contaminated with noise, known as “artifacts”, which need to be filtered from the neural processes to keep the valuable information we need for our applications. One of the main concerns when dealing with electroencephalographic signals (EEG) is assuring that we record clean data with a high signal to noise ratio.
0 kommentar(er)
